From 7be3a29637d839599d5788398391feebcb2740d3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?M=C3=A9d=C3=A9ric=20Hurier=20=28Fmind=29?= Date: Sat, 16 Mar 2024 19:14:23 +0100 Subject: [PATCH] v0.7.0 (#8) --- .gitignore | 2 +- .pre-commit-config.yaml | 14 +- confs/promotion.yaml | 2 + confs/tuning.yaml | 3 - mlops-python-package.code-workspace | 3 +- poetry.lock | 3532 +++++++++++++++++ pyproject.toml | 55 +- src/bikes/core/__init__.py | 1 + src/bikes/{ => core}/metrics.py | 22 +- src/bikes/{ => core}/models.py | 37 +- src/bikes/core/schemas.py | 91 + src/bikes/io/__init__.py | 1 + src/bikes/{ => io}/configs.py | 12 +- src/bikes/{ => io}/datasets.py | 14 +- src/bikes/io/registries.py | 314 ++ src/bikes/io/services.py | 176 + src/bikes/jobs.py | 304 -- src/bikes/jobs/__init__.py | 16 + src/bikes/jobs/base.py | 79 + src/bikes/jobs/inference.py | 64 + src/bikes/jobs/promotion.py | 52 + src/bikes/jobs/training.py | 120 + src/bikes/jobs/tuning.py | 94 + src/bikes/registers.py | 185 - src/bikes/schemas.py | 87 - src/bikes/scripts.py | 57 +- src/bikes/services.py | 212 - src/bikes/settings.py | 28 + src/bikes/utils/__init__.py | 1 + src/bikes/{ => utils}/searchers.py | 55 +- src/bikes/utils/signers.py | 55 + src/bikes/{ => utils}/splitters.py | 49 +- tasks/__init__.py | 4 +- tasks/carbons.py | 19 - tasks/checks.py | 4 +- tasks/cleans.py | 14 +- tasks/commits.py | 2 +- tasks/containers.py | 2 +- tasks/dags.py | 9 +- tasks/docs.py | 2 +- tasks/formats.py | 4 +- tasks/installs.py | 4 +- tasks/mlflow.py | 29 + tasks/packages.py | 2 +- .../{0. invalid.yaml => 1. invalid.yaml} | 0 tests/confs/valid/1. tuning.yaml | 7 +- tests/confs/valid/2. training.yaml | 7 +- tests/confs/valid/3. inference.yaml | 12 - tests/confs/valid/3. promotion.yaml | 2 + tests/confs/valid/4. inference.yaml | 9 + tests/conftest.py | 267 +- tests/{ => core}/test_metrics.py | 7 +- tests/{ => core}/test_models.py | 20 +- tests/{ => core}/test_schemas.py | 5 +- tests/{ => io}/test_configs.py | 12 +- tests/{ => io}/test_datasets.py | 3 +- tests/io/test_registries.py | 131 + tests/io/test_services.py | 59 + tests/jobs/test_base.py | 29 + tests/jobs/test_inference.py | 59 + tests/jobs/test_promotion.py | 66 + tests/jobs/test_training.py | 140 + tests/jobs/test_tuning.py | 81 + tests/test_jobs.py | 234 -- tests/test_registers.py | 82 - tests/test_scripts.py | 33 +- tests/test_services.py | 69 - tests/{ => utils}/test_searchers.py | 19 +- tests/utils/test_signers.py | 20 + tests/{ => utils}/test_splitters.py | 13 +- 70 files changed, 5633 insertions(+), 1585 deletions(-) create mode 100644 confs/promotion.yaml create mode 100644 poetry.lock create mode 100644 src/bikes/core/__init__.py rename src/bikes/{ => core}/metrics.py (76%) rename src/bikes/{ => core}/models.py (80%) create mode 100644 src/bikes/core/schemas.py create mode 100644 src/bikes/io/__init__.py rename src/bikes/{ => io}/configs.py (81%) rename src/bikes/{ => io}/datasets.py (83%) create mode 100644 src/bikes/io/registries.py create mode 100644 src/bikes/io/services.py delete mode 100644 src/bikes/jobs.py create mode 100644 src/bikes/jobs/__init__.py create mode 100644 src/bikes/jobs/base.py create mode 100644 src/bikes/jobs/inference.py create mode 100644 src/bikes/jobs/promotion.py create mode 100644 src/bikes/jobs/training.py create mode 100644 src/bikes/jobs/tuning.py delete mode 100644 src/bikes/registers.py delete mode 100644 src/bikes/schemas.py delete mode 100644 src/bikes/services.py create mode 100644 src/bikes/settings.py create mode 100644 src/bikes/utils/__init__.py rename src/bikes/{ => utils}/searchers.py (66%) create mode 100644 src/bikes/utils/signers.py rename src/bikes/{ => utils}/splitters.py (74%) delete mode 100644 tasks/carbons.py create mode 100644 tasks/mlflow.py rename tests/confs/invalid/{0. invalid.yaml => 1. invalid.yaml} (100%) delete mode 100644 tests/confs/valid/3. inference.yaml create mode 100644 tests/confs/valid/3. promotion.yaml create mode 100644 tests/confs/valid/4. inference.yaml rename tests/{ => core}/test_metrics.py (83%) rename tests/{ => core}/test_models.py (61%) rename tests/{ => core}/test_schemas.py (84%) rename tests/{ => io}/test_configs.py (82%) rename tests/{ => io}/test_datasets.py (92%) create mode 100644 tests/io/test_registries.py create mode 100644 tests/io/test_services.py create mode 100644 tests/jobs/test_base.py create mode 100644 tests/jobs/test_inference.py create mode 100644 tests/jobs/test_promotion.py create mode 100644 tests/jobs/test_training.py create mode 100644 tests/jobs/test_tuning.py delete mode 100644 tests/test_jobs.py delete mode 100644 tests/test_registers.py delete mode 100644 tests/test_services.py rename tests/{ => utils}/test_searchers.py (52%) create mode 100644 tests/utils/test_signers.py rename tests/{ => utils}/test_splitters.py (85%) diff --git a/.gitignore b/.gitignore index 006fd5a..0abc593 100644 --- a/.gitignore +++ b/.gitignore @@ -19,10 +19,10 @@ # Environs .env /.venv/ -poetry.lock # Project /docs/* +/mlruns/* /outputs/* !**/.gitkeep diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 21cf643..4f1d104 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -4,7 +4,6 @@ default_language_version: python: python3.12 repos: - # commons - repo: https://github.com/pre-commit/pre-commit-hooks rev: v4.5.0 hooks: @@ -17,22 +16,13 @@ repos: - id: end-of-file-fixer - id: mixed-line-ending - id: trailing-whitespace - - repo: github.com/PyCQA/bandit - rev: v1.7.7 - hooks: - - id: bandit - args: ["--configfile=pyproject.toml"] - - repo: https://github.com/pre-commit/mirrors-mypy - rev: v1.8.0 - hooks: - - id: mypy - repo: https://github.com/astral-sh/ruff-pre-commit - rev: v0.3.0 + rev: v0.3.3 hooks: - id: ruff - id: ruff-format - repo: https://github.com/commitizen-tools/commitizen - rev: v3.16.0 + rev: v3.18.3 hooks: - id: commitizen - id: commitizen-branch diff --git a/confs/promotion.yaml b/confs/promotion.yaml new file mode 100644 index 0000000..0f5feec --- /dev/null +++ b/confs/promotion.yaml @@ -0,0 +1,2 @@ +job: + KIND: PromotionJob diff --git a/confs/tuning.yaml b/confs/tuning.yaml index 82f6bf9..920a3ec 100644 --- a/confs/tuning.yaml +++ b/confs/tuning.yaml @@ -6,6 +6,3 @@ job: targets: KIND: ParquetReader path: data/targets.parquet - results: - KIND: ParquetWriter - path: outputs/results.parquet diff --git a/mlops-python-package.code-workspace b/mlops-python-package.code-workspace index fb7912c..a8fa8fb 100644 --- a/mlops-python-package.code-workspace +++ b/mlops-python-package.code-workspace @@ -24,8 +24,7 @@ "dchanco.vsc-invoke", "ms-python.mypy-type-checker", "ms-python.python", - "ms-python.mypy-type-checker", "ms-python.vscode-pylance", ] } -} \ No newline at end of file +} diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..c11c8b2 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,3532 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by 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"jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] + +[metadata] +lock-version = "2.0" +python-versions = "^3.12" +content-hash = "4b39bf561448e2bb387cf5f247830e24cb7b0bb3132d92d6a1499cd26667935f" diff --git a/pyproject.toml b/pyproject.toml index 72f5e79..3433665 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,10 +4,10 @@ [tool.poetry] name = "bikes" -version = "0.6.0" +version = "0.7.0" description = "Predict the number of bikes available." -documentation = "https://fmind.github.io/mlops-python-package/" repository = "https://github.com/fmind/mlops-python-package" +documentation = "https://fmind.github.io/mlops-python-package/" authors = ["Médéric HURIER "] readme = "README.md" license = "CC BY" @@ -23,52 +23,47 @@ bikes = 'bikes.scripts:main' [tool.poetry.dependencies] python = "^3.12" -codecarbon = "^2.3.4" loguru = "^0.7.2" -mlflow-skinny = "^2.11.0" +matplotlib = "^3.8.3" +mlflow = "^2.11.0" +numpy = "^1.26.4" omegaconf = "^2.3.0" pandas = "^2.2.1" -pandera = "^0.18.0" +pandera = "^0.18.3" plotly = "^5.19.0" -pyarrow = "^15.0.0" -pydantic = "^2.6.3" +pyarrow = "^15.0.1" +pydantic = "^2.6.4" pydantic-settings = "^2.2.1" scikit-learn = "^1.4.1" -mlflow = "^2.11.0" - -[tool.poetry.group.dev.dependencies] -invoke = "^2.2.0" - -[tool.poetry.group.docs.dependencies] -pdoc = "^14.4.0" [tool.poetry.group.checks.dependencies] -bandit = "^1.7.7" +bandit = "^1.7.8" coverage = "^7.4.3" -mypy = "^1.8.0" -pytest = "^8.0.2" +mypy = "^1.9.0" +pytest = "^8.1.1" pytest-cov = "^4.1.0" pytest-xdist = "^3.5.0" -pandera = { extras = ["mypy"], version = "^0.18.0" } -ruff = "^0.3.0" +pandera = { extras = ["mypy"], version = "^0.18.3" } +ruff = "^0.3.3" [tool.poetry.group.commits.dependencies] -commitizen = "^3.16.0" +commitizen = "^3.18.3" pre-commit = "^3.6.2" -[tool.poetry.group.carbons.dependencies] -dash = "^2.16.0" -dash-bootstrap-components = "^1.5.0" -fire = "^0.5.0" +[tool.poetry.group.dev.dependencies] +invoke = "^2.2.0" + +[tool.poetry.group.docs.dependencies] +pdoc = "^14.4.0" [tool.poetry.group.notebooks.dependencies] ipykernel = "^6.29.3" -nbformat = "^5.9.2" +nbformat = "^5.10.2" # CONFIGURATIONS [tool.bandit] -skips = ["B101"] +targets = ["src"] [tool.commitizen] name = "cz_conventional_commits" @@ -76,7 +71,7 @@ tag_format = "v$version" version_scheme = "pep440" version_provider = "poetry" changelog_start_rev = "v1.0.0" -update_changelog_on_bump = true +update_changelog_on_bump = false major_version_zero = true [tool.coverage.run] @@ -92,6 +87,9 @@ check_untyped_defs = true ignore_missing_imports = true plugins = ["pandera.mypy", "pydantic.mypy"] +[tool.pytest.ini_options] +addopts = "--verbosity=2" + [tool.ruff] fix = true line-length = 100 @@ -100,9 +98,6 @@ target-version = "py312" [tool.ruff.format] docstring-code-format = true -[tool.ruff.lint] -select = ["D"] - [tool.ruff.lint.pydocstyle] convention = "google" diff --git a/src/bikes/core/__init__.py b/src/bikes/core/__init__.py new file mode 100644 index 0000000..442b6d7 --- /dev/null +++ b/src/bikes/core/__init__.py @@ -0,0 +1 @@ +"""Core components of the project.""" diff --git a/src/bikes/metrics.py b/src/bikes/core/metrics.py similarity index 76% rename from src/bikes/metrics.py rename to src/bikes/core/metrics.py index 318c836..6eaf13c 100644 --- a/src/bikes/metrics.py +++ b/src/bikes/core/metrics.py @@ -1,4 +1,4 @@ -"""Evaluate model performance with metrics.""" +"""Evaluate model performances with metrics.""" # %% IMPORTS @@ -8,19 +8,19 @@ import pydantic as pdt from sklearn import metrics -from bikes import models, schemas +from bikes.core import models, schemas # %% METRICS -class Metric(abc.ABC, pdt.BaseModel, strict=True): - """Base class for a metric. +class Metric(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): + """Base class for a project metric. Use metrics to evaluate model performance. e.g., accuracy, precision, recall, MAE, F1, ... - Attributes: - name (str): name of the metric. + Parameters: + name (str): name of the metric for the reporting. """ KIND: str @@ -36,13 +36,13 @@ def score(self, targets: schemas.Targets, outputs: schemas.Outputs) -> float: outputs (schemas.Outputs): predicted values. Returns: - float: single result for the metric computation. + float: single result from the metric computation. """ def scorer( self, model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets ) -> float: - """Score the model outputs against the targets. + """Score the model outputs against targets. Args: model (models.Model): model to evaluate. @@ -50,9 +50,9 @@ def scorer( targets (schemas.Targets): model expected values. Returns: - float: metric result. + float: single result from the metric computation. """ - outputs = model.predict(inputs=inputs) # prediction + outputs = model.predict(inputs=inputs) score = self.score(targets=targets, outputs=outputs) return score @@ -60,7 +60,7 @@ def scorer( class SklearnMetric(Metric): """Compute metrics with sklearn. - Attributes: + Parameters: name (str): name of the sklearn metric. greater_is_better (bool): maximize or minimize. """ diff --git a/src/bikes/models.py b/src/bikes/core/models.py similarity index 80% rename from src/bikes/models.py rename to src/bikes/core/models.py index 078b55c..462fe10 100644 --- a/src/bikes/models.py +++ b/src/bikes/core/models.py @@ -8,10 +8,11 @@ import pydantic as pdt from sklearn import compose, ensemble, pipeline, preprocessing -from bikes import schemas +from bikes.core import schemas # %% TYPES +# Model params ParamKey = str ParamValue = T.Any Params = dict[ParamKey, ParamValue] @@ -19,8 +20,8 @@ # %% MODELS -class Model(abc.ABC, pdt.BaseModel, strict=True): - """Base class for a model. +class Model(abc.ABC, pdt.BaseModel, strict=True, frozen=False, extra="forbid"): + """Base class for a project model. Use a model to adapt AI/ML frameworks. e.g., to swap easily one model with another. @@ -32,7 +33,7 @@ def get_params(self, deep: bool = True) -> Params: """Get the model params. Args: - deep (bool, optional): ignored. Defaults to True. + deep (bool, optional): ignored. Returns: Params: internal model parameters. @@ -62,7 +63,7 @@ def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self: targets (schemas.Targets): model training targets. Returns: - Model: instance of the model. + T.Self: instance of the model. """ @abc.abstractmethod @@ -76,11 +77,22 @@ def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: schemas.Outputs: model prediction outputs. """ + def get_internal_model(self) -> T.Any: + """Return the internal model in the object. + + Raises: + NotImplementedError: method not implemented. + + Returns: + T.Any: any internal model (either empty or fitted). + """ + raise NotImplementedError() + class BaselineSklearnModel(Model): - """Simple baseline model with sklearn. + """Simple baseline model based on scikit-learn. - Attributes: + Parameters: max_depth (int): maximum depth of the random forest. n_estimators (int): number of estimators in the random forest. random_state (int, optional): random state of the machine learning pipeline. @@ -142,12 +154,19 @@ def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> "BaselineSkle @T.override def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: - assert self._pipeline is not None, "Model should be fitted first!" - prediction = self._pipeline.predict(inputs) # return an np.ndarray + model = self.get_internal_model() + prediction = model.predict(inputs) outputs = schemas.Outputs( {schemas.OutputsSchema.prediction: prediction}, index=inputs.index ) return outputs + @T.override + def get_internal_model(self) -> pipeline.Pipeline: + model = self._pipeline + if model is None: + raise ValueError("Model is not fitted yet!") + return model + ModelKind = BaselineSklearnModel diff --git a/src/bikes/core/schemas.py b/src/bikes/core/schemas.py new file mode 100644 index 0000000..8971304 --- /dev/null +++ b/src/bikes/core/schemas.py @@ -0,0 +1,91 @@ +"""Define and validate dataframe schemas.""" + +# %% IMPORTS + +import typing as T + +import pandas as pd +import pandera as pa +import pandera.typing as papd +import pandera.typing.common as padt + +# %% TYPES + +TSchema = T.TypeVar("TSchema", bound="pa.DataFrameModel") + +# %% SCHEMAS + + +class Schema(pa.DataFrameModel): + """Base class for a dataframe schema. + + Use a schema to type your dataframe object. + e.g., to communicate and validate its fields. + """ + + class Config: + """Default configurations for all schemas. + + Parameters: + coerce (bool): convert data type if possible. + strict (bool): ensure the data type is correct. + """ + + coerce: bool = True + strict: bool = True + + @classmethod + def check(cls: T.Type[TSchema], data: pd.DataFrame) -> papd.DataFrame[TSchema]: + """Check the dataframe with this schema. + + Args: + data (pd.DataFrame): dataframe to check. + + Returns: + papd.DataFrame[TSchema]: validated dataframe. + """ + return T.cast(papd.DataFrame[TSchema], cls.validate(data)) + + +class InputsSchema(Schema): + """Schema for the project inputs.""" + + instant: papd.Index[padt.UInt32] = pa.Field(ge=0, check_name=True) + dteday: papd.Series[padt.DateTime] = pa.Field() + season: papd.Series[padt.UInt8] = pa.Field(isin=[1, 2, 3, 4]) + yr: papd.Series[padt.UInt8] = pa.Field(ge=0, le=1) + mnth: papd.Series[padt.UInt8] = pa.Field(ge=1, le=12) + hr: papd.Series[padt.UInt8] = pa.Field(ge=0, le=23) + holiday: papd.Series[padt.Bool] = pa.Field() + weekday: papd.Series[padt.UInt8] = pa.Field(ge=0, le=6) + workingday: papd.Series[padt.Bool] = pa.Field() + weathersit: papd.Series[padt.UInt8] = pa.Field(ge=1, le=4) + temp: papd.Series[padt.Float16] = pa.Field(ge=0, le=1) + atemp: papd.Series[padt.Float16] = pa.Field(ge=0, le=1) + hum: papd.Series[padt.Float16] = pa.Field(ge=0, le=1) + windspeed: papd.Series[padt.Float16] = pa.Field(ge=0, le=1) + casual: papd.Series[padt.UInt32] = pa.Field(ge=0) + registered: papd.Series[padt.UInt32] = pa.Field(ge=0) + + +Inputs = papd.DataFrame[InputsSchema] + + +class TargetsSchema(Schema): + """Schema for the project target.""" + + instant: papd.Index[padt.UInt32] = pa.Field(ge=0, check_name=True) + cnt: papd.Series[padt.UInt32] = pa.Field(ge=0) + + +Targets = papd.DataFrame[TargetsSchema] + + +class OutputsSchema(Schema): + """Schema for the project output.""" + + instant: papd.Index[padt.UInt32] = pa.Field(ge=0, check_name=True) + prediction: papd.Series[padt.UInt32] = pa.Field(ge=0) + + +Outputs = papd.DataFrame[OutputsSchema] diff --git a/src/bikes/io/__init__.py b/src/bikes/io/__init__.py new file mode 100644 index 0000000..2a8a67d --- /dev/null +++ b/src/bikes/io/__init__.py @@ -0,0 +1 @@ +"""Components related to external operations.""" diff --git a/src/bikes/configs.py b/src/bikes/io/configs.py similarity index 81% rename from src/bikes/configs.py rename to src/bikes/io/configs.py index 2d36200..5e2c1b4 100644 --- a/src/bikes/configs.py +++ b/src/bikes/io/configs.py @@ -4,11 +4,11 @@ import typing as T -from omegaconf import DictConfig, ListConfig, OmegaConf +import omegaconf as oc # %% TYPES -Config = ListConfig | DictConfig +Config = oc.ListConfig | oc.DictConfig # %% PARSERS @@ -22,7 +22,7 @@ def parse_file(path: str) -> Config: Returns: Config: representation of the config file. """ - return OmegaConf.load(path) + return oc.OmegaConf.load(path) def parse_string(string: str) -> Config: @@ -34,7 +34,7 @@ def parse_string(string: str) -> Config: Returns: Config: representation of the config string. """ - return OmegaConf.create(string) + return oc.OmegaConf.create(string) # %% MERGERS @@ -49,7 +49,7 @@ def merge_configs(configs: T.Sequence[Config]) -> Config: Returns: Config: representation of the merged config objects. """ - return OmegaConf.merge(*configs) + return oc.OmegaConf.merge(*configs) # %% CONVERTERS @@ -65,4 +65,4 @@ def to_object(config: Config, resolve: bool = True) -> object: Returns: object: conversion of the config to a python object. """ - return OmegaConf.to_container(config, resolve=resolve) + return oc.OmegaConf.to_container(config, resolve=resolve) diff --git a/src/bikes/datasets.py b/src/bikes/io/datasets.py similarity index 83% rename from src/bikes/datasets.py rename to src/bikes/io/datasets.py index 095216c..4d9aeb4 100644 --- a/src/bikes/datasets.py +++ b/src/bikes/io/datasets.py @@ -11,14 +11,14 @@ # %% READERS -class Reader(abc.ABC, pdt.BaseModel, strict=True): +class Reader(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): """Base class for a dataset reader. Use a reader to load a dataset in memory. e.g., to read file, database, cloud storage, ... - Attributes: - limit (int, optional): maximum number of rows to read from dataset. + Parameters: + limit (int, optional): maximum number of rows to read. Defaults to None. """ KIND: str @@ -37,7 +37,7 @@ def read(self) -> pd.DataFrame: class ParquetReader(Reader): """Read a dataframe from a parquet file. - Attributes: + Parameters: path (str): local path to the dataset. """ @@ -59,7 +59,7 @@ def read(self) -> pd.DataFrame: # %% WRITERS -class Writer(abc.ABC, pdt.BaseModel, strict=True): +class Writer(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): """Base class for a dataset writer. Use a writer to save a dataset from memory. @@ -80,8 +80,8 @@ def write(self, data: pd.DataFrame) -> None: class ParquetWriter(Writer): """Writer a dataframe to a parquet file. - Attributes: - path (str): local path to the dataset. + Parameters: + path (str): local or S3 path to the dataset. """ KIND: T.Literal["ParquetWriter"] = "ParquetWriter" diff --git a/src/bikes/io/registries.py b/src/bikes/io/registries.py new file mode 100644 index 0000000..06d686f --- /dev/null +++ b/src/bikes/io/registries.py @@ -0,0 +1,314 @@ +"""Savers, loaders, and registers for model registries.""" + +# %% IMPORTS + +import abc +import typing as T + +import mlflow +import pydantic as pdt + +from bikes.core import models, schemas +from bikes.utils import signers + +# %% TYPES + +# Results of model registry operations +Info: T.TypeAlias = mlflow.models.model.ModelInfo +Alias: T.TypeAlias = mlflow.entities.model_registry.ModelVersion +Version: T.TypeAlias = mlflow.entities.model_registry.ModelVersion + +# %% HELPERS + + +def uri_for_model_alias(name: str, alias: str) -> str: + """Create a model URI from a model name and an alias. + + Args: + name (str): name of the mlflow registered model. + alias (str): alias of the registered model. + + Returns: + str: model URI as "models:/name@alias". + """ + return f"models:/{name}@{alias}" + + +def uri_for_model_version(name: str, version: str) -> str: + """Create a model URI from a model name and a version. + + Args: + name (str): name of the mlflow registered model. + version (str): version of the registered model. + + Returns: + str: model URI as "models:/name/version." + """ + return f"models:/{name}/{version}" + + +# %% SAVERS + + +class Saver(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): + """Base class for saving models in registry. + + Separate model definition from serialization. + e.g., to switch between serialization flavors. + + Parameters: + path (str): model path inside the Mlflow store. + """ + + KIND: str + + path: str = "model" + + @abc.abstractmethod + def save( + self, model: models.Model, signature: signers.Signature, input_example: schemas.Inputs + ) -> Info: + """Save a model in the model registry. + + Args: + model (models.Model): project model to save. + signature (signers.Signature): model signature. + input_example (schemas.Inputs): sample of inputs. + + Returns: + Info: model saving information. + """ + + +class CustomSaver(Saver): + """Saver for project models using the Mlflow PyFunc module. + + https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html + """ + + KIND: T.Literal["CustomSaver"] = "CustomSaver" + + class Adapter(mlflow.pyfunc.PythonModel): # type: ignore[misc] + """Adapt a custom model to the Mlflow PyFunc flavor for saving operations. + + https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html?#mlflow.pyfunc.PythonModel + """ + + def __init__(self, model: models.Model): + """Initialize the custom saver adapter. + + Args: + model (models.Model): project model. + """ + self.model = model + + def predict( + self, + context: mlflow.pyfunc.PythonModelContext, + model_input: schemas.Inputs, + params: dict[str, T.Any] | None = None, + ) -> schemas.Outputs: + """Generate predictions with a custom model for the given inputs. + + Args: + context (mlflow.pyfunc.PythonModelContext): mlflow context. + model_input (schemas.Inputs): inputs for the mlflow model. + params(dict[str, T.Any] | None): additional parameters. + + Returns: + schemas.Outputs: validated outputs of the project model. + """ + return self.model.predict(inputs=model_input) + + @T.override + def save( + self, model: models.Model, signature: signers.Signature, input_example: schemas.Inputs + ) -> Info: + adapter = CustomSaver.Adapter(model=model) + return mlflow.pyfunc.log_model( + python_model=adapter, + signature=signature, + artifact_path=self.path, + input_example=input_example, + ) + + +class BuiltinSaver(Saver): + """Saver for built-in models using an Mlflow flavor module. + + https://mlflow.org/docs/latest/models.html#built-in-model-flavors + + Parameters: + flavor (str): Mlflow flavor module to use for the serialization. + """ + + KIND: T.Literal["BuiltinSaver"] = "BuiltinSaver" + + flavor: str + + @T.override + def save( + self, + model: models.Model, + signature: signers.Signature, + input_example: schemas.Inputs | None = None, + ) -> mlflow.entities.model_registry.ModelVersion: + builtin_model = model.get_internal_model() + module = getattr(mlflow, self.flavor) + return module.log_model( + builtin_model, artifact_path=self.path, signature=signature, input_example=input_example + ) + + +SaverKind = CustomSaver | BuiltinSaver + +# %% LOADERS + + +class Loader(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): + """Base class for loading models from registry. + + Separate model definition from deserialization. + e.g., to switch between deserialization flavors. + """ + + KIND: str + + class Adapter(abc.ABC): + """Adapt any model for the project inference.""" + + @abc.abstractmethod + def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: + """Generate predictions with the internal model for the given inputs. + + Args: + inputs (schemas.Inputs): validated inputs for the project model. + + Returns: + schemas.Outputs: validated outputs of the project model. + """ + + @abc.abstractmethod + def load(self, uri: str) -> "Loader.Adapter": + """Load a model from the model registry. + + Args: + uri (str): URI of a model to load. + + Returns: + Loader.Adapter: model loaded. + """ + + +class CustomLoader(Loader): + """Loader for custom models using the Mlflow PyFunc module. + + https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html + """ + + KIND: T.Literal["CustomLoader"] = "CustomLoader" + + class Adapter(Loader.Adapter): + """Adapt a custom model for the project inference.""" + + def __init__(self, model: mlflow.pyfunc.PyFuncModel) -> None: + """Initialize the adapter from an mlflow pyfunc model. + + Args: + model (mlflow.pyfunc.PyFuncModel): mlflow pyfunc model. + """ + self.model = model + + @T.override + def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: + # model validation is already done in predict + outputs = self.model.predict(data=inputs) + return T.cast(schemas.Outputs, outputs) + + @T.override + def load(self, uri: str) -> "CustomLoader.Adapter": + model = mlflow.pyfunc.load_model(model_uri=uri) + adapter = CustomLoader.Adapter(model=model) + return adapter + + +class BuiltinLoader(Loader): + """Loader for built-in models using the Mlflow PyFunc module. + + Note: use Mlflow PyFunc instead of flavors to use standard API. + + https://mlflow.org/docs/latest/models.html#built-in-model-flavors + """ + + KIND: T.Literal["BuiltinLoader"] = "BuiltinLoader" + + class Adapter(Loader.Adapter): + """Adapt a builtin model for the project inference.""" + + def __init__(self, model: mlflow.pyfunc.PyFuncModel) -> None: + """Initialize the adapter from an mlflow pyfunc model. + + Args: + model (mlflow.pyfunc.PyFuncModel): mlflow pyfunc model. + """ + self.model = model + + @T.override + def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: + columns = list(schemas.OutputsSchema.to_schema().columns) + outputs = self.model.predict(data=inputs) # unchecked data! + return schemas.Outputs(outputs, columns=columns, index=inputs.index) + + @T.override + def load(self, uri: str) -> "BuiltinLoader.Adapter": + model = mlflow.pyfunc.load_model(model_uri=uri) + adapter = BuiltinLoader.Adapter(model=model) + return adapter + + +LoaderKind = CustomLoader | BuiltinLoader + +# %% REGISTERS + + +class Register(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): + """Base class for registring models to a location. + + Separate model definition from its registration. + e.g., to change the model registry backend. + + Parameters: + tags (dict[str, T.Any]): tags for the model. + """ + + KIND: str + + tags: dict[str, T.Any] = {} + + @abc.abstractmethod + def register(self, name: str, model_uri: str) -> Version: + """Register a model given its name and URI. + + Args: + name (str): name of the model to register. + model_uri (str): URI of a model to register. + + Returns: + Version: information about the registered model. + """ + + +class MlflowRegister(Register): + """Register for models in the Mlflow Model Registry. + + https://mlflow.org/docs/latest/model-registry.html + """ + + KIND: T.Literal["MlflowRegister"] = "MlflowRegister" + + @T.override + def register(self, name: str, model_uri: str) -> Version: + return mlflow.register_model(name=name, model_uri=model_uri, tags=self.tags) + + +RegisterKind = MlflowRegister diff --git a/src/bikes/io/services.py b/src/bikes/io/services.py new file mode 100644 index 0000000..d8657ae --- /dev/null +++ b/src/bikes/io/services.py @@ -0,0 +1,176 @@ +"""Manage global context during execution.""" + +# %% IMPORTS + +from __future__ import annotations + +import abc +import contextlib as ctx +import sys +import typing as T + +import loguru +import mlflow +import mlflow.tracking as mt +import pydantic as pdt + +# %% SERVICES + + +class Service(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): + """Base class for a global service. + + Use services to manage global contexts. + e.g., logger object, mlflow client, spark context, ... + """ + + @abc.abstractmethod + def start(self) -> None: + """Start the service.""" + + def stop(self) -> None: + """Stop the service.""" + # does nothing by default + + +class LoggerService(Service): + """Service for logging messages. + + https://loguru.readthedocs.io/en/stable/api/logger.html + + Parameters: + sink (str): logging output. + level (str): logging level. + format (str): logging format. + colorize (bool): colorize output. + serialize (bool): convert to JSON. + backtrace (bool): enable exception trace. + diagnose (bool): enable variable display. + catch (bool): catch errors during log handling. + """ + + sink: str = "stderr" + level: str = "DEBUG" + format: str = ( + "[{time:YYYY-MM-DD HH:mm:ss.SSS}]" + "[{level}]" + "[{name}:{function}:{line}]" + " {message}" + ) + colorize: bool = True + serialize: bool = False + backtrace: bool = True + diagnose: bool = False + catch: bool = True + + @T.override + def start(self) -> None: + loguru.logger.remove() + config = self.model_dump() + # use standard sinks or keep the original + sinks = {"stderr": sys.stderr, "stdout": sys.stdout} + config["sink"] = sinks.get(config["sink"], config["sink"]) + loguru.logger.add(**config) + + def logger(self) -> loguru.Logger: + """Return the main logger. + + Returns: + loguru.Logger: the main logger. + """ + return loguru.logger + + +class MlflowService(Service): + """Service for Mlflow tracking and registry. + + Parameters: + tracking_uri (str): the URI for the Mlflow tracking server. + registry_uri (str): the URI for the Mlflow model registry. + experiment_name (str): the name of tracking experiment. + registry_name (str): the name of model registry. + autolog_disable (bool): disable autologging. + autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions. + autolog_exclusive (bool): If True, enables exclusive autologging. + autolog_log_input_examples (bool): If True, logs input examples during autologging. + autolog_log_model_signatures (bool): If True, logs model signatures during autologging. + autolog_log_models (bool): If True, enables logging of models during autologging. + autolog_log_datasets (bool): If True, logs datasets used during autologging. + autolog_silent (bool): If True, suppresses all Mlflow warnings during autologging. + """ + + class RunConfig(pdt.BaseModel, strict=True, frozen=True, extra="forbid"): + """Run configuration for Mlflow tracking. + + Parameters: + name (str): name of the run. + description (str | None): description of the run. + tags (dict[str, T.Any] | None): tags for the run. + log_system_metrics (bool | None): enable system metrics logging. + """ + + name: str + description: str | None = None + tags: dict[str, T.Any] | None = None + log_system_metrics: bool | None = None + + # server uri + tracking_uri: str = "./mlruns" + registry_uri: str = "./mlruns" + # experiment + experiment_name: str = "bikes" + # registry + registry_name: str = "bikes" + # autologg + autolog_disable: bool = False + autolog_disable_for_unsupported_versions: bool = False + autolog_exclusive: bool = False + autolog_log_input_examples: bool = True + autolog_log_model_signatures: bool = True + autolog_log_models: bool = False + autolog_log_datasets: bool = False + autolog_silent: bool = False + + @T.override + def start(self) -> None: + # server uri + mlflow.set_tracking_uri(uri=self.tracking_uri) + mlflow.set_registry_uri(uri=self.registry_uri) + # experiment + mlflow.set_experiment(experiment_name=self.experiment_name) + # autologging + mlflow.autolog( + disable=self.autolog_disable, + disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions, + exclusive=self.autolog_exclusive, + log_input_examples=self.autolog_log_input_examples, + log_model_signatures=self.autolog_log_model_signatures, + log_models=self.autolog_log_models, + silent=self.autolog_silent, + ) + + @ctx.contextmanager + def run_context(self, run_config: RunConfig) -> T.Generator[mlflow.ActiveRun, None, None]: + """Yield an active Mlflow run and exit it afterwards. + + Args: + run (str): run parameters. + + Yields: + T.Generator[mlflow.ActiveRun, None, None]: active run context. Will be closed as the end of context. + """ + with mlflow.start_run( + run_name=run_config.name, + tags=run_config.tags, + description=run_config.description, + log_system_metrics=run_config.log_system_metrics, + ) as run: + yield run + + def client(self) -> mt.MlflowClient: + """Return a new Mlflow client. + + Returns: + MlflowClient: the mlflow client. + """ + return mt.MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri) diff --git a/src/bikes/jobs.py b/src/bikes/jobs.py deleted file mode 100644 index c8b5bfc..0000000 --- a/src/bikes/jobs.py +++ /dev/null @@ -1,304 +0,0 @@ -"""High-level jobs for the project.""" - -# %% IMPORTS - -import abc -import typing as T - -import mlflow -import pydantic as pdt -from loguru import logger - -from bikes import datasets, metrics, models, registers, schemas, searchers, services, splitters - -# %% TYPES - -# local job variables -Locals = dict[str, T.Any] - -# %% JOBS - - -class Job(abc.ABC, pdt.BaseModel, strict=True): - """Base class for a job. - - use a job to execute runs in context. - e.g., to define common services like logger - - Attributes: - logger_service (services.LoggerService): manage the logging system. - carbon_service (services.CarbonService): manage the carbon system. - mlflow_service (services.MLflowService): manage the mlflow system. - """ - - KIND: str - - logger_service: services.LoggerService = services.LoggerService() - carbon_service: services.CarbonService = services.CarbonService() - mlflow_service: services.MLflowService = services.MLflowService() - - def __enter__(self) -> T.Self: - """Enter the job context. - - Returns: - T.Self: return the current object. - """ - self.logger_service.start() # start then log - logger.debug("[START] Logger service: {}", self.logger_service) - logger.debug("[START] Carbon service: {}", self.carbon_service) - self.carbon_service.start() - logger.debug("[START] MLflow service: {}", self.mlflow_service) - self.mlflow_service.start() - return self - - def __exit__(self, exc_type, exc_value, traceback) -> T.Literal[False]: - """Exit the job context. - - Args: - exc_type: ignored. - exc_value: ignored. - traceback: ignored. - - Returns: - T.Literal[False]: always propagate exceptions. - """ - logger.debug("[STOP] MLflow service: {}", self.mlflow_service) - self.mlflow_service.stop() - logger.debug("[STOP] Carbon service: {}", self.carbon_service) - self.carbon_service.stop() - logger.debug("[STOP] Logger service: {}", self.carbon_service) - self.logger_service.stop() - return False - - @abc.abstractmethod - def run(self) -> Locals: - """Run the job in context. - - Returns: - Locals: local job variables. - """ - - -class TuningJob(Job): - """Find the best hyperparameters for a model. - - Attributes: - run_name (str): name of the MLflow experiment run. - inputs (datasets.ReaderKind): dataset reader with inputs variables. - targets (datasets.ReaderKind): dataset reader with targets variables. - results (datasets.WriterKind): dataset writer for searcher results. - model (models.ModelKind): machine learning model to tune. - metric (metrics.MetricKind): main metric for evaluation. - splitter (splitters.SplitterKind): splitter for datasets. - searcher (searchers.SearcherKind): searcher algorithm. - """ - - KIND: T.Literal["TuningJob"] = "TuningJob" - - # run - run_name: str = "Tuning" - # read - inputs: datasets.ReaderKind - targets: datasets.ReaderKind - # write - results: datasets.WriterKind - # model - model: models.ModelKind = models.BaselineSklearnModel() - # metric - metric: metrics.MetricKind = metrics.SklearnMetric() - # splitter - splitter: splitters.SplitterKind = splitters.TimeSeriesSplitter() - # searcher - searcher: searchers.SearcherKind = searchers.GridCVSearcher( - param_grid={"max_depth": [3, 5, 7]}, - ) - - @T.override - def run(self) -> Locals: - # run - logger.info("Start run: {} ", self.run_name) - with mlflow.start_run(run_name=self.run_name) as run: - logger.info("- Run ID: {}", run.info.run_id) - # read - # - inputs - logger.info("Read inputs: {}", self.inputs) - inputs = schemas.InputsSchema.check(self.inputs.read()) - logger.info("- Inputs shape: {}", inputs.shape) - # - targets - logger.info("Read targets: {}", self.targets) - targets = schemas.TargetsSchema.check(self.targets.read()) - logger.info("- Targets shape: {}", targets.shape) - # - asserts - assert len(inputs) == len(targets), "Inputs and targets should have the same length!" - # model - logger.info("With model: {}", self.model) - # metric - logger.info("With metric: {}", self.metric) - # splitter - logger.info("With splitter: {}", self.splitter) - # searcher - logger.info("Execute searcher: {}", self.searcher) - results, best_score, best_params = self.searcher.search( - model=self.model, - metric=self.metric, - cv=self.splitter, - inputs=inputs, - targets=targets, - ) - logger.info("- # Results: {}", len(results)) - logger.info("- Best Score: {}", best_score) - logger.info("- Best Params: {}", best_params) - # write - logger.info("Write results: {}", self.results) - self.results.write(results) - return locals() - - -class TrainingJob(Job): - """Train and register a single AI/ML model. - - Attributes: - run_name (str): name of the MLflow experiment run. - inputs (datasets.ReaderKind): dataset reader with inputs variables. - targets (datasets.ReaderKind): dataset reader with targets variables. - saver (registers.SaverKind): save the trained model in registry. - model (models.ModelKind): machine learning model to tune. - signer (registers.SignerKind): signer for the trained model. - scorers (list[metrics.MetricKind]): metrics for the evaluation. - splitter (splitters.SplitterKind): splitter for datasets. - registry_alias (str): alias of model. - """ - - KIND: T.Literal["TrainingJob"] = "TrainingJob" - - # run - run_name: str = "Training" - # read - inputs: datasets.ReaderKind - targets: datasets.ReaderKind - # write - saver: registers.SaverKind = registers.CustomSaver() - # model - model: models.ModelKind = models.BaselineSklearnModel() - # signer - signer: registers.SignerKind = registers.InferSigner() - # scorers - scorers: list[metrics.MetricKind] = [metrics.SklearnMetric()] - # splitter - splitter: splitters.SplitterKind = splitters.TrainTestSplitter() - # register - registry_alias: str = "Champion" - - @T.override - def run(self) -> Locals: - # run - logger.info("Start run: {} ", self.run_name) - with mlflow.start_run(run_name=self.run_name) as run: - logger.info("- Run ID: {}", run.info.run_id) - # read - # - inputs - logger.info("Read inputs: {}", self.inputs) - inputs = schemas.InputsSchema.check(self.inputs.read()) - logger.info("- Inputs shape: {}", inputs.shape) - # - targets - logger.info("Read targets: {}", self.targets) - targets = schemas.TargetsSchema.check(self.targets.read()) - logger.info("- Targets shape: {}", targets.shape) - # - asserts - assert len(inputs) == len(targets), "Inputs and targets should have the same length!" - # split - logger.info("With splitter: {}", self.splitter) - # - index - train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets)) - # - inputs - inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index] - logger.info("- Inputs train shape: {}", inputs_train.shape) - logger.info("- Inputs test shape: {}", inputs_test.shape) - # - targets - targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index] - logger.info("- Targets train shape: {}", targets_train.shape) - logger.info("- Targets test shape: {}", targets_test.shape) - # - asserts - assert len(inputs_train) == len( - targets_train - ), "Inputs and targets train should have the same length!" - assert len(inputs_test) == len( - targets_test - ), "Inputs and targets test should have the same length!" - # model - logger.info("Fit model: {}", self.model) - self.model.fit(inputs=inputs_train, targets=targets_train) - # outputs - logger.info("Predict outputs: {}", len(inputs_test)) - outputs_test = self.model.predict(inputs=inputs_test) - logger.info("- Outputs test shape: {}", outputs_test.shape) - assert len(inputs_test) == len( - outputs_test - ), "Inputs and outputs test should have the same length!" - # scorers - for i, scorer in enumerate(self.scorers, start=1): - logger.info("{}. Run scorer: {}", i, scorer) - score = scorer.score(targets=targets_test, outputs=outputs_test) - mlflow.log_metric(key=scorer.name, value=score) - logger.info("- Metric score: {}", score) - # sign - logger.info("Sign model: {}", self.signer) - signature = self.signer.sign(inputs=inputs, outputs=outputs_test) - logger.info("- Model signature: {}", signature.to_dict()) - # save - logger.info("Save model: {}", self.saver) - info = self.saver.save(model=self.model, signature=signature, input_example=inputs) - logger.info("- Model URI: {}", info.model_uri) - # register - logger.info("Register model: {}", self.registry_alias) - version = self.mlflow_service.register( - run_id=run.info.run_id, path=self.saver.path, alias=self.registry_alias - ) - logger.info("- Model version: {}", version.version) - return locals() - - -class InferenceJob(Job): - """Load a model and generate predictions. - - Attributes: - inputs (datasets.ReaderKind): dataset reader with inputs variables. - outputs (datasets.WriterKind): dataset writer for the model outputs. - registry_alias (str): alias of the model to load. - loader (registers.LoaderKind): load the model from registry. - """ - - KIND: T.Literal["InferenceJob"] = "InferenceJob" - - # data - inputs: datasets.ReaderKind - outputs: datasets.WriterKind - # model - registry_alias: str = "Champion" - loader: registers.LoaderKind = registers.CustomLoader() - - @T.override - def run(self) -> Locals: - # read - logger.info("Read inputs: {}", self.inputs) - inputs = self.inputs.read() - inputs = schemas.InputsSchema.check(inputs) - logger.info("- Inputs shape: {}", inputs.shape) - # uri - uri = f"models:/{self.mlflow_service.registry_name}@{self.registry_alias}" - logger.info("With URI: {}", uri) - # load - logger.info("Load model: {}", self.loader) - model = self.loader.load(uri=uri) - logger.info("- Model: {}", model) - # predict - logger.info("Predict outputs: {}", len(inputs)) - outputs = model.predict(data=inputs) - logger.info("- Outputs shape: {}", outputs.shape) - # write - logger.info("Write outputs: {}", self.outputs) - self.outputs.write(data=outputs) - return locals() - - -JobKind = TuningJob | TrainingJob | InferenceJob diff --git a/src/bikes/jobs/__init__.py b/src/bikes/jobs/__init__.py new file mode 100644 index 0000000..dd76e7d --- /dev/null +++ b/src/bikes/jobs/__init__.py @@ -0,0 +1,16 @@ +"""High-level jobs of the project.""" + +# %% IMPORTS + +from bikes.jobs.inference import InferenceJob +from bikes.jobs.promotion import PromotionJob +from bikes.jobs.training import TrainingJob +from bikes.jobs.tuning import TuningJob + +# %% TYPES + +JobKind = TuningJob | TrainingJob | PromotionJob | InferenceJob + +# %% EXPORTS + +__all__ = ["TuningJob", "TrainingJob", "PromotionJob", "InferenceJob", "JobKind"] diff --git a/src/bikes/jobs/base.py b/src/bikes/jobs/base.py new file mode 100644 index 0000000..7e7406c --- /dev/null +++ b/src/bikes/jobs/base.py @@ -0,0 +1,79 @@ +"""Base for high-level project jobs.""" + +# %% IMPORTS + +import abc +import types as TS +import typing as T + +import pydantic as pdt + +from bikes.io import services + +# %% TYPES + +# Local job variables +Locals = T.Dict[str, T.Any] + +# %% JOBS + + +class Job(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): + """Base class for a job. + + use a job to execute runs in context. + e.g., to define common services like logger + + Parameters: + logger_service (services.LoggerService): manage the logging system. + mlflow_service (services.MlflowService): manage the mlflow system. + """ + + KIND: str + + logger_service: services.LoggerService = services.LoggerService() + mlflow_service: services.MlflowService = services.MlflowService() + + def __enter__(self) -> T.Self: + """Enter the job context. + + Returns: + T.Self: return the current object. + """ + self.logger_service.start() + logger = self.logger_service.logger() + logger.debug("[START] Logger service: {}", self.logger_service) + logger.debug("[START] Mlflow service: {}", self.mlflow_service) + self.mlflow_service.start() + return self + + def __exit__( + self, + exc_type: T.Type[BaseException] | None, + exc_value: BaseException | None, + exc_traceback: TS.TracebackType | None, + ) -> T.Literal[False]: + """Exit the job context. + + Args: + exc_type (T.Type[BaseException] | None): ignored. + exc_value (BaseException | None): ignored. + exc_traceback (TS.TracebackType | None): ignored. + + Returns: + T.Literal[False]: always propagate exceptions. + """ + logger = self.logger_service.logger() + logger.debug("[STOP] Mlflow service: {}", self.mlflow_service) + self.mlflow_service.stop() + logger.debug("[STOP] Logger service: {}", self.logger_service) + self.logger_service.stop() + return False # re-raise + + @abc.abstractmethod + def run(self) -> Locals: + """Run the job in context. + + Returns: + Locals: local job variables. + """ diff --git a/src/bikes/jobs/inference.py b/src/bikes/jobs/inference.py new file mode 100644 index 0000000..2f7cc83 --- /dev/null +++ b/src/bikes/jobs/inference.py @@ -0,0 +1,64 @@ +"""Define a job for generating batch predictions from a registered model.""" + +# %% IMPORTS + +import typing as T + +import pydantic as pdt + +from bikes.core import schemas +from bikes.io import datasets, registries +from bikes.jobs import base + +# %% JOBS + + +class InferenceJob(base.Job): + """Generate batch predictions from a registered model. + + Parameters: + inputs (datasets.ReaderKind): reader for the inputs data. + outputs (datasets.WriterKind ): writer for the outputs data. + alias (str): alias tag for the model. Defaults to "Champion". + loader (registries.LoaderKind): loader system for the model. + """ + + KIND: T.Literal["InferenceJob"] = "InferenceJob" + + # Inputs + inputs: datasets.ReaderKind = pdt.Field(..., discriminator="KIND") + # Outputs + outputs: datasets.WriterKind = pdt.Field(..., discriminator="KIND") + # Model + alias: str = "Champion" + # Loader + loader: registries.LoaderKind = pdt.Field(registries.CustomLoader(), discriminator="KIND") + + @T.override + def run(self) -> base.Locals: + # services + logger = self.logger_service.logger() + logger.info("With logger: {}", logger) + # inputs + logger.info("Read inputs: {}", self.inputs) + inputs_ = self.inputs.read() # unchecked! + inputs = schemas.InputsSchema.check(inputs_) + logger.debug("- Inputs shape: {}", inputs.shape) + # model + logger.info("With model: {}", self.mlflow_service.registry_name) + model_uri = registries.uri_for_model_alias( + name=self.mlflow_service.registry_name, alias=self.alias + ) + logger.debug("- Model URI: {}", model_uri) + # loader + logger.info("Load model: {}", self.loader) + model = self.loader.load(uri=model_uri) + logger.debug("- Model: {}", model) + # outputs + logger.info("Predict outputs: {}", len(inputs)) + outputs = model.predict(inputs=inputs) # checked + logger.debug("- Outputs shape: {}", outputs.shape) + # write + logger.info("Write outputs: {}", self.outputs) + self.outputs.write(outputs) + return locals() diff --git a/src/bikes/jobs/promotion.py b/src/bikes/jobs/promotion.py new file mode 100644 index 0000000..85f4891 --- /dev/null +++ b/src/bikes/jobs/promotion.py @@ -0,0 +1,52 @@ +"""Define a job for promoting a registered model version with an alias.""" + +# %% IMPORTS + +import typing as T + +from bikes.jobs import base + +# %% JOBS + + +class PromotionJob(base.Job): + """Define a job for promoting a registered model version with an alias. + + https://mlflow.org/docs/latest/model-registry.html#concepts + + Parameters: + alias (str): the mlflow alias to transition the registered model version. + version (int | None): the model version to transition (use None for latest). + """ + + KIND: T.Literal["PromotionJob"] = "PromotionJob" + + alias: str = "Champion" + version: int | None = None + + @T.override + def run(self) -> base.Locals: + # services + # - logger + logger = self.logger_service.logger() + logger.info("With logger: {}", logger) + # - mlflow + client = self.mlflow_service.client() + name = self.mlflow_service.registry_name + logger.info("With mlflow model name: {}", name) + # version + if self.version is None: # use the latest model version + version = client.search_model_versions( + f"name='{name}'", max_results=1, order_by=["version_number DESC"] + )[0].version + else: + version = self.version + logger.info("From version: {}", version) + # alias + logger.info("To alias: {}", self.alias) + # promote + logger.info("Promote model: {}", name) + client.set_registered_model_alias(name=name, alias=self.alias, version=version) + model_version = client.get_model_version_by_alias(name=name, alias=self.alias) + logger.debug("- Model version: {}", model_version) + return locals() diff --git a/src/bikes/jobs/training.py b/src/bikes/jobs/training.py new file mode 100644 index 0000000..b245787 --- /dev/null +++ b/src/bikes/jobs/training.py @@ -0,0 +1,120 @@ +"""Define a job for training and registring a single AI/ML model.""" + +# %% IMPORTS + +import typing as T + +import pydantic as pdt + +from bikes.core import metrics as metrics_ +from bikes.core import models, schemas +from bikes.io import datasets, registries, services +from bikes.jobs import base +from bikes.utils import signers, splitters + +# %% JOBS + + +class TrainingJob(base.Job): + """Train and register a single AI/ML model. + + Parameters: + run_config (services.MlflowService.RunConfig): mlflow run config. + inputs (datasets.ReaderKind): reader for the inputs data. + targets (datasets.ReaderKind): reader for the targets data. + model (models.ModelKind): machine learning model to train. + metrics (metrics_.MetricKind): metrics for the reporting. + splitter (splitters.SplitterKind): data sets splitter. + saver (registries.SaverKind): model saver. + signer (signers.SignerKind): model signer. + registry (registries.RegisterKind): model register. + """ + + KIND: T.Literal["TrainingJob"] = "TrainingJob" + + # Run + run_config: services.MlflowService.RunConfig = services.MlflowService.RunConfig(name="Training") + # Data + inputs: datasets.ReaderKind = pdt.Field(..., discriminator="KIND") + targets: datasets.ReaderKind = pdt.Field(..., discriminator="KIND") + # Model + model: models.ModelKind = pdt.Field(models.BaselineSklearnModel(), discriminator="KIND") + # Metrics + metrics: list[metrics_.MetricKind] = pdt.Field([metrics_.SklearnMetric()], discriminator="KIND") + # Splitter + splitter: splitters.SplitterKind = pdt.Field( + splitters.TrainTestSplitter(), discriminator="KIND" + ) + # Saver + saver: registries.SaverKind = pdt.Field(registries.CustomSaver(), discriminator="KIND") + # Signer + signer: signers.SignerKind = pdt.Field(signers.InferSigner(), discriminator="KIND") + # Registrer + # - avoid shadowing pydantic `register` pydantic function + registry: registries.RegisterKind = pdt.Field(registries.MlflowRegister(), discriminator="KIND") + + @T.override + def run(self) -> base.Locals: + # services + # - logger + logger = self.logger_service.logger() + logger.info("With logger: {}", logger) + # - mlflow + client = self.mlflow_service.client() + with self.mlflow_service.run_context(run_config=self.run_config) as run: + logger.info("With mlflow run id: {}", run.info.run_id) + # data + # - inputs + logger.info("Read inputs: {}", self.inputs) + inputs_ = self.inputs.read() # unchecked! + inputs = schemas.InputsSchema.check(inputs_) + logger.debug("- Inputs shape: {}", inputs.shape) + # - targets + logger.info("Read targets: {}", self.targets) + targets_ = self.targets.read() # unchecked! + targets = schemas.TargetsSchema.check(targets_) + logger.debug("- Targets shape: {}", targets.shape) + # splitter + logger.info("With splitter: {}", self.splitter) + # - index + train_index, test_index = next(self.splitter.split(inputs=inputs, targets=targets)) + # - inputs + inputs_train = T.cast(schemas.Inputs, inputs.iloc[train_index]) + inputs_test = T.cast(schemas.Inputs, inputs.iloc[test_index]) + logger.debug("- Inputs train shape: {}", inputs_train.shape) + logger.debug("- Inputs test shape: {}", inputs_test.shape) + # - targets + targets_train = T.cast(schemas.Targets, targets.iloc[train_index]) + targets_test = T.cast(schemas.Targets, targets.iloc[test_index]) + logger.debug("- Targets train shape: {}", targets_train.shape) + logger.debug("- Targets test shape: {}", targets_test.shape) + # model + logger.info("Fit model: {}", self.model) + self.model.fit(inputs=inputs_train, targets=targets_train) + # outputs + logger.info("Predict outputs: {}", len(inputs_test)) + outputs_test = self.model.predict(inputs=inputs_test) + logger.debug("- Outputs test shape: {}", outputs_test.shape) + # metrics + for i, metric in enumerate(self.metrics, start=1): + logger.info("{}. Compute metric: {}", i, metric) + score = metric.score(targets=targets_test, outputs=outputs_test) + client.log_metric(run_id=run.info.run_id, key=metric.name, value=score) + logger.debug("- Metric score: {}", score) + # signer + logger.info("Sign model: {}", self.signer) + model_signature = self.signer.sign(inputs=inputs, outputs=outputs_test) + logger.debug("- Model signature: {}", model_signature.to_dict()) + # saver + logger.info("Save model: {}", self.saver) + model_info = self.saver.save( + model=self.model, signature=model_signature, input_example=inputs + ) + logger.debug("- Model URI: {}", model_info.model_uri) + # register + logger.info("Register model: {}", self.registry) + model_version = self.registry.register( + name=self.mlflow_service.registry_name, model_uri=model_info.model_uri + ) + logger.debug("- Model version: {}", model_version) + return locals() diff --git a/src/bikes/jobs/tuning.py b/src/bikes/jobs/tuning.py new file mode 100644 index 0000000..a5a3091 --- /dev/null +++ b/src/bikes/jobs/tuning.py @@ -0,0 +1,94 @@ +"""Define a job for finding the best hyperparameters for a model.""" + +# %% IMPORTS + +import typing as T + +import pydantic as pdt + +from bikes.core import metrics, models, schemas +from bikes.io import datasets, services +from bikes.jobs import base +from bikes.utils import searchers, splitters + +# %% JOBS + + +class TuningJob(base.Job): + """Find the best hyperparameters for a model. + + Parameters: + run_config (services.MlflowService.RunConfig): mlflow run config. + inputs (datasets.ReaderKind): reader for the inputs data. + targets (datasets.ReaderKind): reader for the targets data. + model (models.ModelKind): machine learning model to tune. + metric (metrics.MetricKind): tuning metric to optimize. + splitter (splitters.SplitterKind): data sets splitter. + searcher: (searchers.SearcherKind): hparams searcher. + """ + + KIND: T.Literal["TuningJob"] = "TuningJob" + + # Run + run_config: services.MlflowService.RunConfig = services.MlflowService.RunConfig(name="Tuning") + # Data + inputs: datasets.ReaderKind = pdt.Field(..., discriminator="KIND") + targets: datasets.ReaderKind = pdt.Field(..., discriminator="KIND") + # Model + model: models.ModelKind = pdt.Field(models.BaselineSklearnModel(), discriminator="KIND") + # Metric + metric: metrics.MetricKind = pdt.Field(metrics.SklearnMetric(), discriminator="KIND") + # splitter + splitter: splitters.SplitterKind = pdt.Field( + splitters.TimeSeriesSplitter(), discriminator="KIND" + ) + # Searcher + searcher: searchers.SearcherKind = pdt.Field( + searchers.GridCVSearcher( + param_grid={ + "max_depth": [3, 5, 7], + } + ), + discriminator="KIND", + ) + + @T.override + def run(self) -> base.Locals: + """Run the tuning job in context.""" + # services + # - logger + logger = self.logger_service.logger() + logger.info("With logger: {}", logger) + # - mlflow + with self.mlflow_service.run_context(run_config=self.run_config) as run: + logger.info("With mlflow run id: {}", run.info.run_id) + # data + # - inputs + logger.info("Read inputs: {}", self.inputs) + inputs_ = self.inputs.read() # unchecked! + inputs = schemas.InputsSchema.check(inputs_) + logger.debug("- Inputs shape: {}", inputs.shape) + # - targets + logger.info("Read targets: {}", self.targets) + targets_ = self.targets.read() # unchecked! + targets = schemas.TargetsSchema.check(targets_) + logger.debug("- Targets shape: {}", targets.shape) + # model + logger.info("With model: {}", self.model) + # metric + logger.info("With metric: {}", self.metric) + # splitter + logger.info("With splitter: {}", self.splitter) + # searcher + logger.info("Run searcher: {}", self.searcher) + results, best_score, best_params = self.searcher.search( + model=self.model, + metric=self.metric, + inputs=inputs, + targets=targets, + cv=self.splitter, + ) + logger.debug("- Results: {}", results.shape) + logger.debug("- Best Score: {}", best_score) + logger.debug("- Best Params: {}", best_params) + return locals() diff --git a/src/bikes/registers.py b/src/bikes/registers.py deleted file mode 100644 index cb7674e..0000000 --- a/src/bikes/registers.py +++ /dev/null @@ -1,185 +0,0 @@ -"""Adapters, signers, savers, and loaders for model registries.""" - -# %% IMPORTS - -import abc -import typing as T - -import mlflow -import pydantic as pdt - -from bikes import models, schemas - -# %% TYPES - -Info: T.TypeAlias = mlflow.models.model.ModelInfo -Version: T.TypeAlias = mlflow.entities.model_registry.ModelVersion -Signature: T.TypeAlias = mlflow.models.ModelSignature -CustomModel: T.TypeAlias = mlflow.pyfunc.PythonModel - -# %% ADAPTERS - - -class CustomAdapter(mlflow.pyfunc.PythonModel): - """Adapt a custom model to the MLflow PyFunc flavor. - - https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html - """ - - def __init__(self, model: models.Model): - """Initialize the custom adapter. - - Args: - model (models.Model): project model. - """ - self.model = model - - def predict( - self, context: mlflow.pyfunc.PythonModelContext, inputs: schemas.Inputs - ) -> schemas.Outputs: - """Generate predictions from a custom model. - - Args: - context (mlflow.pyfunc.PythonModelContext): ignored. - inputs (schemas.Inputs): inputs for the model. - - Returns: - schemas.Outputs: outputs of the model. - """ - return self.model.predict(inputs=inputs) - - -# %% SIGNERS - - -class Signer(abc.ABC, pdt.BaseModel, strict=True): - """Base class for making signatures. - - Allow to switch between signing approaches. - e.g., automatic inference vs manual signatures - https://mlflow.org/docs/latest/models.html#model-signature-and-input-example - """ - - KIND: str - - @abc.abstractmethod - def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature: - """Make a model signature from inputs/outputs. - - Args: - inputs (schemas.Inputs): inputs of the model. - outputs (schemas.Outputs): ouputs of the model. - - Returns: - ModelSignature: generated signature for the model. - """ - - -class InferSigner(Signer): - """Generate model signatures from data inference.""" - - KIND: T.Literal["InferModelSigner"] = "InferModelSigner" - - @T.override - def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature: - return mlflow.models.infer_signature(model_input=inputs, model_output=outputs) - - -SignerKind = InferSigner - - -# %% SAVERS - - -class Saver(abc.ABC, pdt.BaseModel, strict=True): - """Base class for saving models in registry. - - Separate model definition from serialization. - e.g., to switch between serialization flavors. - - Attributes: - path (str): model path inside the MLflow artifact store. - """ - - KIND: str - - path: str = "model" - - @abc.abstractmethod - def save( - self, model: models.Model, signature: Signature, input_example: schemas.Inputs - ) -> Info: - """Save a model in the model registry. - - Args: - model (models.Model): model to save. - signature (Signature): model signature. - input_example (schemas.Inputs): inputs sample. - - Returns: - Info: model saving information. - """ - - -class CustomSaver(Saver): - """Saver for custom models using the MLflow PyFunc module. - - https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html - """ - - KIND: T.Literal["CustomSaver"] = "CustomSaver" - - def save( - self, model: models.Model, signature: Signature, input_example: schemas.Inputs - ) -> Info: - """Save a custom model to the MLflow Model Registry.""" - custom = CustomAdapter(model=model) # adapt model - return mlflow.pyfunc.log_model( - artifact_path=self.path, - python_model=custom, - signature=signature, - input_example=input_example, - ) - - -SaverKind = CustomSaver - - -# %% LOADERS - - -class Loader(abc.ABC, pdt.BaseModel, strict=True): - """Base class for loading models from registry. - - Separate model definition from deserialization. - e.g., to switch between deserialization flavors. - """ - - KIND: str - - @abc.abstractmethod - def load(self, uri: str) -> T.Any: - """Load a model from the model registry. - - Args: - uri (str): URI of the model to load. - - Returns: - T.Any: model loaded from registry. - """ - - -class CustomLoader(Loader): - """Loader for custom models using the MLflow PyFunc module. - - https://mlflow.org/docs/latest/python_api/mlflow.pyfunc.html - """ - - KIND: T.Literal["CustomLoader"] = "CustomLoader" - - @T.override - def load(self, uri: str) -> CustomModel: - return mlflow.pyfunc.load_model(model_uri=uri) - - -LoaderKind = CustomLoader diff --git a/src/bikes/schemas.py b/src/bikes/schemas.py deleted file mode 100644 index 54283ea..0000000 --- a/src/bikes/schemas.py +++ /dev/null @@ -1,87 +0,0 @@ -"""Define and validate dataframe schemas.""" - -# %% IMPORTS - -import typing as T - -import pandas as pd -import pandera as pa -import pandera.typing as papd - -# %% SCHEMAS - - -class Schema(pa.DataFrameModel): - """Base class for a dataframe schema. - - Use a schema to type your dataframe object. - e.g., to communicate and validate its fields. - """ - - class Config: - """Default configuration. - - Attributes: - coerce (bool): convert data type if possible. - strict (bool): ensure the data type is correct. - """ - - coerce: bool = True - strict: bool = True - - @classmethod - def check(cls, data: pd.DataFrame, **kwargs: T.Any) -> T.Any: - """Check the dataframe with this schema. - - Args: - data (pd.DataFrame): dataframe to check. - kwargs: additional arguments to validate(). - - Returns: - pd.DataFrame: validated dataframe with schema. - """ - return cls.validate(data, **kwargs) - - -class InputsSchema(Schema): - """Schema for the project inputs.""" - - instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True) - dteday: papd.Series[papd.DateTime] = pa.Field() - season: papd.Series[papd.UInt8] = pa.Field(isin=[1, 2, 3, 4]) - yr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=1) - mnth: papd.Series[papd.UInt8] = pa.Field(ge=1, le=12) - hr: papd.Series[papd.UInt8] = pa.Field(ge=0, le=23) - holiday: papd.Series[papd.Bool] = pa.Field() - weekday: papd.Series[papd.UInt8] = pa.Field(ge=0, le=6) - workingday: papd.Series[papd.Bool] = pa.Field() - weathersit: papd.Series[papd.UInt8] = pa.Field(ge=1, le=4) - temp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1) - atemp: papd.Series[papd.Float16] = pa.Field(ge=0, le=1) - hum: papd.Series[papd.Float16] = pa.Field(ge=0, le=1) - windspeed: papd.Series[papd.Float16] = pa.Field(ge=0, le=1) - casual: papd.Series[papd.UInt32] = pa.Field(ge=0) - registered: papd.Series[papd.UInt32] = pa.Field(ge=0) - - -Inputs = papd.DataFrame[InputsSchema] - - -class TargetsSchema(Schema): - """Schema for the project target.""" - - instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True) - cnt: papd.Series[papd.UInt32] = pa.Field(ge=0) - - -Targets = papd.DataFrame[TargetsSchema] - - -class OutputsSchema(Schema): - """Schema for the project output.""" - - instant: papd.Index[papd.UInt32] = pa.Field(ge=0, check_name=True) - prediction: papd.Series[papd.UInt32] = pa.Field(ge=0) - - -Outputs = papd.DataFrame[OutputsSchema] diff --git a/src/bikes/scripts.py b/src/bikes/scripts.py index 0858d82..f391805 100644 --- a/src/bikes/scripts.py +++ b/src/bikes/scripts.py @@ -1,57 +1,36 @@ -"""Command-line interface for the program.""" +"""Scripts for the CLI application.""" # %% IMPORTS import argparse import json +import sys -import pydantic as pdt -import pydantic_settings as pdts - -from bikes import configs, jobs - -# %% SETTINGS - - -class Settings(pdts.BaseSettings, strict=True): - """Settings for the program. - - Attributes: - job (jobs.JobKind): job associated with settings. - """ - - job: jobs.JobKind = pdt.Field(..., discriminator="KIND") - +from bikes import settings +from bikes.io import configs # %% PARSERS -parser = argparse.ArgumentParser(prog="bikes", description="Run an ML job from configs.") -parser.add_argument("configs", nargs="+", help="Config files for the job (local or remote).") -parser.add_argument("-e", "--extras", nargs="+", default=[], help="Config strings for the job.") +parser = argparse.ArgumentParser(description="Run an AI/ML job fron YAML/JSON configs.") +parser.add_argument("files", nargs="*", help="Config files for the job (local path only).") +parser.add_argument("-e", "--extras", nargs="*", default=[], help="Config strings for the job.") parser.add_argument("-s", "--schema", action="store_true", help="Print settings schema and exit.") # %% SCRIPTS def main(argv: list[str] | None = None) -> int: - """Main function of the program. - - Args: - argv (list[str] | None, optional): program arguments. Defaults to None for sys.argv. - - Returns: - int: status code of the program. - """ + """Main script for the application.""" args = parser.parse_args(argv) - if args.schema is True: - schema = Settings.model_json_schema() - print(json.dumps(schema, indent=2)) - return 0 # success - files = map(configs.parse_file, args.configs) + if args.schema: + schema = settings.MainSettings.model_json_schema() + json.dump(schema, sys.stdout, indent=4) + return 0 + files = map(configs.parse_file, args.files) strings = map(configs.parse_string, args.extras) config = configs.merge_configs([*files, *strings]) - object_ = configs.to_object(config) # convert to dict - settings = Settings.model_validate(object_) # to pydantic - with settings.job as runner: - runner.run() # execute job - return 0 # success + object_ = configs.to_object(config) # python object + setting = settings.MainSettings.model_validate(object_) + with setting.job as runner: + runner.run() + return 0 diff --git a/src/bikes/services.py b/src/bikes/services.py deleted file mode 100644 index aa477f8..0000000 --- a/src/bikes/services.py +++ /dev/null @@ -1,212 +0,0 @@ -"""Manage global context during execution.""" - -# %% IMPORTS - -import abc -import os -import sys -import typing as T - -import codecarbon as cc -import mlflow -import pydantic as pdt -from loguru import logger -from mlflow.tracking import MlflowClient - -# %% SERVICES - - -class Service(abc.ABC, pdt.BaseModel, strict=True): - """Base class for a global service. - - Use services to manage global contexts. - e.g., logger object, mlflow client, spark context, ... - """ - - @abc.abstractmethod - def start(self) -> None: - """Start the service.""" - - def stop(self) -> None: - """Stop the service.""" - # does nothing by default - - -class LoggerService(Service): - """Service for logging messages. - - https://loguru.readthedocs.io/en/stable/api/logger.html - - Attributes: - sink (str): logging output. - level (str): logging level. - format (str): logging format. - colorize (bool): colorize output. - serialize (bool): convert to JSON. - backtrace (bool): enable exception trace. - diagnose (bool): enable variable display. - catch (bool): catch errors during log handling. - """ - - sink: str = "stderr" - level: str = "INFO" - format: str = ( - "[{time:YYYY-MM-DD HH:mm:ss.SSS}]" - "[{level}]" - "[{name}:{function}:{line}]" - " {message}" - ) - colorize: bool = True - serialize: bool = False - backtrace: bool = True - diagnose: bool = False - catch: bool = True - - @T.override - def start(self) -> None: - # sinks - sinks = { - "stderr": sys.stderr, - "stdout": sys.stdout, - } - # cleanup - logger.remove() - # convert - config = self.model_dump() - # replace - # - use standard sinks or keep the original - config["sink"] = sinks.get(config["sink"], config["sink"]) - # config - logger.add(**config) - - -class CarbonService(Service): - """Service for tracking carbon emissions. - - https://mlco2.github.io/codecarbon/parameters.html - - Attributes: - log_level (str): Level of logging to output. - project_name (str): Name of the project to track. - measure_power_secs (int): Interval for measuring in secs. - output_dir (str): Directory where the output files are stored. - output_file (str): Name of the output CSV file for emissions data. - on_csv_write (str): Specifies the action on writing to CSV (append or overwrite). - country_iso_code (str): ISO code of the country for tracking carbon emissions offline. - """ - - # public - # - inputs - log_level: str = "ERROR" - project_name: str = "bikes" - tracking_mode: str = "machine" - measure_power_secs: int = 5 - # - outputs - output_dir: str = "outputs" - output_file: str = "emissions.csv" - on_csv_write: str = "append" - # - offline - country_iso_code: str = "LUX" - # private - _tracker: cc.OfflineEmissionsTracker | None = None - - @T.override - def start(self) -> None: - """Start the carbon service.""" - params = self.model_dump() - os.makedirs(self.output_dir, exist_ok=True) - self._tracker = cc.OfflineEmissionsTracker(**params) - self._tracker.start() - - def stop(self) -> None: - """Stop the carbon service.""" - assert self._tracker, "Carbon tracker should be started!" - self._tracker.flush() - self._tracker.stop() - - -class MLflowService(Service): - """Service for MLflow tracking and registry. - - Attributes: - autolog_disable (bool): disable autologging. - autolog_disable_for_unsupported_versions (bool): disable autologging for unsupported versions. - autolog_exclusive (bool): If True, enables exclusive autologging. - autolog_log_input_examples (bool): If True, logs input examples during autologging. - autolog_log_model_signatures (bool): If True, logs model signatures during autologging. - autolog_log_models (bool): If True, enables logging of models during autologging. - autolog_log_datasets (bool): If True, logs datasets used during autologging. - autolog_silent (bool): If True, suppresses all MLflow warnings during autologging. - enable_system_metrics (bool): enable system metrics logging. - tracking_uri (str): The URI for the MLflow tracking server. - experiment_name (str): The name of the experiment to log runs under. - registry_uri (str): The URI for the MLflow model registry. - registry_name (str): The name of the registry. - """ - - # autolog - autolog_disable: bool = False - autolog_disable_for_unsupported_versions: bool = False - autolog_exclusive: bool = False - autolog_log_input_examples: bool = True - autolog_log_model_signatures: bool = True - autolog_log_models: bool = False - autolog_log_datasets: bool = True - autolog_silent: bool = False - # system - enable_system_metrics: bool = True - # tracking - # tracking_uri: str = "http://localhost:5000" - tracking_uri: str = "./mlruns" - experiment_name: str = "bikes" - # registry - # registry_uri: str = "http://localhost:5000" - registry_uri: str = "./mlruns" - registry_name: str = "bikes" - - @T.override - def start(self) -> None: - """Start the mlflow service.""" - # uri - mlflow.set_tracking_uri(uri=self.tracking_uri) - mlflow.set_registry_uri(uri=self.registry_uri) - # experiment - mlflow.set_experiment(experiment_name=self.experiment_name) - # autologging - mlflow.autolog( - disable=self.autolog_disable, - disable_for_unsupported_versions=self.autolog_disable_for_unsupported_versions, - exclusive=self.autolog_exclusive, - log_input_examples=self.autolog_log_input_examples, - log_model_signatures=self.autolog_log_model_signatures, - log_models=self.autolog_log_models, - silent=self.autolog_silent, - ) - # system metrics - if self.enable_system_metrics: - mlflow.enable_system_metrics_logging() - - def client(self) -> MlflowClient: - """Get an instance of MLflow client.""" - return MlflowClient(tracking_uri=self.tracking_uri, registry_uri=self.registry_uri) - - def register( - self, run_id: str, path: str, alias: str - ) -> mlflow.entities.model_registry.ModelVersion: - """Register a model to mlflow registry. - - Args: - run_id (str): id of mlflow run. - path (str): path of artifact. - alias (str): model alias. - - Returns: - mlflow.entities.model_registry.ModelVersion: registered version. - """ - client = self.client() - model_uri = f"runs:/{run_id}/{path}" - version = mlflow.register_model(model_uri=model_uri, name=self.registry_name) - client.set_registered_model_alias( - name=self.registry_name, alias=alias, version=version.version - ) - return version diff --git a/src/bikes/settings.py b/src/bikes/settings.py new file mode 100644 index 0000000..aeeb2d2 --- /dev/null +++ b/src/bikes/settings.py @@ -0,0 +1,28 @@ +"""Define settings of the application.""" + +# %% IMPORTS + +import pydantic as pdt +import pydantic_settings as pdts + +from bikes import jobs + +# %% SETTINGS + + +class Settings(pdts.BaseSettings, strict=True, frozen=True, extra="forbid"): + """Base class for application settings. + + Use settings to provide high-level preferences. + i.e., to separate settings from provider (e.g., CLI). + """ + + +class MainSettings(Settings): + """Main settings of the application. + + Parameters: + job (jobs.JobKind): job to run. + """ + + job: jobs.JobKind = pdt.Field(..., discriminator="KIND") diff --git a/src/bikes/utils/__init__.py b/src/bikes/utils/__init__.py new file mode 100644 index 0000000..99ef613 --- /dev/null +++ b/src/bikes/utils/__init__.py @@ -0,0 +1 @@ +"""Helper components of the project.""" diff --git a/src/bikes/searchers.py b/src/bikes/utils/searchers.py similarity index 66% rename from src/bikes/searchers.py rename to src/bikes/utils/searchers.py index 19f3e70..7ca7a34 100644 --- a/src/bikes/searchers.py +++ b/src/bikes/utils/searchers.py @@ -9,74 +9,81 @@ import pydantic as pdt from sklearn import model_selection -from bikes import metrics, models, schemas, splitters +from bikes.core import metrics, models, schemas +from bikes.utils import splitters # %% TYPES # Grid of model params -# {param name -> param values} -Grid = dict[str, list[T.Any]] +Grid = dict[models.ParamKey, list[models.ParamValue]] -# results of a model search -# (results, best score, best params) -Results = tuple[pd.DataFrame, float, models.Params] +# Results of a model search +Results = tuple[ + T.Annotated[pd.DataFrame, "details"], + T.Annotated[float, "best score"], + T.Annotated[models.Params, "best params"], +] -# cross-validation options for searchers -CrossValidation = int | splitters.Splits | splitters.Splitter +# Cross-validation options for searchers +CrossValidation = int | splitters.TrainTestSplits | splitters.Splitter # %% SEARCHERS -class Searcher(abc.ABC, pdt.BaseModel, strict=True): +class Searcher(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): """Base class for a searcher. - note: use searcher to tune models. - e.g., to find the best model params. + Use searcher to fine-tune models. + i.e., to find the best model params. + + Parameters: + param_grid (Grid): mapping of param key -> values. """ KIND: str + param_grid: Grid + @abc.abstractmethod def search( self, model: models.Model, metric: metrics.Metric, - cv: CrossValidation, inputs: schemas.Inputs, targets: schemas.Targets, + cv: CrossValidation, ) -> Results: """Search the best model for the given inputs and targets. Args: - model (models.Model): machine learning model to tune. + model (models.Model): AI/ML model to fine-tune. metric (metrics.Metric): main metric to optimize. - cv (CrossValidation): structure for cross-fold. inputs (schemas.Inputs): model inputs for tuning. targets (schemas.Targets): model targets for tuning. + cv (CrossValidation): choice for cross-fold validation. Returns: - Results: all the results of the tuning process. + Results: all the results of the searcher execution process. """ class GridCVSearcher(Searcher): - """Grid searcher with cross-folds. + """Grid searcher with cross-fold validation. - Attributes: - param_grid (Grid): mapping of param key -> values. + Convention: metric returns higher values for better models. + + Parameters: n_jobs (int, optional): number of jobs to run in parallel. refit (bool): refit the model after the tuning. - verbose (int): set the search verbosity level. + verbose (int): set the searcher verbosity level. error_score (str | float): strategy or value on error. - return_train_score (bool): include train scores. + return_train_score (bool): include train scores if True. """ KIND: T.Literal["GridCVSearcher"] = "GridCVSearcher" - # public - param_grid: Grid n_jobs: int | None = None - refit: bool = False + refit: bool = True verbose: int = 3 error_score: str | float = "raise" return_train_score: bool = False @@ -86,9 +93,9 @@ def search( self, model: models.Model, metric: metrics.Metric, - cv: CrossValidation, inputs: schemas.Inputs, targets: schemas.Targets, + cv: CrossValidation, ) -> Results: searcher = model_selection.GridSearchCV( estimator=model, diff --git a/src/bikes/utils/signers.py b/src/bikes/utils/signers.py new file mode 100644 index 0000000..b976bb5 --- /dev/null +++ b/src/bikes/utils/signers.py @@ -0,0 +1,55 @@ +"""Generate signatures for AI/ML models.""" + +# %% IMPORTS + +import abc +import typing as T + +import mlflow +import pydantic as pdt +from mlflow.models import signature as ms + +from bikes.core import schemas + +# %% TYPES + +Signature: T.TypeAlias = ms.ModelSignature + +# %% SIGNERS + + +class Signer(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): + """Base class for generating model signatures. + + Allow to switch between model signing strategies. + e.g., automatic inference, manual model signature, ... + + https://mlflow.org/docs/latest/models.html#model-signature-and-input-example + """ + + KIND: str + + @abc.abstractmethod + def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature: + """Generate a model signature from its inputs/outputs. + + Args: + inputs (schemas.Inputs): inputs data. + outputs (schemas.Outputs): outputs data. + + Returns: + Signature: signature of the model. + """ + + +class InferSigner(Signer): + """Generate model signatures from inputs/outputs data.""" + + KIND: T.Literal["InferSigner"] = "InferSigner" + + @T.override + def sign(self, inputs: schemas.Inputs, outputs: schemas.Outputs) -> Signature: + return mlflow.models.infer_signature(model_input=inputs, model_output=outputs) + + +SignerKind = InferSigner diff --git a/src/bikes/splitters.py b/src/bikes/utils/splitters.py similarity index 74% rename from src/bikes/splitters.py rename to src/bikes/utils/splitters.py index b0d82d0..bbc52ed 100644 --- a/src/bikes/splitters.py +++ b/src/bikes/utils/splitters.py @@ -6,56 +6,57 @@ import typing as T import numpy as np +import numpy.typing as npt import pydantic as pdt from sklearn import model_selection -from bikes import schemas +from bikes.core import schemas # %% TYPES -Index = np.ndarray # row index -TrainTest = tuple[Index, Index] -Splits = T.Iterator[TrainTest] +Index = npt.NDArray[np.int64] +TrainTestIndex = tuple[Index, Index] +TrainTestSplits = T.Iterator[TrainTestIndex] # %% SPLITTERS -class Splitter(abc.ABC, pdt.BaseModel, strict=True): +class Splitter(abc.ABC, pdt.BaseModel, strict=True, frozen=True, extra="forbid"): """Base class for a splitter. - Use splitters to split datasets. + Use splitters to split data in sets. e.g., split between a train/test subsets. - """ # https://scikit-learn.org/stable/glossary.html#term-CV-splitter + """ KIND: str @abc.abstractmethod def split( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None - ) -> Splits: + self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None + ) -> TrainTestSplits: """Split a dataframe into subsets. Args: inputs (schemas.Inputs): model inputs. targets (schemas.Targets): model targets. - groups (list | None, optional): group labels. Defaults to None. + groups (Index | None, optional): group labels. Returns: - Splits: iterator over the dataframe splits. + TrainTestSplits: iterator over the dataframe train/test splits. """ @abc.abstractmethod def get_n_splits( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None + self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None ) -> int: """Get the number of splits generated. Args: inputs (schemas.Inputs): models inputs. targets (schemas.Targets): model targets. - groups (list | None, optional): group labels. Defaults to None. + groups (Index | None, optional): group labels. Returns: int: number of splits generated. @@ -63,11 +64,11 @@ def get_n_splits( class TrainTestSplitter(Splitter): - """Split a dataframe into a train and test subsets. + """Split a dataframe into a train and test set. - Attributes: - shuffle (bool): shuffle dataset before splitting. - test_size (int | float): number or ratio for the test dataset. + Parameters: + shuffle (bool): shuffle the dataset. Default is False. + test_size (int | float): number/ratio for the test set. random_state (int): random state for the splitter object. """ @@ -79,8 +80,8 @@ class TrainTestSplitter(Splitter): @T.override def split( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None - ) -> Splits: + self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None + ) -> TrainTestSplits: index = np.arange(len(inputs)) # return integer position train_index, test_index = model_selection.train_test_split( index, shuffle=self.shuffle, test_size=self.test_size, random_state=self.random_state @@ -89,7 +90,7 @@ def split( @T.override def get_n_splits( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None + self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None ) -> int: return 1 @@ -97,7 +98,7 @@ def get_n_splits( class TimeSeriesSplitter(Splitter): """Split a dataframe into fixed time series subsets. - Attributes: + Parameters: gap (int): gap between splits. n_splits (int): number of split to generate. test_size (int | float): number or ratio for the test dataset. @@ -111,14 +112,14 @@ class TimeSeriesSplitter(Splitter): @T.override def split( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None - ) -> Splits: + self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None + ) -> TrainTestSplits: splitter = model_selection.TimeSeriesSplit(n_splits=self.n_splits, test_size=self.test_size) yield from splitter.split(inputs) @T.override def get_n_splits( - self, inputs: schemas.Inputs, targets: schemas.Targets, groups: list | None = None + self, inputs: schemas.Inputs, targets: schemas.Targets, groups: Index | None = None ) -> int: return self.n_splits diff --git a/tasks/__init__.py b/tasks/__init__.py index f5ec5a1..e9359de 100644 --- a/tasks/__init__.py +++ b/tasks/__init__.py @@ -6,7 +6,7 @@ from invoke import Collection -from . import carbons, checks, cleans, commits, containers, dags, docs, formats, installs, packages +from . import checks, cleans, commits, containers, dags, docs, formats, installs, mlflow, packages # %% NAMESPACES @@ -14,7 +14,6 @@ # %% COLLECTIONS -ns.add_collection(carbons) ns.add_collection(checks) ns.add_collection(cleans) ns.add_collection(commits) @@ -23,4 +22,5 @@ ns.add_collection(docs) ns.add_collection(formats) ns.add_collection(installs) +ns.add_collection(mlflow) ns.add_collection(packages) diff --git a/tasks/carbons.py b/tasks/carbons.py deleted file mode 100644 index 82c0684..0000000 --- a/tasks/carbons.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Carbons tasks for pyinvoke.""" - -# %% IMPORTS - -from invoke import task -from invoke.context import Context - -# %% TASKS - - -@task -def board(ctx: Context, filepath: str = "outputs/emissions.csv", port: int = 8050) -> None: - """Visualize carbon emissions data from file path with the carbon board app.""" - ctx.run(f"poetry run carbonboard --filepath={filepath} --port={port}") - - -@task(pre=[board], default=True) -def all(_: Context) -> None: - """Run all carbon tasks.""" diff --git a/tasks/checks.py b/tasks/checks.py index bd78698..dd0c598 100644 --- a/tasks/checks.py +++ b/tasks/checks.py @@ -2,8 +2,8 @@ # %% IMPORTS -from invoke import task from invoke.context import Context +from invoke.tasks import task # %% TASKS @@ -47,7 +47,7 @@ def bandit(ctx: Context) -> None: @task def coverage(ctx: Context) -> None: """Check the coverage with coverage.""" - ctx.run(f"poetry run pytest --numprocesses='auto' --cov=src/ tests/") + ctx.run("poetry run pytest --numprocesses='auto' --cov=src/ --cov-fail-under=80 tests/") @task(pre=[poetry, format, type, code, bandit, coverage], default=True) diff --git a/tasks/cleans.py b/tasks/cleans.py index 0e6f220..02a719a 100644 --- a/tasks/cleans.py +++ b/tasks/cleans.py @@ -2,8 +2,8 @@ # %% IMPORTS -from invoke import task from invoke.context import Context +from invoke.tasks import task # %% TASKS @@ -30,7 +30,7 @@ def pytest(ctx: Context) -> None: @task def coverage(ctx: Context) -> None: - """Clean coverage tool.""" + """Clean the coverage tool.""" ctx.run("rm -f .coverage*") @@ -55,6 +55,12 @@ def cache(ctx: Context) -> None: ctx.run("rm -rf .cache/") +@task +def mlruns(ctx: Context) -> None: + """Clean the mlruns folder.""" + ctx.run("rm -rf mlruns/*") + + @task def outputs(ctx: Context) -> None: """Clean the outputs folder.""" @@ -91,14 +97,14 @@ def tools(_: Context) -> None: """Run all tools tasks.""" -@task(pre=[dist, docs, cache, outputs]) +@task(pre=[dist, docs, cache, mlruns, outputs]) def folders(_: Context) -> None: """Run all folders tasks.""" @task(pre=[venv, poetry, python]) def sources(_: Context) -> None: - """Run all folders tasks.""" + """Run all sources tasks.""" @task(pre=[tools, folders], default=True) diff --git a/tasks/commits.py b/tasks/commits.py index 2ef2400..88b087b 100644 --- a/tasks/commits.py +++ b/tasks/commits.py @@ -2,8 +2,8 @@ # %% IMPORTS -from invoke import task from invoke.context import Context +from invoke.tasks import task # %% TASKS diff --git a/tasks/containers.py b/tasks/containers.py index f1714c8..9e13840 100644 --- a/tasks/containers.py +++ b/tasks/containers.py @@ -2,8 +2,8 @@ # %% IMPORTS -from invoke import task from invoke.context import Context +from invoke.tasks import task from . import packages diff --git a/tasks/dags.py b/tasks/dags.py index 16217e1..83cdbeb 100644 --- a/tasks/dags.py +++ b/tasks/dags.py @@ -2,8 +2,8 @@ # %% IMPORTS -from invoke import call, task from invoke.context import Context +from invoke.tasks import call, task # %% TASKS @@ -16,9 +16,10 @@ def job(ctx: Context, name: str) -> None: @task( pre=[ - call(job, name="tuning"), # type: ignore - call(job, name="training"), # type: ignore - call(job, name="inference"), # type: ignore + call(job, name="tuning"), # type: ignore[arg-type] + call(job, name="training"), # type: ignore[arg-type] + call(job, name="promotion"), # type: ignore[arg-type] + call(job, name="inference"), # type: ignore[arg-type] ], default=True, ) diff --git a/tasks/docs.py b/tasks/docs.py index a3d8da1..d11d831 100644 --- a/tasks/docs.py +++ b/tasks/docs.py @@ -2,8 +2,8 @@ # %% IMPORTS -from invoke import task from invoke.context import Context +from invoke.tasks import task from . import cleans diff --git a/tasks/formats.py b/tasks/formats.py index 71dd031..154593e 100644 --- a/tasks/formats.py +++ b/tasks/formats.py @@ -2,15 +2,15 @@ # %% IMPORTS -from invoke import task from invoke.context import Context +from invoke.tasks import task # %% TASKS @task def code(ctx: Context) -> None: - """Format code with ruff.""" + """Format python code with ruff.""" ctx.run("poetry run ruff format src/ tasks/ tests/") diff --git a/tasks/installs.py b/tasks/installs.py index 1bcce41..05669be 100644 --- a/tasks/installs.py +++ b/tasks/installs.py @@ -2,8 +2,8 @@ # %% IMPORTS -from invoke import task from invoke.context import Context +from invoke.tasks import task # %% TASKS @@ -16,7 +16,7 @@ def poetry(ctx: Context) -> None: @task def pre_commit(ctx: Context) -> None: - """Run pre-commit install.""" + """Install pre-commit hooks on git.""" ctx.run("poetry run pre-commit install --hook-type pre-push") ctx.run("poetry run pre-commit install --hook-type commit-msg") diff --git a/tasks/mlflow.py b/tasks/mlflow.py new file mode 100644 index 0000000..0482ce2 --- /dev/null +++ b/tasks/mlflow.py @@ -0,0 +1,29 @@ +"""Mlflow tasks for pyinvoke.""" + +# %% IMPORTS + +from invoke.context import Context +from invoke.tasks import task + +# %% TASKS + + +@task +def doctor(ctx: Context) -> None: + """Run mlflow doctor to diagnose issues.""" + ctx.run("poetry run mlflow doctor") + + +@task +def serve( + ctx: Context, host: str = "127.0.0.1", port: str = "5000", backend_uri: str = "./mlruns" +) -> None: + """Start mlflow server with the given host, port, and backend uri.""" + ctx.run( + f"poetry run mlflow server --host={host} --port={port} --backend-store-uri={backend_uri}" + ) + + +@task(pre=[doctor, serve], default=True) +def all(_: Context) -> None: + """Run all mlflow tasks.""" diff --git a/tasks/packages.py b/tasks/packages.py index 8bb2d54..06f3103 100644 --- a/tasks/packages.py +++ b/tasks/packages.py @@ -2,8 +2,8 @@ # %% IMPORTS -from invoke import task from invoke.context import Context +from invoke.tasks import task from . import cleans diff --git a/tests/confs/invalid/0. invalid.yaml b/tests/confs/invalid/1. invalid.yaml similarity index 100% rename from tests/confs/invalid/0. invalid.yaml rename to tests/confs/invalid/1. invalid.yaml diff --git a/tests/confs/valid/1. tuning.yaml b/tests/confs/valid/1. tuning.yaml index 58ec32e..41b391d 100644 --- a/tests/confs/valid/1. tuning.yaml +++ b/tests/confs/valid/1. tuning.yaml @@ -2,15 +2,12 @@ job: KIND: TuningJob inputs: KIND: ParquetReader - path: ${test_data_path:}/inputs.parquet + path: "${tests_path:}/data/inputs.parquet" limit: 1500 targets: KIND: ParquetReader - path: ${test_data_path:}/targets.parquet + path: "${tests_path:}/data/targets.parquet" limit: 1500 - results: - KIND: ParquetWriter - path: ${tmp_path:}/results.parquet splitter: KIND: TimeSeriesSplitter n_splits: 3 diff --git a/tests/confs/valid/2. training.yaml b/tests/confs/valid/2. training.yaml index 5e5d92b..f1849a3 100644 --- a/tests/confs/valid/2. training.yaml +++ b/tests/confs/valid/2. training.yaml @@ -2,12 +2,9 @@ job: KIND: TrainingJob inputs: KIND: ParquetReader - path: ${test_data_path:}/inputs.parquet + path: "${tests_path:}/data/inputs.parquet" limit: 1500 targets: KIND: ParquetReader - path: ${test_data_path:}/targets.parquet + path: "${tests_path:}/data/targets.parquet" limit: 1500 - serializer: - KIND: JoblibModelSerializer - path: ${tmp_path:}/model.joblib diff --git a/tests/confs/valid/3. inference.yaml b/tests/confs/valid/3. inference.yaml deleted file mode 100644 index ffe366e..0000000 --- a/tests/confs/valid/3. inference.yaml +++ /dev/null @@ -1,12 +0,0 @@ -job: - KIND: InferenceJob - inputs: - KIND: ParquetReader - path: ${test_data_path:}/inputs.parquet - limit: 1500 - outputs: - KIND: ParquetWriter - path: ${tmp_path:}/outputs.parquet - deserializer: - KIND: JoblibModelDeserializer - path: ${tmp_path:}/model.joblib diff --git a/tests/confs/valid/3. promotion.yaml b/tests/confs/valid/3. promotion.yaml new file mode 100644 index 0000000..0f5feec --- /dev/null +++ b/tests/confs/valid/3. promotion.yaml @@ -0,0 +1,2 @@ +job: + KIND: PromotionJob diff --git a/tests/confs/valid/4. inference.yaml b/tests/confs/valid/4. inference.yaml new file mode 100644 index 0000000..5295dc6 --- /dev/null +++ b/tests/confs/valid/4. inference.yaml @@ -0,0 +1,9 @@ +job: + KIND: InferenceJob + inputs: + KIND: ParquetReader + path: "${tests_path:}/data/inputs.parquet" + limit: 1500 + outputs: + KIND: ParquetWriter + path: "${tmp_path:}/outputs.parquet" diff --git a/tests/conftest.py b/tests/conftest.py index 9a07f0d..24a408a 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -5,10 +5,12 @@ import os import typing as T -import mlflow import omegaconf import pytest -from bikes import datasets, metrics, models, registers, schemas, searchers, services, splitters +from _pytest import logging as pl +from bikes.core import metrics, models, schemas +from bikes.io import datasets, registries, services +from bikes.utils import searchers, signers, splitters # %% CONFIGS @@ -59,32 +61,20 @@ def outputs_path(data_path: str) -> str: return os.path.join(data_path, "outputs.parquet") -@pytest.fixture(scope="function") -def tmp_carbon_path(tmp_path: str) -> str: - """Return a tmp path of the carbon folder.""" - return os.path.join(tmp_path, "carbons") - - @pytest.fixture(scope="function") def tmp_outputs_path(tmp_path: str) -> str: """Return a tmp path for the outputs dataset.""" return os.path.join(tmp_path, "outputs.parquet") -@pytest.fixture(scope="function") -def tmp_results_path(tmp_path: str) -> str: - """Return a tmp path of the results dataset.""" - return os.path.join(tmp_path, "results.parquet") - - # %% - Configs @pytest.fixture(scope="session") def extra_config() -> str: """Extra config for scripts.""" - # use OmegaConf resolver for ${tmp_path:} - string = """ + # use OmegaConf resolver: ${tmp_path:} + config = """ { "job": { "mlflow_service": { @@ -94,7 +84,7 @@ def extra_config() -> str: } } """ - return string + return config # %% - Datasets @@ -103,19 +93,19 @@ def extra_config() -> str: @pytest.fixture(scope="session") def inputs_reader(inputs_path: str) -> datasets.ParquetReader: """Return a reader for the inputs dataset.""" - return datasets.ParquetReader(path=inputs_path, limit=LIMIT) + return datasets.ParquetReader(path=inputs_path) @pytest.fixture(scope="session") def targets_reader(targets_path: str) -> datasets.ParquetReader: """Return a reader for the targets dataset.""" - return datasets.ParquetReader(path=targets_path, limit=LIMIT) + return datasets.ParquetReader(path=targets_path) @pytest.fixture(scope="session") def outputs_reader(outputs_path: str) -> datasets.ParquetReader: """Return a reader for the outputs dataset.""" - return datasets.ParquetReader(path=outputs_path, limit=LIMIT) + return datasets.ParquetReader(path=outputs_path) @pytest.fixture(scope="function") @@ -124,12 +114,6 @@ def tmp_outputs_writer(tmp_outputs_path: str) -> datasets.ParquetWriter: return datasets.ParquetWriter(path=tmp_outputs_path) -@pytest.fixture(scope="function") -def tmp_results_writer(tmp_results_path: str) -> datasets.ParquetWriter: - """Return a writer for the tmp results dataset.""" - return datasets.ParquetWriter(path=tmp_results_path) - - # %% - Dataframes @@ -169,22 +153,25 @@ def time_series_splitter() -> splitters.TimeSeriesSplitter: return splitters.TimeSeriesSplitter(n_splits=N_SPLITS, test_size=TEST_SIZE) +# %% - Searchers + + @pytest.fixture(scope="session") -def train_test_split( - train_test_splitter: splitters.TrainTestSplitter, - inputs: schemas.Inputs, - targets: schemas.Targets, -) -> splitters.TrainTest: - """Return the train and test indexes for the inputs dataframe.""" - return next(train_test_splitter.split(inputs=inputs, targets=targets)) +def searcher() -> searchers.Searcher: + """Return the default searcher object.""" + param_grid = {"max_depth": [1, 2], "n_estimators": [3]} + return searchers.GridCVSearcher(param_grid=param_grid) + + +# %% - Subsets @pytest.fixture(scope="session") def train_test_sets( - train_test_split: splitters.TrainTest, inputs: schemas.Inputs, targets: schemas.Targets + train_test_splitter: splitters.Splitter, inputs: schemas.Inputs, targets: schemas.Targets ) -> tuple[schemas.Inputs, schemas.Targets, schemas.Inputs, schemas.Targets]: - """Return the inputs/targets train and test sets from the train and test split.""" - train_index, test_index = train_test_split + """Return the inputs and targets train and test sets from the splitter.""" + train_index, test_index = next(train_test_splitter.split(inputs=inputs, targets=targets)) inputs_train, inputs_test = inputs.iloc[train_index], inputs.iloc[test_index] targets_train, targets_test = targets.iloc[train_index], targets.iloc[test_index] return ( @@ -195,32 +182,17 @@ def train_test_sets( ) -# %% - Searchers - - -@pytest.fixture(scope="session") -def default_searcher() -> searchers.GridCVSearcher: - """Return the default searcher.""" - param_grid = {"max_depth": [2, 4], "n_estimators": [10, 15]} - return searchers.GridCVSearcher(param_grid=param_grid) - - # %% - Models @pytest.fixture(scope="session") -def empty_model() -> models.BaselineSklearnModel: - """Return an empty model for testing.""" - return models.BaselineSklearnModel() - - -@pytest.fixture(scope="session") -def default_model( - empty_model: models.BaselineSklearnModel, inputs: schemas.Inputs, targets: schemas.Targets +def model( + train_test_sets: tuple[schemas.Inputs, schemas.Targets, schemas.Inputs, schemas.Targets], ) -> models.BaselineSklearnModel: - """Return the default model.""" - model = empty_model.fit(inputs=inputs, targets=targets) - # model.predict(inputs=inputs).to_parquet("tests/data/outputs.parquet") + """Return a train model for testing.""" + model = models.BaselineSklearnModel() + inputs_train, targets_train, _, _ = train_test_sets + model.fit(inputs=inputs_train, targets=targets_train) return model @@ -228,126 +200,151 @@ def default_model( @pytest.fixture(scope="session") -def default_metric() -> metrics.SklearnMetric: +def metric() -> metrics.SklearnMetric: """Return the default metric.""" return metrics.SklearnMetric() +# %% - Signers + + +@pytest.fixture(scope="session") +def signer() -> signers.Signer: + """Return a model signer.""" + return signers.InferSigner() + + # %% - Services @pytest.fixture(scope="session", autouse=True) -def logger_service() -> services.LoggerService: +def logger_service() -> T.Generator[services.LoggerService, None, None]: """Return and start the logger service.""" service = services.LoggerService(colorize=False, diagnose=True) - service.start() # ready to be used - return service - - -@pytest.fixture(scope="function") -def carbon_service(tmp_carbon_path: str) -> T.Generator[services.CarbonService, None, None]: - """Return and start the carbon service.""" - service = services.CarbonService(output_dir=tmp_carbon_path) - service.start() # ready to be used + service.start() yield service service.stop() +@pytest.fixture +def logger_caplog( + caplog: pl.LogCaptureFixture, logger_service: services.LoggerService +) -> T.Generator[pl.LogCaptureFixture, None, None]: + """Extend pytest caplog fixture with the logger service (loguru).""" + # https://loguru.readthedocs.io/en/stable/resources/migration.html#replacing-caplog-fixture-from-pytest-library + logger = logger_service.logger() + handler_id = logger.add( + caplog.handler, + level=0, + format="{message}", + filter=lambda record: record["level"].no >= caplog.handler.level, + enqueue=False, # Set to 'True' if your test is spawning child processes. + ) + yield caplog + logger.remove(handler_id) + + @pytest.fixture(scope="function", autouse=True) -def mlflow_service(tmp_path: str) -> services.MLflowService: +def mlflow_service(tmp_path: str) -> T.Generator[services.MlflowService, None, None]: """Return and start the mlflow service.""" - tracking_uri = f"{tmp_path}/experiments/" - registry_uri = f"{tmp_path}/models/" - service = services.MLflowService( - tracking_uri=tracking_uri, - registry_uri=registry_uri, - experiment_name="Testing", - registry_name="Testing", + service = services.MlflowService( + tracking_uri=f"{tmp_path}/tracking/", + registry_uri=f"{tmp_path}/registry/", + experiment_name="Experiment-Testing", + registry_name="Registry-Testing", ) - service.start() # ready to be used - return service + service.start() + yield service + service.stop() -# %% - Registers +# %% - Resolvers -@pytest.fixture(scope="session") -def default_signer() -> registers.InferSigner: - """Return the default infer signer.""" - return registers.InferSigner() +@pytest.fixture(scope="session", autouse=True) +def tests_path_resolver(tests_path: str) -> str: + """Register the tests path resolver with OmegaConf.""" + def resolver() -> str: + """Get tests path.""" + return tests_path -@pytest.fixture(scope="session") -def default_signature( - default_signer: registers.InferSigner, inputs: schemas.Inputs, outputs: schemas.Outputs -) -> registers.Signature: - """Return the default signature.""" - return default_signer.sign(inputs=inputs, outputs=outputs) + omegaconf.OmegaConf.register_new_resolver("tests_path", resolver, use_cache=True, replace=False) + return tests_path -@pytest.fixture(scope="session") -def default_saver() -> registers.CustomSaver: - """Return the default model saver.""" - return registers.CustomSaver(path="test") +@pytest.fixture(scope="function", autouse=True) +def tmp_path_resolver(tmp_path: str) -> str: + """Register the tmp path resolver with OmegaConf.""" + def resolver() -> str: + """Get tmp data path.""" + return tmp_path -@pytest.fixture(scope="session") -def default_loader() -> registers.CustomLoader: - """Return the default model loader.""" - return registers.CustomLoader() + omegaconf.OmegaConf.register_new_resolver("tmp_path", resolver, use_cache=False, replace=True) + return tmp_path -# %% - Resolvers +# %% - Signatures -@pytest.fixture(scope="session", autouse=True) -def test_data_path_resolver(data_path: str) -> str: - """Register test_data with OmegaConf.""" +@pytest.fixture(scope="session") +def signature( + signer: signers.Signer, inputs: schemas.Inputs, outputs: schemas.Outputs +) -> signers.Signature: + """Return the signature for the testing model.""" + return signer.sign(inputs=inputs, outputs=outputs) - def test_data_resolver() -> str: - """Get data_path.""" - return data_path - omegaconf.OmegaConf.register_new_resolver( - "test_data_path", test_data_resolver, use_cache=True, replace=True - ) - return data_path +# %% - Registries -@pytest.fixture(scope="function", autouse=True) -def tmp_path_resolver(tmp_path: str) -> str: - """Register tmp_path with Omegaconf.""" +@pytest.fixture(scope="session") +def saver() -> registries.CustomSaver: + """Return the default model saver.""" + return registries.CustomSaver(path="custom-model") - def tmp_path_resolver() -> str: - """Return tmp_path.""" - return tmp_path - omegaconf.OmegaConf.register_new_resolver("tmp_path", tmp_path_resolver, replace=True) - return tmp_path +@pytest.fixture(scope="session") +def loader() -> registries.CustomLoader: + """Return the default model loader.""" + return registries.CustomLoader() -# %% - Mlflow Registry +@pytest.fixture(scope="session") +def register() -> registries.MlflowRegister: + """Return the default model register.""" + tags = {"context": "test", "role": "fixture"} + return registries.MlflowRegister(tags=tags) @pytest.fixture(scope="function") -def default_alias() -> str: - """Return the default model alias.""" - return "Default" +def model_version( + model: models.Model, + inputs: schemas.Inputs, + signature: signers.Signature, + saver: registries.Saver, + register: registries.Register, + mlflow_service: services.MlflowService, +) -> registries.Version: + """Save and register the default model version.""" + run_config = mlflow_service.RunConfig(name="Custom-Run") + with mlflow_service.run_context(run_config=run_config): + info = saver.save(model=model, signature=signature, input_example=inputs) + version = register.register(name=mlflow_service.registry_name, model_uri=info.model_uri) + return version @pytest.fixture(scope="function") -def default_mlflow_model_version( - inputs: schemas.Inputs, - default_alias: str, - default_model: models.Model, - default_saver: registers.CustomSaver, - default_signature: registers.Signature, - mlflow_service: services.MLflowService, -) -> registers.Version: - """Return an MLflow version for the default model.""" - with mlflow.start_run(run_name="Default") as run: - default_saver.save(model=default_model, signature=default_signature, input_example=inputs) - version = mlflow_service.register( - run_id=run.info.run_id, path=default_saver.path, alias=default_alias - ) - return version +def model_alias( + model_version: registries.Version, + mlflow_service: services.MlflowService, +) -> registries.Alias: + """Promote the default model version with an alias.""" + alias = "Promotion" + client = mlflow_service.client() + client.set_registered_model_alias( + name=mlflow_service.registry_name, alias=alias, version=model_version.version + ) + model_alias = client.get_model_version_by_alias(name=mlflow_service.registry_name, alias=alias) + return model_alias diff --git a/tests/test_metrics.py b/tests/core/test_metrics.py similarity index 83% rename from tests/test_metrics.py rename to tests/core/test_metrics.py index 05b6fa8..f6114e6 100644 --- a/tests/test_metrics.py +++ b/tests/core/test_metrics.py @@ -1,7 +1,8 @@ # %% IMPORTS + import pytest -from bikes import metrics, models, schemas +from bikes.core import metrics, models, schemas # %% METRICS @@ -17,17 +18,17 @@ def test_sklearn_metric( name: str, interval: tuple[int, int], greater_is_better: bool, + model: models.Model, inputs: schemas.Inputs, targets: schemas.Targets, outputs: schemas.Outputs, - default_model: models.Model, ) -> None: # given low, high = interval metric = metrics.SklearnMetric(name=name, greater_is_better=greater_is_better) # when score = metric.score(targets=targets, outputs=outputs) - scorer = metric.scorer(model=default_model, inputs=inputs, targets=targets) + scorer = metric.scorer(model=model, inputs=inputs, targets=targets) # then assert low <= score <= high, "Score is not in the expected interval!" assert low <= scorer <= high, "Scorer is not in the expected interval!" diff --git a/tests/test_models.py b/tests/core/test_models.py similarity index 61% rename from tests/test_models.py rename to tests/core/test_models.py index adea3c1..fa7d04c 100644 --- a/tests/test_models.py +++ b/tests/core/test_models.py @@ -2,14 +2,15 @@ import typing as T -from bikes import models, schemas +import pytest +from bikes.core import models, schemas # %% MODELS def test_model() -> None: # given - class MyModel(models.Model): # type: ignore + class MyModel(models.Model): KIND: T.Literal["MyModel"] = "MyModel" # public @@ -22,18 +23,21 @@ def fit(self, inputs: schemas.Inputs, targets: schemas.Targets) -> T.Self: return self def predict(self, inputs: schemas.Inputs) -> schemas.Outputs: - pass + return schemas.Outputs() # when model = MyModel(a=10) params_init = model.get_params() params_set_params = model.set_params(b=20).get_params() + with pytest.raises(NotImplementedError) as error: + model.get_internal_model() # then - assert params_init == {"a": 10, "b": 2}, "Model should get the right params at init!" + assert params_init == {"a": 10, "b": 2}, "Model should have the given params after init!" assert params_set_params == { "a": 10, "b": 20, - }, "Model should set the right params after set_params!" + }, "Model should have the given params after set_params!" + assert isinstance(error.value, NotImplementedError), "Model should raise NotImplementedError!" def test_baseline_sklearn_model( @@ -44,8 +48,14 @@ def test_baseline_sklearn_model( inputs_train, targets_train, inputs_test, _ = train_test_sets model = models.BaselineSklearnModel().set_params(**params) # when + with pytest.raises(ValueError) as not_fitted_error: + model.get_internal_model() model.fit(inputs=inputs_train, targets=targets_train) outputs = model.predict(inputs=inputs_test) # then + assert not_fitted_error.match( + "Model is not fitted yet!" + ), "Model should raise an error when not fitted!" assert outputs.ndim == 2, "Outputs should be a dataframe!" assert model.get_params() == params, "Model should have the given params!" + assert model.get_internal_model() is not None, "Internal model should be fitted!" diff --git a/tests/test_schemas.py b/tests/core/test_schemas.py similarity index 84% rename from tests/test_schemas.py rename to tests/core/test_schemas.py index a64cfc3..7102286 100644 --- a/tests/test_schemas.py +++ b/tests/core/test_schemas.py @@ -1,6 +1,7 @@ # %% IMPORTS -from bikes import datasets, schemas +from bikes.core import schemas +from bikes.io import datasets # %% SCHEMAS @@ -23,7 +24,7 @@ def test_targets_schema(targets_reader: datasets.Reader) -> None: assert schema.check(data) is not None, "Targets data should be valid!" -def test_output_schema(outputs_reader: datasets.Reader) -> None: +def test_outputs_schema(outputs_reader: datasets.Reader) -> None: # given schema = schemas.OutputsSchema # when diff --git a/tests/test_configs.py b/tests/io/test_configs.py similarity index 82% rename from tests/test_configs.py rename to tests/io/test_configs.py index 8d14498..2cf9bfc 100644 --- a/tests/test_configs.py +++ b/tests/io/test_configs.py @@ -2,8 +2,8 @@ import os -from bikes import configs -from omegaconf import OmegaConf +import omegaconf as oc +from bikes.io import configs # %% PARSERS @@ -25,7 +25,7 @@ def test_parse_file(tmp_path: str) -> None: "a": 1, "b": True, "c": [3, 4], - }, "File config should be loaded correctly!" + }, "File config should be parsed correctly!" def test_parse_string() -> None: @@ -38,7 +38,7 @@ def test_parse_string() -> None: "a": 1, "b": 2, "data": [3, 4], - }, "String config should be loaded correctly!" + }, "String config should be parsed correctly!" # %% MERGERS @@ -46,7 +46,7 @@ def test_parse_string() -> None: def test_merge_configs() -> None: # given - confs = [OmegaConf.create({"x": i, i: i}) for i in range(3)] + confs = [oc.OmegaConf.create({"x": i, i: i}) for i in range(3)] # when config = configs.merge_configs(confs) # then @@ -68,7 +68,7 @@ def test_to_object() -> None: "b": True, "c": [3, 4], } - config = OmegaConf.create(values) + config = oc.OmegaConf.create(values) # when object_ = configs.to_object(config) # then diff --git a/tests/test_datasets.py b/tests/io/test_datasets.py similarity index 92% rename from tests/test_datasets.py rename to tests/io/test_datasets.py index f4b6d7b..0f4004d 100644 --- a/tests/test_datasets.py +++ b/tests/io/test_datasets.py @@ -3,7 +3,8 @@ import os import pytest -from bikes import datasets, schemas +from bikes.core import schemas +from bikes.io import datasets # %% READERS diff --git a/tests/io/test_registries.py b/tests/io/test_registries.py new file mode 100644 index 0000000..1002f6a --- /dev/null +++ b/tests/io/test_registries.py @@ -0,0 +1,131 @@ +# %% IMPORTS + +from bikes.core import models, schemas +from bikes.io import registries, services +from bikes.utils import signers + +# %% HELPERS + + +def test_uri_for_model_alias() -> None: + # given + name = "testing" + alias = "Champion" + # when + uri = registries.uri_for_model_alias(name=name, alias=alias) + # then + assert uri == f"models:/{name}@{alias}", "The model URI should be valid!" + + +def test_uri_for_model_version() -> None: + # given + name = "testing" + version = "1" + # when + uri = registries.uri_for_model_version(name=name, version=version) + # then + assert uri == f"models:/{name}/{version}", "The model URI should be valid!" + + +# %% SAVERS/LOADERS/REGISTERS + + +def test_custom_pipeline( + model: models.Model, + inputs: schemas.Inputs, + signature: signers.Signature, + mlflow_service: services.MlflowService, +) -> None: + # given + path = "custom" + name = "Custom" + tags = {"registry": "mlflow"} + saver = registries.CustomSaver(path=path) + loader = registries.CustomLoader() + register = registries.MlflowRegister(tags=tags) + run_config = services.MlflowService.RunConfig(name="Custom-Run") + # when + with mlflow_service.run_context(run_config=run_config) as run: + info = saver.save(model=model, signature=signature, input_example=inputs) + version = register.register(name=name, model_uri=info.model_uri) + model_uri = registries.uri_for_model_version(name=name, version=version.version) + adapter = loader.load(uri=model_uri) + outputs = adapter.predict(inputs=inputs) + # then + # - uri + assert model_uri == f"models:/{name}/{version.version}", "The model URI should be valid!" + # - info + assert info.run_id == run.info.run_id, "The run id should be the same!" + assert info.artifact_path == path, "The artifact path should be the same!" + assert info.signature == signature, "The model signature should be the same!" + assert info.flavors.get("python_function"), "The model should have a pyfunc flavor!" + # - version + assert version.name == name, "The model version name should be the same!" + assert version.tags == tags, "The model version tags should be the same!" + assert version.aliases == [], "The model version aliases should be empty!" + assert version.run_id == run.info.run_id, "The model version run id should be the same!" + # - adapter + assert ( + adapter.model.metadata.run_id == version.run_id + ), "The adapter model run id should be the same!" + assert ( + adapter.model.metadata.signature == signature + ), "The adapter model signature should be the same!" + assert ( + adapter.model.metadata.flavors.get("python_function") is not None + ), "The adapter model should have a python_function flavor!" + # - output + assert schemas.OutputsSchema.check(outputs) is not None, "Outputs should be valid!" + + +def test_builtin_pipeline( + model: models.Model, + inputs: schemas.Inputs, + signature: signers.Signature, + mlflow_service: services.MlflowService, +) -> None: + # given + path = "builtin" + name = "Builtin" + flavor = "sklearn" + tags = {"registry": "mlflow"} + saver = registries.BuiltinSaver(path=path, flavor=flavor) + loader = registries.BuiltinLoader() + register = registries.MlflowRegister(tags=tags) + run_config = services.MlflowService.RunConfig(name="Custom-Run") + # when + with mlflow_service.run_context(run_config=run_config) as run: + info = saver.save(model=model, signature=signature, input_example=inputs) + version = register.register(name=name, model_uri=info.model_uri) + model_uri = registries.uri_for_model_version(name=name, version=version.version) + adapter = loader.load(uri=model_uri) + outputs = adapter.predict(inputs=inputs) + # then + # - uri + assert model_uri == f"models:/{name}/{version.version}", "The model URI should be valid!" + # - info + assert info.run_id == run.info.run_id, "The run id should be the same!" + assert info.artifact_path == path, "The artifact path should be the same!" + assert info.signature == signature, "The model signature should be the same!" + assert info.flavors.get("python_function"), "The model should have a pyfunc flavor!" + assert info.flavors.get(flavor), f"The model should have a built-in model flavor: {flavor}!" + # - version + assert version.name == name, "The model version name should be the same!" + assert version.tags == tags, "The model version tags should be the same!" + assert version.aliases == [], "The model version aliases should be empty!" + assert version.run_id == run.info.run_id, "The model version run id should be the same!" + # - adapter + assert ( + adapter.model.metadata.run_id == version.run_id + ), "The adapter model run id should be the same!" + assert ( + adapter.model.metadata.signature == signature + ), "The adapter model signature should be the same!" + assert ( + adapter.model.metadata.flavors.get("python_function") is not None + ), "The adapter model should have a python_function flavor!" + assert adapter.model.metadata.flavors.get( + flavor + ), f"The model should have a built-in model flavor: {flavor}!" + # - output + assert schemas.OutputsSchema.check(outputs) is not None, "Outputs should be valid!" diff --git a/tests/io/test_services.py b/tests/io/test_services.py new file mode 100644 index 0000000..dca2ae7 --- /dev/null +++ b/tests/io/test_services.py @@ -0,0 +1,59 @@ +# %% IMPORTS + +import _pytest.logging as pl +import mlflow +from bikes.io import services + +# %% SERVICES + + +def test_logger_service( + logger_service: services.LoggerService, logger_caplog: pl.LogCaptureFixture +) -> None: + # given + service = logger_service + logger = service.logger() + # when + logger.debug("DEBUG") + logger.error("ERROR") + # then + assert "DEBUG" in logger_caplog.messages, "Debug message should be logged!" + assert "ERROR" in logger_caplog.messages, "Error message should be logged!" + + +def test_mlflow_service(mlflow_service: services.MlflowService) -> None: + # given + service = mlflow_service + run_config = mlflow_service.RunConfig( + name="testing", + tags={"service": "mlflow"}, + description="a test run.", + log_system_metrics=True, + ) + # when + client = service.client() + with service.run_context(run_config=run_config) as context: + pass + finished = client.get_run(run_id=context.info.run_id) + # then + # - run + assert run_config.tags is not None, "Run config tags should be set!" + # - mlflow + assert service.tracking_uri == mlflow.get_tracking_uri(), "Tracking URI should be the same!" + assert service.registry_uri == mlflow.get_registry_uri(), "Registry URI should be the same!" + assert mlflow.get_experiment_by_name(service.experiment_name), "Experiment should be setup!" + # - client + assert service.tracking_uri == client.tracking_uri, "Tracking URI should be the same!" + assert service.registry_uri == client._registry_uri, "Tracking URI should be the same!" + assert client.get_experiment_by_name(service.experiment_name), "Experiment should be setup!" + # - context + assert context.info.run_name == run_config.name, "Context name should be the same!" + assert ( + run_config.description in context.data.tags.values() + ), "Context desc. should be in tags values!" + assert ( + context.data.tags.items() > run_config.tags.items() + ), "Context tags should be a subset of the given tags!" + assert context.info.status == "RUNNING", "Context should be running!" + # - finished + assert finished.info.status == "FINISHED", "Finished should be finished!" diff --git a/tests/jobs/test_base.py b/tests/jobs/test_base.py new file mode 100644 index 0000000..2bb2648 --- /dev/null +++ b/tests/jobs/test_base.py @@ -0,0 +1,29 @@ +# %% IMPORTS + +from bikes.io import services +from bikes.jobs import base + +# %% JOBS + + +def test_job( + logger_service: services.LoggerService, mlflow_service: services.MlflowService +) -> None: + # given + class MyJob(base.Job): + KIND: str = "MyJob" + + def run(self) -> base.Locals: + a, b = 1, "test" + return locals() + + job = MyJob(logger_service=logger_service, mlflow_service=mlflow_service) + # when + with job as runner: + out = runner.run() + # then + # - inputs + assert hasattr(job, "logger_service"), "Job should have an Logger service!" + assert hasattr(job, "mlflow_service"), "Job should have an Mlflow service!" + # - outputs + assert set(out) == {"self", "a", "b"}, "Run should return local variables!" diff --git a/tests/jobs/test_inference.py b/tests/jobs/test_inference.py new file mode 100644 index 0000000..ef721e6 --- /dev/null +++ b/tests/jobs/test_inference.py @@ -0,0 +1,59 @@ +# %% IMPORTS + + +from bikes import jobs +from bikes.io import datasets, registries, services + +# %% JOBS + + +def test_inference_job( + mlflow_service: services.MlflowService, + logger_service: services.LoggerService, + inputs_reader: datasets.Reader, + tmp_outputs_writer: datasets.Writer, + model_alias: registries.Version, + loader: registries.Loader, +) -> None: + # given + assert len(model_alias.aliases) == 1, "Model should have one alias!" + alias = model_alias.aliases[0] + # when + job = jobs.InferenceJob( + mlflow_service=mlflow_service, + logger_service=logger_service, + inputs=inputs_reader, + outputs=tmp_outputs_writer, + alias=alias, + loader=loader, + ) + with job as runner: + out = runner.run() + # then + # - vars + assert set(out) == { + "self", + "logger", + "inputs", + "inputs_", + "model_uri", + "model", + "outputs", + } + # - inputs + assert out["inputs"].ndim == out["inputs_"].ndim == 2, "Inputs should be a dataframe!" + # - model uri + assert alias in out["model_uri"], "Model URI should contain the model alias!" + assert ( + mlflow_service.registry_name in out["model_uri"] + ), "Model URI should contain the registry name!" + # - model + assert ( + out["model"].model.metadata.run_id == model_alias.run_id + ), "Model run id should be the same!" + assert out["model"].model.metadata.signature is not None, "Model should have a signature!" + assert out["model"].model.metadata.flavors.get( + "python_function" + ), "Model should have a pyfunc flavor!" + # - outputs + assert out["outputs"].ndim == 2, "Outputs should be a dataframe!" diff --git a/tests/jobs/test_promotion.py b/tests/jobs/test_promotion.py new file mode 100644 index 0000000..9b8322b --- /dev/null +++ b/tests/jobs/test_promotion.py @@ -0,0 +1,66 @@ +# %% IMPORTS + +import mlflow +import pytest +from bikes import jobs +from bikes.io import registries, services + +# %% JOBS + + +@pytest.mark.parametrize( + "version", + [ + None, # latest version + 1, # specific version + pytest.param( + 2, + marks=pytest.mark.xfail( + reason="Version does not exist.", + raises=mlflow.exceptions.MlflowException, + ), + ), + ], +) +def test_promotion_job( + version: int | None, + mlflow_service: services.MlflowService, + logger_service: services.LoggerService, + model_version: registries.Version, +) -> None: + # given + alias = "Testing" + # when + job = jobs.PromotionJob( + mlflow_service=mlflow_service, + logger_service=logger_service, + version=version, + alias=alias, + ) + with job as runner: + out = runner.run() + # then + # - vars + assert set(out) == { + "self", + "logger", + "client", + "name", + "version", + "model_version", + } + # - name + assert out["name"] == mlflow_service.registry_name, "Model name should be the same!" + # - version + assert out["version"] == model_version.version, "Version number should be the same!" + # - model version + assert out["model_version"].name == out["name"], "Model version name should be the same!" + assert ( + out["model_version"].version == out["version"] + ), "Model version number should be the same!" + assert ( + out["model_version"].run_id == model_version.run_id + ), "Model version run id should be the same!" + assert out["model_version"].aliases == [ + alias + ], "Model version aliases should contain the given alias!" diff --git a/tests/jobs/test_training.py b/tests/jobs/test_training.py new file mode 100644 index 0000000..a1cfe10 --- /dev/null +++ b/tests/jobs/test_training.py @@ -0,0 +1,140 @@ +# %% IMPORTS + +from bikes import jobs +from bikes.core import metrics, models +from bikes.io import datasets, registries, services +from bikes.utils import signers, splitters + +# %% JOBS + + +def test_training_job( + mlflow_service: services.MlflowService, + logger_service: services.LoggerService, + inputs_reader: datasets.Reader, + targets_reader: datasets.Reader, + model: models.Model, + metric: metrics.Metric, + train_test_splitter: splitters.TrainTestSplitter, + saver: registries.Saver, + signer: signers.Signer, + register: registries.Register, +) -> None: + # given + run_config = services.MlflowService.RunConfig( + name="TrainingTest", tags={"context": "training"}, description="Training job." + ) + splitter = train_test_splitter + client = mlflow_service.client() + # when + job = jobs.TrainingJob( + mlflow_service=mlflow_service, + logger_service=logger_service, + run_config=run_config, + inputs=inputs_reader, + targets=targets_reader, + model=model, + metrics=[metric], + splitter=splitter, + saver=saver, + signer=signer, + registry=register, + ) + with job as runner: + out = runner.run() + # then + # - vars + assert set(out) == { + "self", + "logger", + "client", + "run", + "inputs", + "inputs_", + "targets", + "targets_", + "train_index", + "test_index", + "inputs_test", + "inputs_train", + "inputs_test", + "targets_train", + "targets_test", + "outputs_test", + "i", + "metric", + "score", + "model_signature", + "model_info", + "model_version", + } + # - run + assert run_config.tags is not None, "Run config tags should be set!" + assert out["run"].info.run_name == run_config.name, "Run name should be the same!" + assert run_config.description in out["run"].data.tags.values(), "Run desc. should be tags!" + assert ( + out["run"].data.tags.items() > run_config.tags.items() + ), "Run tags should be a subset of tags!" + # - data + assert out["inputs"].ndim == out["inputs_"].ndim == 2, "Inputs should be a dataframe!" + assert out["targets"].ndim == out["targets_"].ndim == 2, "Targets should be a dataframe!" + # - splitter + assert len(out["inputs_train"]) + len(out["inputs_test"]) == len( + out["inputs"] + ), "Train and test inputs should have the same length as inputs!" + assert len(out["targets_train"]) + len(out["targets_test"]) == len( + out["targets"] + ), "Train and test targets should have the same length as targets!" + assert ( + len(out["train_index"]) == len(out["inputs_train"]) == len(out["targets_train"]) + ), "Train inputs and targets should have the same length!" + assert ( + len(out["test_index"]) == len(out["inputs_test"]) == len(out["targets_test"]) + ), "Test inputs and targets should have the same length!" + # - outputs + assert ( + out["outputs_test"].shape == out["targets_test"].shape + ), "Outputs should have the same shape as targets!" + assert ( + len(out["test_index"]) == len(out["outputs_test"]) == len(out["inputs_test"]) + ), "Outputs should have the same length as inputs!" + # - i and score + assert out["i"] == len(job.metrics), "i should be the number of metrics computed!" + assert float("-inf") < out["score"] < float("+inf"), "Score should be between 0 and 1!" + # - model signature + assert out["model_signature"].inputs is not None, "Model signature inputs should not be None!" + assert out["model_signature"].outputs is not None, "Model signature outputs should not be None!" + # - model info + assert ( + out["model_info"].run_id == out["run"].info.run_id + ), "Model info run id should be the same!" + assert ( + out["model_info"].signature == out["model_signature"] + ), "Model info signature should be the same!" + assert out["model_info"].artifact_path == saver.path, "Model info path should be the same!" + # - model version + assert out["model_version"].version == 1, "Model version number should be 1!" + assert out["model_version"].aliases == [], "Model version aliases should be empty!" + assert out["model_version"].tags == register.tags, "Model version tags should be the same!" + assert ( + out["model_version"].name == mlflow_service.registry_name + ), "Model name should be the same!" + assert ( + out["model_version"].run_id == out["run"].info.run_id + ), "Model version run id should be the same!" + # - mlflow tracking + experiment = client.get_experiment_by_name(name=mlflow_service.experiment_name) + assert ( + experiment.name == mlflow_service.experiment_name + ), "Mlflow Experiment name should be the same!" + runs = client.search_runs(experiment_ids=experiment.experiment_id) + assert len(runs) == 1, "There should be a single Mlflow run for training!" + assert metric.name in runs[0].data.metrics, "Metric should be logged in Mlflow!" + assert runs[0].info.status == "FINISHED", "Mlflow run status should be set as FINISHED!" + # - mlflow registry + model_version = client.get_model_version( + name=mlflow_service.registry_name, version=out["model_version"].version + ) + assert ( + model_version.run_id == out["run"].info.run_id + ), "MLFlow model version run id should be the same!" diff --git a/tests/jobs/test_tuning.py b/tests/jobs/test_tuning.py new file mode 100644 index 0000000..f5d437c --- /dev/null +++ b/tests/jobs/test_tuning.py @@ -0,0 +1,81 @@ +# %% IMPORTS + +from bikes import jobs +from bikes.core import metrics, models +from bikes.io import datasets, services +from bikes.utils import searchers, splitters + +# %% JOBS + + +def test_tuning_job( + mlflow_service: services.MlflowService, + logger_service: services.LoggerService, + inputs_reader: datasets.Reader, + targets_reader: datasets.Reader, + model: models.Model, + metric: metrics.Metric, + time_series_splitter: splitters.TimeSeriesSplitter, + searcher: searchers.Searcher, +) -> None: + # given + run_config = services.MlflowService.RunConfig( + name="TuningTest", tags={"context": "tuning"}, description="Tuning job." + ) + splitter = time_series_splitter + client = mlflow_service.client() + # when + job = jobs.TuningJob( + mlflow_service=mlflow_service, + logger_service=logger_service, + run_config=run_config, + inputs=inputs_reader, + targets=targets_reader, + model=model, + metric=metric, + splitter=splitter, + searcher=searcher, + ) + with job as runner: + out = runner.run() + # then + # - vars + assert set(out) == { + "self", + "logger", + "run", + "inputs", + "inputs_", + "targets", + "targets_", + "results", + "best_params", + "best_score", + } + # - run + assert run_config.tags is not None, "Run config tags should be set!" + assert out["run"].info.run_name == run_config.name, "Run name should be the same!" + assert run_config.description in out["run"].data.tags.values(), "Run desc. should be tags!" + assert ( + out["run"].data.tags.items() > run_config.tags.items() + ), "Run tags should be a subset of tags!" + # - data + assert out["inputs"].ndim == out["inputs_"].ndim == 2, "Inputs should be a dataframe!" + assert out["targets"].ndim == out["inputs_"].ndim == 2, "Targets should be a dataframe!" + # - results + assert out["results"].ndim == 2, "Results should be a dataframe!" + # - best score + assert ( + float("-inf") < out["best_score"] < float("inf") + ), "Best score should be between -inf and +inf!" + # - best params + assert ( + out["best_params"].keys() == searcher.param_grid.keys() + ), "Best params should have the same keys!" + # - mlflow tracking + experiment = client.get_experiment_by_name(name=mlflow_service.experiment_name) + assert ( + experiment.name == mlflow_service.experiment_name + ), "Mlflow experiment name should be the same!" + runs = client.search_runs(experiment_ids=experiment.experiment_id) + assert len(runs) == len(out["results"]) + 1, "Mlflow should have 1 run per result + parent!" diff --git a/tests/test_jobs.py b/tests/test_jobs.py deleted file mode 100644 index 04650f6..0000000 --- a/tests/test_jobs.py +++ /dev/null @@ -1,234 +0,0 @@ -# %% IMPORTS - -import os - -from bikes import datasets, jobs, metrics, models, registers, searchers, services, splitters - -# %% JOBS - - -def test_tuning_job( - inputs_reader: datasets.Reader, - targets_reader: datasets.Reader, - tmp_results_writer: datasets.ParquetWriter, - default_model: models.Model, - default_metric: metrics.Metric, - default_searcher: searchers.GridCVSearcher, - time_series_splitter: splitters.TimeSeriesSplitter, - logger_service: services.LoggerService, - carbon_service: services.CarbonService, - mlflow_service: services.MLflowService, -) -> None: - # given - run_name = "TuningTest" - job = jobs.TuningJob( - run_name=run_name, - inputs=inputs_reader, - targets=targets_reader, - results=tmp_results_writer, - model=default_model, - metric=default_metric, - searcher=default_searcher, - splitter=time_series_splitter, - logger_service=logger_service, - carbon_service=carbon_service, - mlflow_service=mlflow_service, - ) - mlflow_client = mlflow_service.client() - n_trials = sum(len(vs) for vs in default_searcher.param_grid.values()) - # when - with job as runner: - out = runner.run() - # then - # - vars - assert set(out) == { - "self", - "run", - "inputs", - "targets", - "results", - "best_score", - "best_params", - } - # - run - assert out["run"].info.run_name == run_name, "Run name should be the same!" - # - read - assert out["inputs"].ndim == 2, "Inputs should be a dataframe!" - assert out["targets"].ndim == 2, "Target should be a dataframe!" - # - search - assert len(out["results"]) == n_trials, "Results should one row per candidate!" - assert float("-inf") <= out["best_score"] <= float("+inf"), "Best score should be a float!" - assert ( - out["best_params"].keys() == default_searcher.param_grid.keys() - ), "Best params should have the same keys!" - # - write - assert os.path.exists(tmp_results_writer.path), "Results should be saved to the given path!" - # - mlflow tracking - mlflow_experiment = mlflow_client.get_experiment_by_name(name=mlflow_service.experiment_name) - assert ( - mlflow_experiment.name == mlflow_service.experiment_name - ), "Mlflow experiment name should be the same!" - mlflow_runs = mlflow_client.search_runs(experiment_ids=mlflow_experiment.experiment_id) - assert ( - len(mlflow_runs) == n_trials + 1 - ), "There should be as many Mlflow runs as trials + 1 for parent!" - - -def test_training_job( - inputs_reader: datasets.Reader, - targets_reader: datasets.Reader, - default_saver: registers.Saver, - empty_model: models.Model, - default_metric: metrics.Metric, - default_signer: registers.Signer, - train_test_splitter: splitters.TrainTestSplitter, - logger_service: services.LoggerService, - carbon_service: services.CarbonService, - mlflow_service: services.MLflowService, -) -> None: - # given - run_name = "TrainingTest" - scorers = [default_metric] - registry_alias = "Testing" - job = jobs.TrainingJob( - run_name=run_name, - inputs=inputs_reader, - targets=targets_reader, - saver=default_saver, - model=empty_model, - signer=default_signer, - scorers=scorers, - splitter=train_test_splitter, - registry_alias=registry_alias, - logger_service=logger_service, - carbon_service=carbon_service, - mlflow_service=mlflow_service, - ) - mlflow_client = mlflow_service.client() - # when - with job as runner: - out = runner.run() - # then - # - vars - assert set(out) == { - "self", - "run", - "inputs", - "targets", - "train_index", - "test_index", - "inputs_train", - "inputs_test", - "targets_train", - "targets_test", - "outputs_test", - "scorer", - "score", - "i", - "signature", - "info", - "version", - } - # - run - assert out["run"].info.run_name == run_name, "Run name should be the same!" - # - read - assert out["inputs"].ndim == 2, "Inputs should be a dataframe!" - assert out["targets"].ndim == 2, "Target should be a dataframe!" - # - split - assert ( - len(out["train_index"]) + len(out["test_index"]) - == len(out["inputs"]) - == len(out["targets"]) - ), "Train and test indexes should have the same length as inputs! and targets!" - assert ( - len(out["inputs_train"]) == len(out["targets_train"]) == len(out["train_index"]) - ), "Inputs and targets train should have the same length as train index!" - assert ( - len(out["inputs_test"]) == len(out["targets_test"]) == len(out["test_index"]) - ), "Inputs and targets test should have the same length as test index!" - # - outputs - assert len(out["outputs_test"]) == len( - out["inputs_test"] - ), "Outputs should have the same length as inputs!" - assert ( - out["outputs_test"].shape == out["targets_test"].shape - ), "Outputs should have the same shape as targets!" - # - score - assert out["i"] == len(scorers), "i should have the same length as scorers!" - assert ( - float("-inf") <= out["score"] <= float("+inf") - ), "Score should be between a numeric value!" - # - signature - assert out["signature"].inputs is not None, "Signature inputs should not be None!" - assert out["signature"].outputs is not None, "Signature outputs should not be None!" - # - info - assert out["info"].run_id == out["run"].info.run_id, "Info run id should be the same!" - assert out["info"].signature == out["signature"], "Info signatures should be the same!" - assert out["info"].artifact_path == default_saver.path, "Info path should be the same!" - # - version - assert out["version"].version == 1, "Version number should be 1!" - assert out["version"].run_id == out["run"].info.run_id, "Version run id should be the same!" - # - mlflow tracking - mlflow_experiment = mlflow_client.get_experiment_by_name(name=mlflow_service.experiment_name) - assert ( - mlflow_experiment.name == mlflow_service.experiment_name - ), "MLflow Experiment name should be the same!" - mlflow_runs = mlflow_client.search_runs(experiment_ids=mlflow_experiment.experiment_id) - assert len(mlflow_runs) == 1, "There should be a single MLflow run for the training!" - # - mlflow registry - model_version = mlflow_client.get_model_version( - name=mlflow_service.registry_name, version=out["version"].version - ) - assert ( - model_version.run_id == out["run"].info.run_id - ), "MLFlow model version run id should be the same!" - - -def test_inference_job( - inputs_reader: datasets.ParquetReader, - tmp_outputs_writer: datasets.ParquetWriter, - default_loader: registers.Loader, - default_alias: str, - default_mlflow_model_version: registers.Version, - logger_service: services.LoggerService, - carbon_service: services.CarbonService, - mlflow_service: services.MLflowService, -) -> None: - # given - registry_alias = "Testing" - job = jobs.InferenceJob( - inputs=inputs_reader, - outputs=tmp_outputs_writer, - registry_alias=default_alias, - loader=default_loader, - logger_service=logger_service, - carbon_service=carbon_service, - mlflow_service=mlflow_service, - ) - # when - with job as runner: - out = runner.run() - # then - # - vars - assert set(out) == { - "self", - "inputs", - "uri", - "model", - "outputs", - } - # - inputs - assert out["inputs"].ndim == 2, "Inputs should be a dataframe!" - # - uri - assert mlflow_service.registry_name in out["uri"], "URI should contain the registry name!" - assert registry_alias in out["uri"], "URI should contain the registry alias!" - # - model - assert out["model"].metadata.signature is not None, "Model should have a valid signature!" - assert out["model"].metadata.flavors.get( - "python_function" - ), "Model should have a pyfunc flavor!" - assert ( - out["model"].metadata.run_id == default_mlflow_model_version.run_id - ), "Model run id should be the same!" - # - outputs - assert os.path.exists(tmp_outputs_writer.path), "Outputs should be saved to the given path!" diff --git a/tests/test_registers.py b/tests/test_registers.py deleted file mode 100644 index 0ca7ed9..0000000 --- a/tests/test_registers.py +++ /dev/null @@ -1,82 +0,0 @@ -"""Test the registers module.""" - -# %% IMPORTS - -import mlflow -from bikes import models, registers, schemas, services - -# %% ADAPTERS - - -def test_custom_adapter(default_model: models.Model, inputs: schemas.Inputs) -> None: - # given - adapter = registers.CustomAdapter(model=default_model) - # when - outputs = adapter.predict(context=None, inputs=inputs) - # then - assert schemas.OutputsSchema.check(outputs) is not None, "Outputs should be valid!" - - -# %% SIGNERS - - -def test_infer_signer(inputs: schemas.Inputs, outputs: schemas.Outputs) -> None: - # given - signer = registers.InferSigner() - # when - signature = signer.sign(inputs=inputs, outputs=outputs) - # then - assert set(signature.inputs.input_names()) == set( - inputs.columns - ), "Signature inputs should contain input column names." - assert set(signature.outputs.input_names()) == set( - outputs.columns - ), "Signature outputs should contain output column names." - - -# %% SAVERS - - -def test_custom_saver( - inputs: schemas.Inputs, default_model: models.Model, default_signature: registers.Signature -) -> None: - # given - path = "custom" - saver = registers.CustomSaver(path=path) - # when - with mlflow.start_run(run_name="Custom") as run: - info = saver.save(model=default_model, signature=default_signature, input_example=inputs) - # then - assert info.run_id == run.info.run_id, "The run id should be the same!" - assert info.artifact_path == path, "The artifact path should be the same!" - assert info.flavors.get("python_function"), "The model should have a pyfunc!" - assert info.signature == default_signature, "The model signature should be the same!" - - -# %% LOADERS - - -def test_custom_loader( - inputs: schemas.Inputs, - mlflow_service: services.MLflowService, - default_mlflow_model_version: registers.Version, -) -> None: - # given - name = mlflow_service.registry_name - version = default_mlflow_model_version.version - uri = f"models:/{name}/{version}" - loader = registers.CustomLoader() - # when - model = loader.load(uri=uri) - outputs = model.predict(data=inputs) - # then - # # - model - assert model.metadata.signature is not None, "The model should have a valid signature!" - assert ( - model.metadata.run_id == default_mlflow_model_version.run_id - ), "The model run id should be the same!" - assert ( - model.metadata.flavors.get("python_function") is not None - ), "The model should have a python_function flavor!" - # - output - assert schemas.OutputsSchema.check(outputs) is not None, "Outputs should be valid!" diff --git a/tests/test_scripts.py b/tests/test_scripts.py index 0170c4f..063a0f9 100644 --- a/tests/test_scripts.py +++ b/tests/test_scripts.py @@ -2,42 +2,47 @@ import json import os -import typing as T +import pydantic as pdt import pytest +from _pytest import capture as pc from bikes import scripts # %% SCRIPTS -def test_schema(capsys: T.Any) -> None: +def test_schema(capsys: pc.CaptureFixture[str]) -> None: # given - argv = ["prog", "--schema"] + args = ["prog", "--schema"] # when - scripts.main(argv) + scripts.main(args) captured = capsys.readouterr() # then - assert captured.err == "", "Capture error should be empty!" - assert json.loads(captured.out), "Capture output should be a valid JSON!" + assert captured.err == "", "Captured error should be empty!" + assert json.loads(captured.out), "Captured output should be a JSON!" @pytest.mark.parametrize( "scenario", [ - # passed "valid", - # failure - pytest.param("invalid", marks=pytest.mark.xfail), + pytest.param( + "invalid", + marks=pytest.mark.xfail( + reason="Invalid config.", + raises=pdt.ValidationError, + ), + ), ], ) def test_main(scenario: str, confs_path: str, extra_config: str) -> None: # given - root = os.path.join(confs_path, scenario) + folder = os.path.join(confs_path, scenario) + confs = list(sorted(os.listdir(folder))) # when - for path in sorted(os.listdir(root)): - config = os.path.join(root, path) + for conf in confs: # one job per config + config = os.path.join(folder, conf) argv = [config, "-e", extra_config] - print("Running main with:", argv) status = scripts.main(argv=argv) # then - assert status == 0, "Main should return 0!" + assert status == 0, f"Job should succeed for config: {config}" diff --git a/tests/test_services.py b/tests/test_services.py deleted file mode 100644 index a8b2796..0000000 --- a/tests/test_services.py +++ /dev/null @@ -1,69 +0,0 @@ -# %% IMPORTS - -import os -import typing as T - -import mlflow -import pytest -from bikes import services -from loguru import logger - -# %% SERVICES - - -def test_logger_service(capsys: T.Any) -> None: - # given - service = services.LoggerService(sink="stdout", level="INFO") - # when - service.start() - logger.info("INFO") - logger.debug("DEBUG") - # then - capture = capsys.readouterr() - assert capture.err == "", "No output to stderr!" - assert "INFO" in capture.out, "INFO should be logged!" - assert "DEBUG" not in capture.out, "Debug should not be logged!" - - -def test_carbon_service(tmp_carbon_path: str) -> None: - # given - output_dir = tmp_carbon_path - output_file = "emissions-test.csv" - output_path = os.path.join(output_dir, output_file) - service = services.CarbonService(output_dir=output_dir, output_file=output_file) - # when - service.start() - service.stop() - # then - assert os.path.exists(output_path), "Output path should be created!" - assert os.stat(output_path).st_size > 0, "Output path should not be empty!" - - -@pytest.mark.parametrize("enable_system_metrics", [True, False]) -def test_mlflow_service( - enable_system_metrics: bool, mlflow_service: services.MLflowService -) -> None: - # given - service = mlflow_service - service.enable_system_metrics = enable_system_metrics - # when - service.start() - client = service.client() - service.stop() # no effect - # then - # - mlflow - assert ( - mlflow_service.tracking_uri == mlflow.get_tracking_uri() - ), "Tracking URI should be the same!" - assert ( - mlflow_service.registry_uri == mlflow.get_registry_uri() - ), "Registry URI should be the same!" - assert mlflow.get_experiment_by_name( - mlflow_service.experiment_name - ), "Experiment should be setup!" - # - client - assert mlflow_service.tracking_uri == client.tracking_uri, "Tracking URI should be the same!" - assert mlflow_service.registry_uri == client._registry_uri, "Tracking URI should be the same!" - assert client.get_experiment_by_name( - mlflow_service.experiment_name - ), "Experiment should be setup!" diff --git a/tests/test_searchers.py b/tests/utils/test_searchers.py similarity index 52% rename from tests/test_searchers.py rename to tests/utils/test_searchers.py index 161a61c..a39004c 100644 --- a/tests/test_searchers.py +++ b/tests/utils/test_searchers.py @@ -1,31 +1,28 @@ # %% IMPORTS -from bikes import metrics, models, schemas, searchers, splitters +from bikes.core import metrics, models, schemas +from bikes.utils import searchers, splitters # %% SEARCHERS def test_grid_cv_searcher( - default_model: models.Model, - default_metric: metrics.Metric, - time_series_splitter: splitters.Splitter, + model: models.Model, + metric: metrics.Metric, inputs: schemas.Inputs, targets: schemas.Targets, + train_test_splitter: splitters.TrainTestSplitter, ) -> None: # given param_grid = {"max_depth": [3, 5, 7]} searcher = searchers.GridCVSearcher(param_grid=param_grid) # when result, best_score, best_params = searcher.search( - model=default_model, - metric=default_metric, - cv=time_series_splitter, - inputs=inputs, - targets=targets, + model=model, metric=metric, inputs=inputs, targets=targets, cv=train_test_splitter ) # then assert set(best_params) == set(param_grid), "Best params should have the same keys as grid!" - assert float("-inf") <= best_score <= float("+inf"), "Best score should be a floating number!" + assert float("-inf") < best_score < float("+inf"), "Best score should be a floating number!" assert len(result) == sum( - map(len, param_grid.values()) + len(vs) for vs in param_grid.values() ), "Results should have one row per candidate!" diff --git a/tests/utils/test_signers.py b/tests/utils/test_signers.py new file mode 100644 index 0000000..515a448 --- /dev/null +++ b/tests/utils/test_signers.py @@ -0,0 +1,20 @@ +# %% IMPORTS + +from bikes.core import schemas +from bikes.utils import signers + +# %% SIGNERS + + +def test_infer_signer(inputs: schemas.Inputs, outputs: schemas.Outputs) -> None: + # given + signer = signers.InferSigner() + # when + signature = signer.sign(inputs=inputs, outputs=outputs) + # then + assert set(signature.inputs.input_names()) == set( + inputs.columns + ), "Signature inputs should contain input column names." + assert set(signature.outputs.input_names()) == set( + outputs.columns + ), "Signature outputs should contain output column names." diff --git a/tests/test_splitters.py b/tests/utils/test_splitters.py similarity index 85% rename from tests/test_splitters.py rename to tests/utils/test_splitters.py index 46a0ea6..1f628f0 100644 --- a/tests/test_splitters.py +++ b/tests/utils/test_splitters.py @@ -1,13 +1,14 @@ # %% IMPORTS -from bikes import schemas, splitters +from bikes.core import schemas +from bikes.utils import splitters # %% SPLITTERS def test_train_test_splitter(inputs: schemas.Inputs, targets: schemas.Targets) -> None: # given - shuffle = True + shuffle = False test_size = 50 random_state = 0 splitter = splitters.TrainTestSplitter( @@ -16,15 +17,15 @@ def test_train_test_splitter(inputs: schemas.Inputs, targets: schemas.Targets) - # when n_splits = splitter.get_n_splits(inputs=inputs, targets=targets) splits = list(splitter.split(inputs=inputs, targets=targets)) - train_index, test_index = splits[0] # indexes + train_index, test_index = splits[0] # train/test indexes # then assert n_splits == len(splits) == 1, "Splitter should return 1 split!" - assert len(test_index) == test_size, "Test index should have the given test size!" + assert len(test_index) == test_size, "Test index should have the given size!" assert ( - len(train_index) == len(inputs) - test_size + len(train_index) == len(targets) - test_size ), "Train index should have the remaining size!" assert not inputs.iloc[test_index].empty, "Test index should be a subset of the inputs!" - assert not inputs.iloc[train_index].empty, "Train index should be a subset of the inputs!" + assert not targets.iloc[train_index].empty, "Train index should be a subset of the targets!" def test_time_series_splitter(inputs: schemas.Inputs, targets: schemas.Targets) -> None: