diff --git a/.github/workflows/publish.yaml b/.github/workflows/publish.yaml index ec47921..59465b9 100644 --- a/.github/workflows/publish.yaml +++ b/.github/workflows/publish.yaml @@ -14,18 +14,18 @@ jobs: GITHUB_CONTEXT: ${{ toJson(github) }} run: echo "$GITHUB_CONTEXT" - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: - python-version: "3.8" + python-version: "3.11" - name: Install poetry - run: pip install poetry==1.3.2 + run: pip install poetry>=1.5.0 - name: Install Dependencies - run: poetry install --no-dev + run: poetry install --without dev - name: Build run: poetry build diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index 54c2785..b0aaeb1 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -14,15 +14,15 @@ jobs: runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: - python-version: 3.9 + python-version: "3.11" - name: Install poetry - run: pip install poetry==1.3.2 + run: pip install poetry>=1.5.0 - name: Install dependencies run: make install @@ -36,18 +36,18 @@ jobs: strategy: fail-fast: false matrix: - python-version: ["3.7", "3.8", "3.9", "3.10"] + python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"] steps: - - uses: actions/checkout@v2 + - uses: actions/checkout@v4 - name: Set up Python - uses: actions/setup-python@v2 + uses: actions/setup-python@v5 with: python-version: ${{ matrix.python-version }} - name: Install poetry - run: pip install poetry==1.3.2 + run: pip install poetry>=1.5.0 - name: Install dependencies run: make install @@ -56,4 +56,4 @@ jobs: run: make test - name: Upload coverage - uses: codecov/codecov-action@v3 + uses: codecov/codecov-action@v4 diff --git a/.gitignore b/.gitignore index c0adba5..5b741e9 100644 --- a/.gitignore +++ b/.gitignore @@ -44,6 +44,9 @@ settings.json .mypy_cache/ .pytest_cache/ +# Claude Code +CLAUDE.md + # Tests artifacts reports/ coverage.xml diff --git a/.readthedocs.yaml b/.readthedocs.yaml index 3ca9095..51a98cc 100644 --- a/.readthedocs.yaml +++ b/.readthedocs.yaml @@ -1,13 +1,13 @@ version: 2 build: - os: ubuntu-20.04 + os: ubuntu-22.04 tools: - python: "3.8" + python: "3.11" jobs: post_install: - - pip install --no-cache-dir poetry - - poetry export -f requirements.txt -o requirements.txt --without-hashes + - pip install --no-cache-dir poetry poetry-plugin-export + - poetry export -f requirements.txt -o requirements.txt --without-hashes --without dev - pip install --no-cache-dir -r requirements.txt sphinx: diff --git a/CHANGELOG.rst b/CHANGELOG.rst index 9ae0f93..eb3502e 100644 --- a/CHANGELOG.rst +++ b/CHANGELOG.rst @@ -1,6 +1,50 @@ Release Notes ============= +Version 0.5.0 (06.01.2025) +--------------------------- + +**Breaking Changes:** + +* Minimum Python version raised to 3.9 (dropped support for 3.7, 3.8) + +* Minimum PySpark version raised to 3.4 (dropped support for 3.2, 3.3) + +**New Features:** + +* Added support for Python 3.11, 3.12, 3.13 + +**Bug Fixes:** + +* Added hnswlib as fallback for nmslib on macOS ARM (fixes segfault in metric split) + +**Dependencies:** + +* Updated numpy to >=1.24.0, <3.0.0 + +* Updated pandas to >=1.5.0, <3.0.0 + +* Updated scipy to >=1.10.0 + +* Updated scikit-learn to >=1.3.0 + +* Updated nmslib to >=2.1.0 + +* Added hnswlib >=0.7.0 as alternative KNN backend + +* Updated catboost to >=1.2.0 + +* Updated other dependencies for Python 3.12/3.13 compatibility + +**Internal:** + +* Replaced deprecated ``pkg_resources`` with ``importlib.metadata`` + +* Updated CI/CD to test Python 3.9-3.13 + +* Updated GitHub Actions to v4/v5 + + Version 0.4.1 (21.04.2023) --------------------------- diff --git a/README.rst b/README.rst index e7c16dc..29b455f 100644 --- a/README.rst +++ b/README.rst @@ -17,7 +17,7 @@ Ambrosia :target: https://codecov.io/gh/MobileTeleSystems/Ambrosia .. |Black| image:: https://img.shields.io/badge/code%20style-black-000000.svg :target: https://github.com/psf/black -.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/ambrosia.svg +.. |Python Versions| image:: https://img.shields.io/pypi/pyversions/ambrosia.svg?v=0.5.0 :target: https://pypi.org/project/ambrosia .. |Telegram Channel| image:: https://img.shields.io/badge/telegram-Ambrosia-blueviolet.svg?logo=telegram :target: https://t.me/+Tkt43TNUUSAxNWNi @@ -62,6 +62,8 @@ and `Tutorials Preprocessor: + """ + Linearize a ratio metric for use in A/B testing. + + Computes a per-unit linearized value that is approximately normally + distributed, enabling correct t-test usage for ratio metrics: + + linearized_i = numerator_i - ratio * denominator_i + + where ratio = mean(numerator) / mean(denominator) is estimated on + the data passed to this ``Preprocessor`` instance (reference / control data). + + Parameters + ---------- + numerator : ColumnNameType + Column name of the ratio numerator (e.g. ``"revenue"``). + denominator : ColumnNameType + Column name of the ratio denominator (e.g. ``"orders"``). + transformed_name : ColumnNameType, optional + Name for the new linearized column. Defaults to + ``"{numerator}_lin"``. + load_path : Path, optional + Path to a json file with pre-fitted parameters. + + Returns + ------- + self : Preprocessor + Instance object. + """ + transformer = LinearizationTransformer() + if load_path is None: + transformer.fit_transform(self.dataframe, numerator, denominator, transformed_name, inplace=True) + else: + transformer.load_params(load_path) + transformer.transform(self.dataframe, inplace=True) + self.transformers.append(transformer) + return self + def transformations(self) -> List: """ List of all transformations which were called. diff --git a/ambrosia/preprocessing/transformers.py b/ambrosia/preprocessing/transformers.py index 6397641..1649e86 100644 --- a/ambrosia/preprocessing/transformers.py +++ b/ambrosia/preprocessing/transformers.py @@ -16,7 +16,7 @@ Module contains tools for metrics transformations during a preprocessing task. """ -from typing import Dict, Union +from typing import Dict, Optional, Union import numpy as np import pandas as pd @@ -386,3 +386,134 @@ def inverse_transform(self, dataframe: pd.DataFrame, inplace: bool = False) -> U transformed: pd.DataFrame = dataframe if inplace else dataframe.copy() transformed[self.column_names] = np.exp(transformed[self.column_names].values) return None if inplace else transformed + + +class LinearizationTransformer(AbstractFittableTransformer): + """ + Linearization transformer for ratio metrics. + + Converts a ratio metric (numerator / denominator) into a per-unit linearized + metric that is approximately normally distributed, enabling correct t-test usage: + + linearized_i = numerator_i - ratio * denominator_i + + where ratio = mean(numerator) / mean(denominator), estimated on the reference + (control group / historical) data passed to fit(). + + Parameters + ---------- + numerator : str + Column name of the ratio numerator (e.g. "revenue"). + denominator : str + Column name of the ratio denominator (e.g. "orders"). + transformed_name : str, optional + Name for the new column. Defaults to ``"{numerator}_lin"``. + + Examples + -------- + >>> transformer = LinearizationTransformer() + >>> transformer.fit(control_df, "revenue", "orders", "arpu_lin") + >>> transformer.transform(experiment_df, inplace=True) + """ + + def __str__(self) -> str: + return "Linearization transformation" + + def __init__(self) -> None: + self.numerator: Optional[str] = None + self.denominator: Optional[str] = None + self.transformed_name: Optional[str] = None + self.ratio: Optional[float] = None + super().__init__() + + def get_params_dict(self) -> Dict: + self._check_fitted() + return { + "numerator": self.numerator, + "denominator": self.denominator, + "transformed_name": self.transformed_name, + "ratio": self.ratio, + } + + def load_params_dict(self, params: Dict) -> None: + for key in ("numerator", "denominator", "transformed_name", "ratio"): + if key not in params: + raise TypeError(f"params argument must contain: {key}") + setattr(self, key, params[key]) + self.fitted = True + + def fit( + self, + dataframe: pd.DataFrame, + numerator: str, + denominator: str, + transformed_name: Optional[str] = None, + ): + """ + Estimate ratio = mean(numerator) / mean(denominator) on reference data. + + Parameters + ---------- + dataframe : pd.DataFrame + Reference dataframe (typically control group or historical data). + numerator : str + Column name of the ratio numerator. + denominator : str + Column name of the ratio denominator. + transformed_name : str, optional + Name for the linearized column. Defaults to ``"{numerator}_lin"``. + """ + self._check_cols(dataframe, [numerator, denominator]) + denom_mean = dataframe[denominator].mean() + if denom_mean == 0: + raise ValueError(f"Mean of denominator column '{denominator}' is zero; cannot compute ratio.") + self.numerator = numerator + self.denominator = denominator + self.transformed_name = transformed_name if transformed_name is not None else f"{numerator}_lin" + self.ratio = dataframe[numerator].mean() / denom_mean + self.fitted = True + return self + + def transform(self, dataframe: pd.DataFrame, inplace: bool = False) -> Union[pd.DataFrame, None]: + """ + Apply linearization: transformed = numerator - ratio * denominator. + + Parameters + ---------- + dataframe : pd.DataFrame + Dataframe to transform. + inplace : bool, default: ``False`` + If ``True`` modifies dataframe in place, otherwise returns a copy. + """ + self._check_fitted() + self._check_cols(dataframe, [self.numerator, self.denominator]) + df = dataframe if inplace else dataframe.copy() + df[self.transformed_name] = df[self.numerator] - self.ratio * df[self.denominator] + return None if inplace else df + + def fit_transform( + self, + dataframe: pd.DataFrame, + numerator: str, + denominator: str, + transformed_name: Optional[str] = None, + inplace: bool = False, + ) -> Union[pd.DataFrame, None]: + """ + Fit and transform in one step. + + Parameters + ---------- + dataframe : pd.DataFrame + Reference dataframe for fitting and transformation. + numerator : str + Column name of the ratio numerator. + denominator : str + Column name of the ratio denominator. + transformed_name : str, optional + Name for the linearized column. + inplace : bool, default: ``False`` + If ``True`` modifies dataframe in place. + """ + self.fit(dataframe, numerator, denominator, transformed_name) + return self.transform(dataframe, inplace) diff --git a/ambrosia/tester/handlers.py b/ambrosia/tester/handlers.py index 7d65c35..5c97779 100644 --- a/ambrosia/tester/handlers.py +++ b/ambrosia/tester/handlers.py @@ -51,7 +51,15 @@ class SparkCriteria(enum.Enum): class TheoreticalTesterHandler: def __init__( - self, group_a, group_b, column: str, alpha: np.ndarray, effect_type: str, criterion: StatCriterion, **kwargs + self, + group_a, + group_b, + column: str, + alpha: np.ndarray, + effect_type: str, + criterion: StatCriterion, + metric_func=None, + **kwargs, ): self.group_a = group_a self.group_b = group_b @@ -59,6 +67,7 @@ def __init__( self.alpha = alpha self.effect_type = effect_type self.criterion = criterion + self.metric_func = metric_func self.kwargs = kwargs def _correct_criterion(self, criterion: tp.Any) -> bool: @@ -79,8 +88,12 @@ def get_criterion(self, criterion: str, data_example: types.SparkOrPandas): def _set_kwargs(self): if isinstance(self.group_a, pd.DataFrame): - self.group_a = self.group_a[self.column].values - self.group_b = self.group_b[self.column].values + if self.metric_func is not None: + self.group_a = np.asarray(self.metric_func(self.group_a)) + self.group_b = np.asarray(self.metric_func(self.group_b)) + else: + self.group_a = self.group_a[self.column].values + self.group_b = self.group_b[self.column].values elif isinstance(self.group_a, types.SparkDataFrame): self.kwargs["column"] = self.column self.kwargs["alpha"] = self.alpha diff --git a/ambrosia/tester/tester.py b/ambrosia/tester/tester.py index 386d6a5..38264e0 100644 --- a/ambrosia/tester/tester.py +++ b/ambrosia/tester/tester.py @@ -29,7 +29,7 @@ """ import itertools from copy import deepcopy -from typing import Dict, List, Optional, Union +from typing import Callable, Dict, List, Optional, Union from warnings import warn import numpy as np @@ -88,6 +88,12 @@ class Tester(ABToolAbstract): metrics : MetricNameType, optional Metrics (columns of dataframe) which is used to calculate experiment result. + metric_funcs : Dict[str, Callable], optional + Dictionary mapping metric names to callable functions. + Each function receives a ``pd.DataFrame`` (group data) and must + return an array-like of numeric values. When provided, the + function is used instead of column lookup for the corresponding + metric name. Only supported for pandas DataFrames. Attributes ---------- @@ -241,6 +247,7 @@ def __init__( id_column: Optional[types.ColumnNameType] = None, first_type_errors: types.StatErrorType = 0.05, metrics: Optional[types.MetricNamesType] = None, + metric_funcs: Optional[Dict[str, Callable]] = None, ): """ Tester class constructor to initialize the object. @@ -257,6 +264,7 @@ def __init__( self.set_experiment_results(experiment_results=experiment_results) self.set_errors(first_type_errors) self.set_metrics(metrics) + self.__metric_funcs = metric_funcs or {} @staticmethod def __filter_data( @@ -372,9 +380,15 @@ def __pre_run(method: str, args: types._UsageArgumentsType, **kwargs) -> types.T if method not in accepted_methods: raise ValueError(f'Choose method from {", ".join(accepted_methods)}') result: types.TesterResult = {} + metric_funcs: Dict = args.get("metric_funcs", {}) for metric in args["metrics"]: - a_values: np.ndarray = args["data_a_group"][metric].values - b_values: np.ndarray = args["data_b_group"][metric].values + metric_func = metric_funcs.get(metric) + if metric_func is not None: + a_values: np.ndarray = np.asarray(metric_func(args["data_a_group"])) + b_values: np.ndarray = np.asarray(metric_func(args["data_b_group"])) + else: + a_values = args["data_a_group"][metric].values + b_values = args["data_b_group"][metric].values if method == "theory": # TODO: Make it SolverClass ~ method # solver = SolverClass(...) @@ -386,6 +400,7 @@ def __pre_run(method: str, args: types._UsageArgumentsType, **kwargs) -> types.T alpha=np.array(args["alpha"]), effect_type=args["effect_type"], criterion=args["criterion"], + metric_func=metric_func, **kwargs, ) sub_result = solver.solve() @@ -473,6 +488,7 @@ def run( criterion: Optional[ABStatCriterion] = None, correction_method: Union[str, None] = "bonferroni", as_table: bool = True, + metric_funcs: Optional[Dict[str, Callable]] = None, **kwargs, ) -> types.TesterResult: """ @@ -515,6 +531,11 @@ def run( as_table : bool, default: ``True`` Return the test results as a pandas dataframe. If ``False``, a list of dicts with results will be returned. + metric_funcs : Dict[str, Callable], optional + Dictionary mapping metric names to callable functions. + Each function receives a group ``pd.DataFrame`` and returns + array-like values. Overrides functions set in constructor + for matching metric names. Only pandas DataFrames supported. **kwargs : Dict Other keyword arguments. @@ -556,6 +577,8 @@ def run( chosen_args: types._UsageArgumentsType = Tester._prepare_arguments(arguments_choice) chosen_args["effect_type"] = effect_type chosen_args["criterion"] = criterion + effective_metric_funcs = {**self.__metric_funcs, **(metric_funcs or {})} + chosen_args["metric_funcs"] = effective_metric_funcs hypothesis_num: int = len(list(itertools.combinations(chosen_args["experiment_results"], 2))) * len( chosen_args["metrics"] @@ -602,6 +625,7 @@ def test( criterion: Optional[ABStatCriterion] = None, correction_method: Union[str, None] = "bonferroni", as_table: bool = True, + metric_funcs: Optional[Dict[str, Callable]] = None, **kwargs, ) -> types.TesterResult: """ @@ -649,6 +673,10 @@ def test( as_table : bool, default: ``True`` Return the test results as a pandas dataframe. If ``False``, a list of dicts with results will be returned. + metric_funcs : Dict[str, Callable], optional + Dictionary mapping metric names to callable functions. + Each function receives a group ``pd.DataFrame`` and returns + array-like values. Only pandas DataFrames supported. **kwargs : Dict Other keyword arguments. @@ -673,5 +701,6 @@ def test( criterion=criterion, correction_method=correction_method, as_table=as_table, + metric_funcs=metric_funcs, **kwargs, ) diff --git a/ambrosia/tools/import_tools.py b/ambrosia/tools/import_tools.py index fe428be..66ab99d 100644 --- a/ambrosia/tools/import_tools.py +++ b/ambrosia/tools/import_tools.py @@ -18,8 +18,7 @@ import typing as tp - -import pkg_resources +from importlib.metadata import distributions class NotInstalledPackage(Exception): @@ -41,7 +40,7 @@ class PysparkNotInstalled(NotInstalledPackage, default_message="Install pyspark def get_installed_package_names() -> tp.List[str]: - return [package.key for package in pkg_resources.working_set] + return [dist.metadata["Name"].lower() for dist in distributions()] def check_package(package_name: str) -> bool: diff --git a/ambrosia/tools/knn.py b/ambrosia/tools/knn.py index 3a90129..45b2fd2 100644 --- a/ambrosia/tools/knn.py +++ b/ambrosia/tools/knn.py @@ -14,22 +14,53 @@ from typing import Any, List -import nmslib import numpy as np +# Try nmslib first, fallback to hnswlib if it fails (e.g., on macOS ARM) +_USE_HNSWLIB = False +try: + import nmslib +except ImportError: + _USE_HNSWLIB = True + nmslib = None + +# Always try to import hnswlib as fallback +try: + import hnswlib +except ImportError: + hnswlib = None + class NMTree: """ - KNN solution from NMSLIB. + KNN solution with nmslib (primary) or hnswlib (fallback for macOS ARM). """ def __init__(self, points: np.ndarray, payload: np.ndarray, ef_search: int): self.__was = set() - self.__index = nmslib.init(space="l2") - self.__index.addDataPointBatch(points) - self.__index.createIndex() - self.__index.setQueryTimeParams({"efSearch": ef_search}) self.__payload = payload + self.__use_hnswlib = _USE_HNSWLIB + + if not self.__use_hnswlib: + try: + self.__index = nmslib.init(space="l2") + self.__index.addDataPointBatch(points) + self.__index.createIndex() + self.__index.setQueryTimeParams({"efSearch": ef_search}) + except Exception: + # nmslib crashed (segfault on macOS ARM), try hnswlib + self.__use_hnswlib = True + + if self.__use_hnswlib: + if hnswlib is None: + raise ImportError( + "nmslib failed and hnswlib is not installed. " "Please install hnswlib: pip install hnswlib" + ) + dim = points.shape[1] + self.__index = hnswlib.Index(space="l2", dim=dim) + self.__index.init_index(max_elements=len(points), ef_construction=200, M=16) + self.__index.add_items(points, np.arange(len(points))) + self.__index.set_ef(ef_search) def query_batch( self, @@ -58,8 +89,16 @@ def query_batch( threads : int, default: ``1`` Amount of threads to be used """ - neighbours = self.__index.knnQueryBatch(points, k=group_size * amount, num_threads=threads) - indices: List[np.ndarray] = [x[0] for x in neighbours] + k = group_size * amount + + if self.__use_hnswlib: + self.__index.set_num_threads(threads) + labels, _ = self.__index.knn_query(points, k=k) + indices = [row for row in labels] + else: + neighbours = self.__index.knnQueryBatch(points, k=k, num_threads=threads) + indices: List[np.ndarray] = [x[0] for x in neighbours] + for i, index in enumerate(indices): current_answer: List = [] for j in index: diff --git a/ambrosia/tools/pvalue_tools.py b/ambrosia/tools/pvalue_tools.py index f9feb38..1002cf3 100644 --- a/ambrosia/tools/pvalue_tools.py +++ b/ambrosia/tools/pvalue_tools.py @@ -56,11 +56,7 @@ def calc_statistic_for_delta_method( """ return np.sqrt( np.abs( - ( - var_group_b / (mean_a**2) - - 2 * covariance_ab * mean_b / mean_a**3 - + var_group_a * (mean_b**2) / mean_a**4 - ) + (var_group_b / (mean_a**2) - 2 * covariance_ab * mean_b / mean_a**3 + var_group_a * (mean_b**2) / mean_a**4) / size ) ) diff --git a/context7.json b/context7.json new file mode 100644 index 0000000..ffa6bc5 --- /dev/null +++ b/context7.json @@ -0,0 +1,4 @@ +{ + "url": "https://context7.com/mobiletelesystems/ambrosia", + "public_key": "pk_cSA3cXMaOugl1CKQyOg54" +} diff --git a/docs/requirements.txt b/docs/requirements.txt index d7b9261..73520ab 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,7 +1,6 @@ -sphinx +sphinx>=7.0,<9.0 sphinx_copybutton -sphinx_gallery==0.10.0 nbsphinx nbsphinx_link -numpydoc +numpydoc>=1.6.0,<1.9.0 furo diff --git a/docs/source/ambrosia_elements/designer.rst b/docs/source/ambrosia_elements/designer.rst index 9bd6fb1..010dd5f 100644 --- a/docs/source/ambrosia_elements/designer.rst +++ b/docs/source/ambrosia_elements/designer.rst @@ -32,9 +32,9 @@ groups size, and experiment statistical power, based on historical metrics value Examples of using experiment design tools ----------------------------------------- -.. nblinkgallery:: - :name: designer-examples +.. toctree:: + :maxdepth: 1 - /pandas_examples/03_pandas_designer - /pandas_examples/04_binary_design - /spark_examples/07_spark_designer \ No newline at end of file + /pandas_examples/03_pandas_designer + /pandas_examples/04_binary_design + /spark_examples/07_spark_designer \ No newline at end of file diff --git a/docs/source/ambrosia_elements/preprocessing.rst b/docs/source/ambrosia_elements/preprocessing.rst index de9027a..e16964d 100644 --- a/docs/source/ambrosia_elements/preprocessing.rst +++ b/docs/source/ambrosia_elements/preprocessing.rst @@ -25,10 +25,10 @@ Data preprocessing tools: Examples of using data transformation tools ------------------------------------------- -.. nblinkgallery:: - :name: preprocessing-examples +.. toctree:: + :maxdepth: 1 - /pandas_examples/00_preprocessing - /pandas_examples/01_vr_transformations - /pandas_examples/02_preprocessor - /pandas_examples/11_cuped_example \ No newline at end of file + /pandas_examples/00_preprocessing + /pandas_examples/01_vr_transformations + /pandas_examples/02_preprocessor + /pandas_examples/11_cuped_example \ No newline at end of file diff --git a/docs/source/ambrosia_elements/splitter.rst b/docs/source/ambrosia_elements/splitter.rst index ad51d6e..de47cd9 100644 --- a/docs/source/ambrosia_elements/splitter.rst +++ b/docs/source/ambrosia_elements/splitter.rst @@ -29,8 +29,8 @@ experimental groups using different approaches. Examples of using groups splitting tools ---------------------------------------- -.. nblinkgallery:: - :name: splitter-examples +.. toctree:: + :maxdepth: 1 /pandas_examples/05_pandas_splitter /spark_examples/08_spark_splitter \ No newline at end of file diff --git a/docs/source/ambrosia_elements/tester.rst b/docs/source/ambrosia_elements/tester.rst index f279a5d..6683a94 100644 --- a/docs/source/ambrosia_elements/tester.rst +++ b/docs/source/ambrosia_elements/tester.rst @@ -29,8 +29,8 @@ and calculating the experimental uplift value with corresponding confidence inte Examples of using testing tools ------------------------------- -.. nblinkgallery:: - :name: tester-examples +.. toctree:: + :maxdepth: 1 /pandas_examples/06_pandas_tester /spark_examples/09_spark_tester \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index c7c2fd7..7dcea1f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -5,6 +5,13 @@ import os import sys +# Mock heavy C++ dependencies for ReadTheDocs +autodoc_mock_imports = [ + "nmslib", + "hnswlib", + "catboost", + "pyspark", +] # Configuration file for the Sphinx documentation builder. # @@ -54,7 +61,6 @@ "numpydoc", "nbsphinx", "nbsphinx_link", - "sphinx_gallery.load_style", "sphinx_copybutton", "sphinx.ext.autosectionlabel", ] diff --git a/docs/source/nb_pandas_examples.rst b/docs/source/nb_pandas_examples.rst index 5b0bfaa..cfcc4ca 100644 --- a/docs/source/nb_pandas_examples.rst +++ b/docs/source/nb_pandas_examples.rst @@ -1,8 +1,8 @@ Pandas Data Examples -------------------- -.. nblinkgallery:: - :name: pandas-examples +.. toctree:: + :maxdepth: 1 /pandas_examples/00_preprocessing /pandas_examples/01_vr_transformations diff --git a/docs/source/nb_spark_examples.rst b/docs/source/nb_spark_examples.rst index fddcca6..48db39d 100644 --- a/docs/source/nb_spark_examples.rst +++ b/docs/source/nb_spark_examples.rst @@ -1,9 +1,9 @@ Spark Data Examples ------------------- -.. nblinkgallery:: - :name: spark-examples +.. toctree:: + :maxdepth: 1 - /spark_examples/07_spark_designer - /spark_examples/08_spark_splitter - /spark_examples/09_spark_tester \ No newline at end of file + /spark_examples/07_spark_designer + /spark_examples/08_spark_splitter + /spark_examples/09_spark_tester \ No newline at end of file diff --git a/examples/test_installation.ipynb b/examples/test_installation.ipynb new file mode 100644 index 0000000..fa4fce9 --- /dev/null +++ b/examples/test_installation.ipynb @@ -0,0 +1,197 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Тест установки Ambrosia\n", + "\n", + "Этот ноутбук проверяет работоспособность библиотеки Ambrosia после установки." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Проверка импорта" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "print(f\"Python version: {sys.version}\")\n", + "\n", + "import ambrosia\n", + "from ambrosia.designer import Designer\n", + "from ambrosia.splitter import Splitter\n", + "from ambrosia.tester import Tester\n", + "print(f\"Ambrosia version: {ambrosia.__version__}\")\n", + "print(\"✅ Импорт успешен!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Подготовка тестовых данных" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "\n", + "np.random.seed(42)\n", + "\n", + "df = pd.DataFrame({\n", + " 'user_id': range(1000),\n", + " 'metric': np.random.normal(100, 15, 1000),\n", + " 'feature': np.random.rand(1000)\n", + "})\n", + "\n", + "print(f\"Размер данных: {df.shape}\")\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Designer — расчёт размера выборки" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "designer = Designer(dataframe=df, effects=1.05, metrics='metric')\n", + "result = designer.run('size')\n", + "print(\"Результат Designer (размер выборки):\")\n", + "print(result)\n", + "print(\"\\n✅ Designer работает!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Splitter (hash) — разбиение на группы" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "splitter = Splitter(dataframe=df, id_column='user_id', groups_size=500)\n", + "result_hash = splitter.run(method='hash', groups_number=2)\n", + "print(\"Распределение по группам (hash):\")\n", + "print(result_hash['group'].value_counts())\n", + "print(\"\\n✅ Splitter (hash) работает!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 5. Splitter (metric) — проверка hnswlib fallback\n", + "\n", + "Этот тест проверяет, что metric split работает на macOS ARM благодаря fallback на hnswlib." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "splitter_metric = Splitter(dataframe=df, id_column='user_id', groups_size=500)\n", + "result_metric = splitter_metric.run(method='metric', groups_number=2, fit_columns=['feature'])\n", + "print(\"Распределение по группам (metric):\")\n", + "print(result_metric['group'].value_counts())\n", + "print(\"\\n✅ Splitter (metric) работает! (hnswlib fallback успешен)\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 6. Tester — статистический тест" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df_test = pd.DataFrame({\n", + " 'user_id': range(200),\n", + " 'group': ['A'] * 100 + ['B'] * 100,\n", + " 'metric': np.concatenate([\n", + " np.random.normal(100, 15, 100),\n", + " np.random.normal(105, 15, 100)\n", + " ])\n", + "})\n", + "\n", + "tester = Tester(dataframe=df_test, column_groups='group')\n", + "result_test = tester.run(metrics='metric')\n", + "print(\"Результат Tester:\")\n", + "print(result_test)\n", + "print(\"\\n✅ Tester работает!\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 7. Итог" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print(\"=\"*50)\n", + "print(\"РЕЗУЛЬТАТЫ ТЕСТИРОВАНИЯ\")\n", + "print(\"=\"*50)\n", + "print(f\"Python: {sys.version.split()[0]}\")\n", + "print(f\"Ambrosia: {ambrosia.__version__}\")\n", + "print()\n", + "print(\"✅ Импорт: OK\")\n", + "print(\"✅ Designer: OK\")\n", + "print(\"✅ Splitter (hash): OK\")\n", + "print(\"✅ Splitter (metric): OK\")\n", + "print(\"✅ Tester: OK\")\n", + "print()\n", + "print(\"🎉 Все проверки пройдены успешно!\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "3.13.0" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/poetry.lock b/poetry.lock index a40864f..2d6307d 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,317 +1,671 @@ -# This file is automatically @generated by Poetry and should not be changed by hand. +# This file is automatically @generated by Poetry 2.1.3 and should not be changed by hand. 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a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "Ambrosia" -version = "0.4.1" +version = "0.5.0" description = "A Python library for working with A/B tests." license = "Apache-2.0" authors = [ @@ -22,10 +22,11 @@ keywords = [ classifiers = [ "Development Status :: 3 - Alpha", "Topic :: Scientific/Engineering :: Mathematics", - "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.10", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", "Programming Language :: Python :: 3", "Topic :: Software Development :: Libraries :: Python Modules", "Intended Audience :: Science/Research", @@ -39,38 +40,40 @@ packages = [ ] [tool.poetry.dependencies] -python = ">=3.7.2, <3.11.0" -jinja2 = "^3.0.0" -joblib = "^1.1.0" -nmslib = "^2.0.4" -numpy = ">=1.19.5, <2.0.0" -pandas = ">=0.25.3, <2.0.0" -pyspark = { version = "^3.2", optional = true } -PyYAML = "6.0" -scikit-learn = "^1.0.2" -scipy = "^1.6.3" -tqdm = "^4.27.0" -hyperopt = "^0.2.7" -catboost = "^1.0.4" -statsmodels = ">=0.13.0" +python = ">=3.9, <3.14" +jinja2 = ">=3.1.0, <4.0.0" +joblib = ">=1.3.0, <2.0.0" +nmslib = ">=2.1.0, <3.0.0" +hnswlib = ">=0.7.0, <1.0.0" +numpy = ">=1.24.0, <3.0.0" +pandas = ">=1.5.0, <3.0.0" +pyspark = { version = ">=3.4, <5.0", optional = true } +PyYAML = ">=6.0.1, <7.0.0" +scikit-learn = ">=1.3.0, <2.0.0" +scipy = ">=1.10.0, <2.0.0" +tqdm = ">=4.65.0, <5.0.0" +hyperopt = ">=0.2.7, <0.3.0" +setuptools = ">=65.0.0, <82.0.0" +catboost = ">=1.2.0, <2.0.0" +statsmodels = ">=0.14.0, <1.0.0" -[tool.poetry.dev-dependencies] -autopep8 = "1.6.0" -black = "22.6.0" -isort = "5.10.1" -pylint = "2.14.5" -flake8 = "4.0.1" -flake8-docstrings = "1.6.0" -pytest = "7.1.2" -pytest-cov = "3.0.0" -pytest-lazy-fixture = "0.6.3" +[tool.poetry.group.dev.dependencies] +autopep8 = ">=2.0.0, <3.0.0" +black = ">=23.0.0, <25.0.0" +isort = ">=5.12.0, <6.0.0" +pylint = ">=3.0.0, <4.0.0" +flake8 = ">=6.1.0, <8.0.0" +flake8-docstrings = ">=1.7.0, <2.0.0" +pytest = ">=7.4.0, <9.0.0" +pytest-cov = ">=4.1.0, <6.0.0" +pytest-lazy-fixtures = ">=1.0.0, <2.0.0" [tool.poetry.extras] spark = ["pyspark"] [tool.black] line-length = 120 -target-version = ['py37', 'py38', 'py39', 'py310'] +target-version = ['py39', 'py310', 'py311', 'py312', 'py313'] include = '\.pyi?$' exclude = ''' @@ -101,5 +104,5 @@ markers = [ ] [build-system] -requires = ["poetry>=1.0.5"] -build-backend = "poetry.masonry.api" +requires = ["poetry-core>=1.0.0"] +build-backend = "poetry.core.masonry.api" diff --git a/tests/test_designer.py b/tests/test_designer.py index 2450366..1459971 100644 --- a/tests/test_designer.py +++ b/tests/test_designer.py @@ -5,6 +5,7 @@ import pandas as pd import pytest import yaml +from pytest_lazy_fixtures import lf from ambrosia.designer import Designer, design, design_binary, load_from_config @@ -53,12 +54,12 @@ def test_corret_type(designer_simple_table, designer_ltv): @pytest.mark.parametrize( "param_to_design, designer, expected_value", [ - ("size", pytest.lazy_fixture("designer_simple_table"), 603), - ("effect", pytest.lazy_fixture("designer_simple_table"), "49.2%"), - ("power", pytest.lazy_fixture("designer_simple_table"), "20.7%"), - ("size", pytest.lazy_fixture("designer_ltv"), 1553), - ("effect", pytest.lazy_fixture("designer_ltv"), "17.6%"), - ("power", pytest.lazy_fixture("designer_ltv"), "35.6%"), + ("size", lf("designer_simple_table"), 603), + ("effect", lf("designer_simple_table"), "49.2%"), + ("power", lf("designer_simple_table"), "20.7%"), + ("size", lf("designer_ltv"), 1553), + ("effect", lf("designer_ltv"), "17.6%"), + ("power", lf("designer_ltv"), "35.6%"), ], ) def test_run_theory(param_to_design, expected_value, designer): @@ -167,9 +168,9 @@ def test_design_binary_function(to_design, effects, sizes, beta, method, groups_ @pytest.mark.parametrize( "param_to_design, designer, expected_value", [ - ("size", pytest.lazy_fixture("designer_ltv_spark"), 1553), - ("effect", pytest.lazy_fixture("designer_ltv_spark"), "17.6%"), - ("power", pytest.lazy_fixture("designer_ltv_spark"), "35.6%"), + ("size", lf("designer_ltv_spark"), 1553), + ("effect", lf("designer_ltv_spark"), "17.6%"), + ("power", lf("designer_ltv_spark"), "35.6%"), ], ) def test_run_theory_spark(param_to_design, expected_value, designer): @@ -186,9 +187,9 @@ def test_run_theory_spark(param_to_design, expected_value, designer): @pytest.mark.parametrize( "param_to_design, designer", [ - ("size", pytest.lazy_fixture("designer_ltv_spark")), - ("effect", pytest.lazy_fixture("designer_ltv_spark")), - ("power", pytest.lazy_fixture("designer_ltv_spark")), + ("size", lf("designer_ltv_spark")), + ("effect", lf("designer_ltv_spark")), + ("power", lf("designer_ltv_spark")), ], ) def test_empiric_spark(param_to_design, designer): diff --git a/tests/test_preprocessor.py b/tests/test_preprocessor.py index 9106336..e2954b9 100644 --- a/tests/test_preprocessor.py +++ b/tests/test_preprocessor.py @@ -1,5 +1,6 @@ import os +import numpy as np import pandas as pd import pytest @@ -117,3 +118,71 @@ def test_store_load_config(data_for_agg): transformed_by_config: pd.DataFrame = loaded_preprocessor.apply_transformations() os.remove(store_path) assert (transformed == transformed_by_config).all(None) + + +@pytest.mark.smoke() +def test_linearize_basic(data_nonlin_var): + """ + Test that linearize creates new column and returns self. + """ + preprocessor = Preprocessor(data_nonlin_var, verbose=False) + result = preprocessor.linearize("target", "feature_1", transformed_name="target_lin") + assert result is preprocessor # method chaining + assert "target_lin" in preprocessor.data().columns + + +@pytest.mark.unit() +def test_linearize_formula(data_nonlin_var): + """ + Test that linearized values satisfy: linearized = num - ratio * denom. + """ + preprocessor = Preprocessor(data_nonlin_var, verbose=False) + preprocessor.linearize("target", "feature_1", transformed_name="target_lin") + df = preprocessor.data() + transformer = preprocessor.transformations()[-1] + ratio = transformer.ratio + expected = data_nonlin_var["target"] - ratio * data_nonlin_var["feature_1"] + np.testing.assert_allclose(df["target_lin"].values, expected.values, rtol=1e-10) + + +@pytest.mark.unit() +def test_linearize_in_chain(data_nonlin_var): + """ + Test linearize as part of a preprocessing chain. + """ + preprocessor = Preprocessor(data_nonlin_var, verbose=False) + result = ( + preprocessor.robust("feature_1", alpha=0.01) + .linearize("target", "feature_1", transformed_name="target_lin") + .data() + ) + assert "target_lin" in result.columns + + +@pytest.mark.unit() +def test_linearize_load_store(data_nonlin_var): + """ + Test that linearization transformer can be serialized and replayed. + """ + store_path = "tests/configs/linearize_config.json" + preprocessor = Preprocessor(data_nonlin_var, verbose=False) + preprocessor.linearize("target", "feature_1", transformed_name="target_lin") + preprocessor.store_transformations(store_path) + + loaded_preprocessor = Preprocessor(data_nonlin_var, verbose=False) + loaded_preprocessor.load_transformations(store_path) + + os.remove(store_path) + + for t, lt in zip(preprocessor.transformations(), loaded_preprocessor.transformations()): + assert t.get_params_dict() == lt.get_params_dict() + + +@pytest.mark.unit() +def test_linearize_default_name(data_nonlin_var): + """ + Test that default transformed_name is '{numerator}_lin'. + """ + preprocessor = Preprocessor(data_nonlin_var, verbose=False) + preprocessor.linearize("target", "feature_1") + assert "target_lin" in preprocessor.data().columns diff --git a/tests/test_stratification.py b/tests/test_stratification.py index 4ebc1b4..5da05ee 100644 --- a/tests/test_stratification.py +++ b/tests/test_stratification.py @@ -1,5 +1,6 @@ import numpy as np import pytest +from pytest_lazy_fixtures import lf from ambrosia.tools.ab_abstract_component import EmptyStratValue from ambrosia.tools.stratification import Stratification @@ -43,8 +44,8 @@ def test_strat_sizes(stratificator): @pytest.mark.parametrize( "ids, column, answer", [ - (np.arange(0, 1000, 43), None, pytest.lazy_fixture("answer_ids_strat")), - (pytest.lazy_fixture("id_for_b_strat"), "id", pytest.lazy_fixture("answer_ids_strat_column")), + (np.arange(0, 1000, 43), None, lf("answer_ids_strat")), + (lf("id_for_b_strat"), "id", lf("answer_ids_strat_column")), ], ) def test_test_ids(ids, column, answer, stratificator): diff --git a/tests/test_tester.py b/tests/test_tester.py index c4ba892..b8eab6b 100644 --- a/tests/test_tester.py +++ b/tests/test_tester.py @@ -384,3 +384,80 @@ def test_paired_bootstrap(effect_type, alternative): ) assert test_results_dep[0]["pvalue"] < test_results_ind[0]["pvalue"] assert test_results_dep[0]["confidence_interval"][0] > test_results_ind[0]["confidence_interval"][0] + + +@pytest.mark.unit +def test_metric_func_constructor(results_ltv_retention_conversions): + """ + Test that metric_funcs passed to constructor are used when metric name matches. + """ + # ratio metric: ltv / retention (arbitrary, just to test callable path) + ratio_func = lambda df: (df["ltv"] / (df["retention"] + 1e-6)).values + tester = Tester( + dataframe=results_ltv_retention_conversions, + column_groups="group", + metrics=["ratio_metric"], + metric_funcs={"ratio_metric": ratio_func}, + ) + result = tester.run(as_table=False) + assert len(result) == 1 + assert "pvalue" in result[0] + + +@pytest.mark.unit +@pytest.mark.parametrize("method", ["theory", "empiric"]) +def test_metric_func_run(method, results_ltv_retention_conversions): + """ + Test that metric_funcs passed to run() work for theory and empiric methods. + """ + double_ltv = lambda df: (df["ltv"] * 2).values + tester = Tester( + dataframe=results_ltv_retention_conversions, + column_groups="group", + metrics=["ltv"], + ) + result_normal = tester.run(method=method, metrics=["ltv"], as_table=False) + result_func = tester.run( + method=method, + metrics=["custom"], + metric_funcs={"custom": double_ltv}, + as_table=False, + ) + # Doubling values doesn't change pvalue for ttest (same scale), but effect should be doubled + assert abs(result_func[0]["effect"]) == pytest.approx(abs(result_normal[0]["effect"]) * 2, rel=1e-4) + + +@pytest.mark.unit +def test_metric_func_overrides_constructor(results_ltv_retention_conversions): + """ + Test that metric_funcs in run() override those set in constructor. + """ + func_a = lambda df: df["ltv"].values + func_b = lambda df: (df["ltv"] * 3).values + tester = Tester( + dataframe=results_ltv_retention_conversions, + column_groups="group", + metric_funcs={"my_metric": func_a}, + ) + result_a = tester.run(metrics=["my_metric"], as_table=False) + result_b = tester.run(metrics=["my_metric"], metric_funcs={"my_metric": func_b}, as_table=False) + assert abs(result_b[0]["effect"]) == pytest.approx(abs(result_a[0]["effect"]) * 3, rel=1e-4) + + +@pytest.mark.unit +def test_metric_func_bootstrap(results_ltv_retention_conversions): + """ + Test that metric_funcs work with empiric (bootstrap) method. + """ + double_ltv = lambda df: (df["ltv"] * 2).values + tester = Tester( + dataframe=results_ltv_retention_conversions, + column_groups="group", + metrics=["custom"], + metric_funcs={"custom": double_ltv}, + ) + result = tester.run(method="empiric", as_table=False) + assert len(result) == 1 + assert "pvalue" in result[0] + assert "effect" in result[0] + assert "confidence_interval" in result[0]