From 0b0248b75a450167029f3d1d5384b345d3352f9b Mon Sep 17 00:00:00 2001 From: Alexey Zankevich Date: Wed, 11 Dec 2024 20:24:00 -0500 Subject: [PATCH] Output label transformer support (#8) --- orient_express/model_wrapper.py | 40 +- orient_express/sklearn_pipeline.py | 81 ++ poetry.lock | 1746 +++++++++++++++++++++++++++- pyproject.toml | 3 +- tests/conftest.py | 6 + tests/test_sklearn_pipeline.py | 181 +++ 6 files changed, 2029 insertions(+), 28 deletions(-) create mode 100644 orient_express/sklearn_pipeline.py create mode 100644 tests/conftest.py create mode 100644 tests/test_sklearn_pipeline.py diff --git a/orient_express/model_wrapper.py b/orient_express/model_wrapper.py index 6a7696d..f48142d 100644 --- a/orient_express/model_wrapper.py +++ b/orient_express/model_wrapper.py @@ -1,4 +1,6 @@ import os +from typing import Optional + import joblib import logging import pandas as pd @@ -9,20 +11,20 @@ class ModelExpress: def __init__( self, - model_name, - project_name, - bucket_name, - model_version=None, - model=None, - region="us-central1", - serialized_model_path="model.joblib", - serving_container_image_uri="us-west1-docker.pkg.dev/shiftsmart-api/orient-express/xgboost-scikit-learn:latest", - serving_container_predict_route="/v1/models/orient-express-model:predict", - serving_container_health_route="/v1/models/orient-express-model", - endpoint_name=None, + model_name: str, + project_name: str, + bucket_name: str, + model_version: Optional[int] = None, + model: object = None, + region: str = "us-central1", + serialized_model_path: str = "model.joblib", + serving_container_image_uri: str = "us-west1-docker.pkg.dev/shiftsmart-api/orient-express/xgboost-scikit-learn:latest", + serving_container_predict_route: str = "/v1/models/orient-express-model:predict", + serving_container_health_route: str = "/v1/models/orient-express-model", + endpoint_name: Optional[str] = None, machine_type="n1-standard-4", - min_replica_count=1, - max_replica_count=1, + min_replica_count: int = 1, + max_replica_count: int = 1, ): self.model = model self.model_name = model_name @@ -56,7 +58,7 @@ def _vertex_init(self): aiplatform.init(project=self.project_name, location=self.region) self._vertex_initialized = True - def get_latest_vertex_model(self, model_name): + def get_latest_vertex_model(self, model_name: str): """If there are a few models with the same name, load the most recent one. It's highly recommended to keep only 1 model with the same name to avoid the confusion """ @@ -96,7 +98,7 @@ def upload(self): return self.create_model_version(new_version, last_model) - def get_artifacts_path(self, version, file_name=None): + def get_artifacts_path(self, version: int, file_name: str = None): dir_name = f"models/{self.model_name}/{version}" if file_name: return f"{dir_name}/{file_name}" @@ -159,7 +161,7 @@ def deploy(self): traffic_percentage=100, ) - def remote_predict(self, input_df): + def remote_predict(self, input_df: pd.DataFrame): self._vertex_init() if not self.endpoint: @@ -174,7 +176,7 @@ def remote_predict(self, input_df): predictions = self.endpoint.predict(instances=instances) return predictions.predictions - def local_predict(self, input_df): + def local_predict(self, input_df: pd.DataFrame): self._vertex_init() if not self.model: @@ -182,7 +184,7 @@ def local_predict(self, input_df): return self.model.predict(input_df) - def local_predict_proba(self, input_df): + def local_predict_proba(self, input_df: pd.DataFrame): if not self.model: self.load_model_from_registry() @@ -207,7 +209,7 @@ def load_model_from_registry(self): self.model = joblib.load(self.serialized_model_path) - def download_artifacts(self, artifact_uri): + def download_artifacts(self, artifact_uri: str): storage_client = storage.Client() bucket_name, artifact_path = artifact_uri.replace("gs://", "").split("/", 1) bucket = storage_client.bucket(bucket_name) diff --git a/orient_express/sklearn_pipeline.py b/orient_express/sklearn_pipeline.py new file mode 100644 index 0000000..e9d693e --- /dev/null +++ b/orient_express/sklearn_pipeline.py @@ -0,0 +1,81 @@ +from sklearn.base import BaseEstimator, TransformerMixin +from sklearn.preprocessing import LabelEncoder + + +class LabelEncoderTransformer(BaseEstimator, TransformerMixin): + """ + A wrapper class that integrates a model and a label encoder to handle + encoded predictions and their corresponding probabilities. + + Attributes: + model (object): A machine learning model with `fit`, `predict`, and `predict_proba` methods. + label_encoder (object): A label encoder that maps encoded class labels to their original string labels. + + Methods: + fit(X, y): + Trains the model on the provided features and labels. + predict(X): + Predicts the classes for the given features and returns the original string labels. + predict_proba(X): + Returns the probabilities for each class for the given features, along with their original class labels. + """ + + def __init__(self, model: BaseEstimator, label_encoder: LabelEncoder): + """ + Initializes the LabelEncoderTransformer. + + Args: + model (BaseEstimator): A machine learning model. + label_encoder (LabelEncoder): A label encoder with `fit` and `inverse_transform` methods. + """ + self.model = model + self.label_encoder = label_encoder + + def fit(self, X, y): + """ + Fits the model to the training data. + + Args: + X (array-like): Feature matrix. + y (array-like): Target labels. + + Returns: + self: Fitted LabelEncoderTransformer instance. + """ + self.model.fit(X, y) + return self + + def predict(self, X): + """ + Predicts the target labels and returns the original string labels. + + Args: + X (array-like): Feature matrix for predictions. + + Returns: + array-like: Predicted class labels in their original form. + """ + encoded_predictions = self.model.predict(X) + return self.label_encoder.inverse_transform(encoded_predictions) + + def predict_proba(self, X): + """ + Returns class probabilities along with their original labels. + + Args: + X (array-like): Feature matrix for predictions. + + Returns: + list: A list of lists, where each inner list contains `[class_name, probability]` pairs. + """ + # Get raw probabilities + probabilities = self.model.predict_proba(X) + + # Get all class labels + class_names = self.label_encoder.classes_ + # Combine class names with probabilities + combined_output = [ + [[class_name, prob] for class_name, prob in 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+python-versions = "*" +files = [ + {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"}, + {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"}, +] + [[package]] name = "websocket-client" version = "1.8.0" @@ -2738,6 +4438,17 @@ files = [ {file = "websockets-13.1.tar.gz", hash = "sha256:a3b3366087c1bc0a2795111edcadddb8b3b59509d5db5d7ea3fdd69f954a8878"}, ] +[[package]] +name = "widgetsnbextension" +version = "4.0.13" +description = "Jupyter interactive widgets for Jupyter Notebook" +optional = false +python-versions = ">=3.7" +files = [ + {file = "widgetsnbextension-4.0.13-py3-none-any.whl", hash = "sha256:74b2692e8500525cc38c2b877236ba51d34541e6385eeed5aec15a70f88a6c71"}, + {file = "widgetsnbextension-4.0.13.tar.gz", hash = "sha256:ffcb67bc9febd10234a362795f643927f4e0c05d9342c727b65d2384f8feacb6"}, +] + [[package]] name = "xgboost" version = "2.1.2" @@ -2864,7 +4575,26 @@ idna = ">=2.0" multidict = ">=4.0" propcache = ">=0.2.0" +[[package]] +name = "zipp" +version = "3.21.0" +description = "Backport of pathlib-compatible object wrapper for zip files" +optional = false +python-versions = ">=3.9" +files = [ + {file = "zipp-3.21.0-py3-none-any.whl", hash = "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931"}, + {file = "zipp-3.21.0.tar.gz", hash = "sha256:2c9958f6430a2040341a52eb608ed6dd93ef4392e02ffe219417c1b28b5dd1f4"}, +] + +[package.extras] +check = ["pytest-checkdocs (>=2.4)", "pytest-ruff (>=0.2.1)"] +cover = ["pytest-cov"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +enabler = ["pytest-enabler (>=2.2)"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-ignore-flaky"] +type = ["pytest-mypy"] + [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "c5de56e90b72f012fc67d683edce252ee616be2481e8a9707b62a54784be8591" +content-hash = "4cabc149f82e6a097236207e7943ccd074746ac61c87faef2701afe7ebc04eb5" diff --git a/pyproject.toml b/pyproject.toml index 4c8b822..27a7e24 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "orient_express" -version = "0.3.3" +version = "0.3.4" description = "A library to simplify model deployment to Vertex AI" authors = ["Alexey Zankevich "] readme = "README.md" @@ -10,6 +10,7 @@ python = ">=3.9,<3.13" google-cloud-aiplatform = "*" google-cloud-storage = "*" pandas = "*" +jupyter = "^1.1.1" [tool.poetry.dev-dependencies] black = "24.10.0" diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..17d8873 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,6 @@ +import pytest + + +@pytest.fixture +def default_seed(): + return 42 diff --git a/tests/test_sklearn_pipeline.py b/tests/test_sklearn_pipeline.py new file mode 100644 index 0000000..65f1d9e --- /dev/null +++ b/tests/test_sklearn_pipeline.py @@ -0,0 +1,181 @@ +import pytest +import numpy as np +from sklearn.preprocessing import LabelEncoder +from sklearn.ensemble import RandomForestClassifier +from sklearn.datasets import make_classification +from orient_express.sklearn_pipeline import ( + LabelEncoderTransformer, +) # Replace with your actual module name + + +@pytest.fixture +def sample_data(default_seed): + X, y = make_classification( + n_samples=50, + n_features=4, + n_clusters_per_class=1, + n_classes=2, + random_state=default_seed, + ) + return X, np.array([str(f"label-{item}") for item in y]) + + +@pytest.fixture +def label_encoder(sample_data): + X, y_labels = sample_data + + # Generate synthetic data for testing + label_encoder = LabelEncoder() + label_encoder.fit(y_labels) + + return label_encoder + + +@pytest.fixture +def y_as_ids(label_encoder, sample_data): + X, y_labels = sample_data + return label_encoder.transform(y_labels) + + +@pytest.fixture +def transformer(label_encoder, default_seed): + model = RandomForestClassifier(random_state=default_seed) + return LabelEncoderTransformer(model=model, label_encoder=label_encoder) + + +def test_fit(transformer, sample_data, y_as_ids): + X, y_labels = sample_data + + # Fit the transformer + transformer.fit(X, y_as_ids) + + # Assert that the model is fitted (e.g., has feature importances) + assert hasattr(transformer.model, "feature_importances_") + + +@pytest.fixture +def expected_labels(): + return np.array( + [ + "label-1", + "label-1", + "label-1", + "label-0", + "label-0", + "label-1", + "label-1", + "label-0", + "label-0", + "label-0", + "label-1", + "label-1", + "label-0", + "label-0", + "label-0", + "label-1", + "label-0", + "label-1", + "label-1", + "label-1", + "label-0", + "label-1", + "label-0", + "label-0", + "label-1", + "label-1", + "label-1", + "label-0", + "label-0", + "label-1", + "label-0", + "label-0", + "label-1", + "label-1", + "label-0", + "label-1", + "label-1", + "label-1", + "label-0", + "label-0", + "label-1", + "label-1", + "label-0", + "label-0", + "label-0", + "label-0", + "label-0", + "label-1", + "label-1", + "label-0", + ] + ) + + +def test_predict(transformer, sample_data, y_as_ids, label_encoder, expected_labels): + X, y_labels = sample_data + + transformer.fit(X, y_as_ids) + predictions = transformer.predict(X) + + np.testing.assert_array_equal(predictions, expected_labels) + + +def test_predict_proba(transformer, y_as_ids, label_encoder, sample_data): + X, y_labels = sample_data + + transformer.fit(X, y_as_ids) + probabilities = transformer.predict_proba(X) + # Ensure probabilities match the expected structure + + assert probabilities == [ + [["label-0", 0.04], ["label-1", 0.96]], + [["label-0", 0.03], ["label-1", 0.97]], + [["label-0", 0.22], ["label-1", 0.78]], + [["label-0", 0.99], ["label-1", 0.01]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.08], ["label-1", 0.92]], + [["label-0", 0.07], ["label-1", 0.93]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.66], ["label-1", 0.34]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 0.99], ["label-1", 0.01]], + [["label-0", 0.01], ["label-1", 0.99]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 0.01], ["label-1", 0.99]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.02], ["label-1", 0.98]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.02], ["label-1", 0.98]], + [["label-0", 0.04], ["label-1", 0.96]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 0.21], ["label-1", 0.79]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.98], ["label-1", 0.02]], + [["label-0", 1.0], ["label-1", 0.0]], + [["label-0", 0.99], ["label-1", 0.01]], + [["label-0", 0.0], ["label-1", 1.0]], + [["label-0", 0.08], ["label-1", 0.92]], + [["label-0", 1.0], ["label-1", 0.0]], + ]