diff --git a/.gitignore b/.gitignore index 0a79a60..e91c990 100644 --- a/.gitignore +++ b/.gitignore @@ -11,6 +11,8 @@ __pycache__/ *.json *.pkl +# Autogluon +AutogluonModels/ # ignore mlflow folder mlruns/ diff --git a/examples/delay/configs/base.json b/examples/delay/configs/base.json index 113cc1d..79436a9 100644 --- a/examples/delay/configs/base.json +++ b/examples/delay/configs/base.json @@ -7,14 +7,16 @@ "target": "Delay", "task": "classification", "columns_to_ignore_for_training": [ - "FlightID" + "FlightID", + "FlightNumber" ] }, "steps": [ { "step_type": "GenerateStep", "parameters": { - "train_path": "examples/delay/data/train.csv" + "train_path": "examples/delay/data/train.csv", + "predict_path": "examples/delay/data/test.csv" } }, { diff --git a/examples/delay/data/test.csv b/examples/delay/data/test.csv new file mode 100644 index 0000000..6b68b42 --- /dev/null +++ b/examples/delay/data/test.csv @@ -0,0 +1,11 @@ +FlightID,Airline,FlightNumber,Origin,Destination,ScheduledDeparture,ScheduledArrival,Distance,Weather +101,NK,101,FLL,LGA,2024-07-10 07:00:00,2024-07-10 10:00:00,1076,Clear +102,B6,202,BOS,SFO,2024-07-10 08:30:00,2024-07-10 12:30:00,2704,Fog +103,AS,303,SEA,ANC,2024-07-10 10:00:00,2024-07-10 12:30:00,1448,Rain +104,F9,404,LAS,MCO,2024-07-10 11:30:00,2024-07-10 19:00:00,2035,Clear +105,NK,505,ORD,MSY,2024-07-10 13:00:00,2024-07-10 15:30:00,837,Thunderstorm +106,B6,606,JFK,AUS,2024-07-10 14:30:00,2024-07-10 17:30:00,1521,Clear +107,AS,707,PDX,HNL,2024-07-10 16:00:00,2024-07-10 19:30:00,2603,Windy +108,F9,808,DEN,SAN,2024-07-10 17:30:00,2024-07-10 19:00:00,853,Clear +109,NK,909,DTW,PHX,2024-07-10 19:00:00,2024-07-10 20:30:00,1671,Hail +110,B6,1010,BOS,MCO,2024-07-10 20:30:00,2024-07-10 23:30:00,1121,Clear \ No newline at end of file diff --git a/ml_garden/__init__.py b/ml_garden/__init__.py index 36902c5..aa30bc3 100644 --- a/ml_garden/__init__.py +++ b/ml_garden/__init__.py @@ -1,6 +1,4 @@ from .core.pipeline import Pipeline Pipeline.step_registry.auto_register_steps_from_package("ml_garden.core.steps") -Pipeline.model_registry.auto_register_models_from_package( - "ml_garden.implementation.tabular.xgboost" -) +Pipeline.model_registry.auto_register_models_from_package("ml_garden.implementation.tabular") diff --git a/ml_garden/core/data_container.py b/ml_garden/core/data_container.py index e7c9809..4139323 100644 --- a/ml_garden/core/data_container.py +++ b/ml_garden/core/data_container.py @@ -675,6 +675,31 @@ def predictions(self, value: pd.Series): """ self["predictions"] = value + @property + def predict_proba(self) -> pd.DataFrame: + """ + Get the prediction probabilities from the DataContainer. + + Returns + ------- + pd.DataFrame + The prediction probabilities stored in the DataContainer. + """ + return self["predict_proba"] + + @predict_proba.setter + def predict_proba(self, value: pd.DataFrame): + """ + Set the prediction probabilities in the DataContainer. + + Parameters + ---------- + value : pd.DataFrame + The prediction probabilities to be stored in the DataContainer. + Should be a DataFrame with a column for each class. + """ + self["predict_proba"] = value + @property def explainer(self) -> BaseExplainer: """ diff --git a/ml_garden/core/model.py b/ml_garden/core/model.py index 7513b97..c36c353 100644 --- a/ml_garden/core/model.py +++ b/ml_garden/core/model.py @@ -24,3 +24,19 @@ def fit( @abstractmethod def predict(self, X: pd.DataFrame) -> pd.Series: """Abstract method for making predictions.""" + + def predict_proba(self, X: pd.DataFrame) -> pd.DataFrame: + """ + Predict class probabilities with the trained model. + + Parameters + ---------- + X : pd.DataFrame + Features to make probability predictions on. + + Returns + ------- + pd.DataFrame + Predicted class probabilities for the input features. + """ + pass diff --git a/ml_garden/core/model_registry.py b/ml_garden/core/model_registry.py index 409f268..cb51eb9 100644 --- a/ml_garden/core/model_registry.py +++ b/ml_garden/core/model_registry.py @@ -1,26 +1,71 @@ import importlib import logging import pkgutil +from typing import Dict, Type from ml_garden.core.model import Model class ModelClassNotFoundError(Exception): + """Exception raised when a model class is not found in the registry.""" + pass class ModelRegistry: def __init__(self): - self._model_registry = {} + """ + Initialize a new ModelRegistry instance. + + Attributes + ---------- + _model_registry : dict + A dictionary mapping model names to model classes. + logger : logging.Logger + Logger for the class. + """ + self._model_registry: Dict[str, Type[Model]] = {} self.logger = logging.getLogger(__name__) - def register_model(self, model_class: type): - model_name = model_class.__name__ + def register_model(self, model_class: Type[Model]) -> None: + """ + Register a model class in the registry. + + Parameters + ---------- + model_class : Type[Model] + The model class to be registered. + + Raises + ------ + ValueError + If the model_class is not a subclass of Model. + """ + model_name = model_class.__name__.lower() if not issubclass(model_class, Model): raise ValueError(f"{model_class} must be a subclass of Model") self._model_registry[model_name] = model_class - def get_model_class(self, model_name: str) -> type: + def get_model_class(self, model_name: str) -> Type[Model]: + """ + Retrieve a model class from the registry. + + Parameters + ---------- + model_name : str + The name of the model class to retrieve. + + Returns + ------- + Type[Model] + The model class. + + Raises + ------ + ModelClassNotFoundError + If the model class is not found in the registry. + """ + model_name = model_name.lower() if model_name in self._model_registry: return self._model_registry[model_name] else: @@ -29,10 +74,31 @@ def get_model_class(self, model_name: str) -> type: f" {list(self._model_registry.keys())}" ) - def get_all_model_classes(self) -> dict: + def get_all_model_classes(self) -> Dict[str, Type[Model]]: + """ + Get all registered model classes. + + Returns + ------- + dict + A dictionary of all registered model classes. + """ return self._model_registry - def auto_register_models_from_package(self, package_name: str): + def auto_register_models_from_package(self, package_name: str) -> None: + """ + Automatically register all model classes from a given package. + + Parameters + ---------- + package_name : str + The name of the package to search for model classes. + + Raises + ------ + ImportError + If the package cannot be imported. + """ try: package = importlib.import_module(package_name) prefix = package.__name__ + "." diff --git a/ml_garden/core/steps/calculate_features.py b/ml_garden/core/steps/calculate_features.py index 8575d61..c434cc1 100644 --- a/ml_garden/core/steps/calculate_features.py +++ b/ml_garden/core/steps/calculate_features.py @@ -65,7 +65,7 @@ def __init__( f" features: {list(self.feature_extractors.keys())}" ) - def _convert_column_to_datetime(self, df: pd.DataFrame, column: str) -> pd.DataFrame: + def _convert_column_to_datetime(self, df: pd.DataFrame, column: str, log: bool) -> pd.DataFrame: """Convert a column to datetime. Parameters ---------- @@ -73,6 +73,8 @@ def _convert_column_to_datetime(self, df: pd.DataFrame, column: str) -> pd.DataF The DataFrame containing the column to convert column : str The name of the column to convert + log: bool + If True, logs information. Returns ------- pd.DataFrame @@ -85,14 +87,15 @@ def _convert_column_to_datetime(self, df: pd.DataFrame, column: str) -> pd.DataF df[column], errors="raise", ) - self.logger.info(f"Column '{column}' automatically converted to datetime.") + if log: + self.logger.info(f"Column '{column}' automatically converted to datetime.") except ValueError as e: self.logger.error(f"Error converting column '{column}' to datetime: {e}") except Exception as e: self.logger.error(f"Unexpected error converting column '{column}' to datetime: {e}") else: - self.logger.debug(f"Column '{column}' is already a datetime type.") - + if log: + self.logger.debug(f"Column '{column}' is already a datetime type.") return df def _extract_feature(self, df: pd.DataFrame, column: str, feature: str) -> None: @@ -122,6 +125,14 @@ def _extract_feature(self, df: pd.DataFrame, column: str, feature: str) -> None: ) raise ValueError(error_message) + def _drop_datetime_columns(self, df: pd.DataFrame, log: bool) -> pd.DataFrame: + """Drop the datetime columns from the `df`.""" + if self.datetime_columns: + if log: + self.logger.info(f"Dropping original datetime columns: {self.datetime_columns}") + return df.drop(columns=self.datetime_columns) + return df + def execute(self, data: DataContainer) -> DataContainer: """Execute the step. Parameters @@ -135,21 +146,18 @@ def execute(self, data: DataContainer) -> DataContainer: """ self.logger.info("Calculating features") - if not data.is_train: - data.flow = self._create_datetime_features(data.flow, log=True) + datasets = [ + ("X_prediction", data.X_prediction, True), + ("X_train", data.X_train, True), + ("X_validation", data.X_validation, False), + ("X_test", data.X_test, False), + ] - if data.train is not None: - data.train = self._create_datetime_features(data.train, log=True) - - if data.validation is not None: - data.validation = self._create_datetime_features(data.validation) - - if data.test is not None: - data.test = self._create_datetime_features(data.test) - - ## add datetime columns to ignore columns for training - if self.datetime_columns: - data.columns_to_ignore_for_training.extend(self.datetime_columns) + for attr_name, dataset, should_log in datasets: + if dataset is not None: + dataset = self._create_datetime_features(dataset, log=should_log) + dataset = self._drop_datetime_columns(dataset, log=should_log) + setattr(data, attr_name, dataset) return data @@ -173,7 +181,7 @@ def _create_datetime_features( if self.datetime_columns: for column in self.datetime_columns: if column in df.columns: - df = self._convert_column_to_datetime(df, column) + df = self._convert_column_to_datetime(df, column, log) if self.features: for feature in self.features: @@ -191,4 +199,7 @@ def _create_datetime_features( if log: self.logger.warning("No datetime columns specified. Skipping feature extraction.") + if log: + self.logger.info(f"Created new features: {self.features}") + return df diff --git a/ml_garden/core/steps/encode.py b/ml_garden/core/steps/encode.py index 033fca3..c17c6ee 100644 --- a/ml_garden/core/steps/encode.py +++ b/ml_garden/core/steps/encode.py @@ -51,54 +51,44 @@ def execute(self, data: DataContainer) -> DataContainer: """Execute the encoding step.""" self.logger.info("Encoding data") - target_column_name = data.target - if not data.is_train: - categorical_features, numeric_features = self._get_feature_types( - data.flow.drop(columns=data.columns_to_ignore_for_training), data.target - ) + categorical_features, numeric_features = self._get_feature_types(data.X_prediction) data.X_prediction, _, _ = self._apply_encoding( - data.flow, - target_column_name, - data.columns_to_ignore_for_training, - categorical_features, - numeric_features, + X=data.X_prediction, + y=None, + categorical_features=categorical_features, + numeric_features=numeric_features, saved_encoder=data._encoder, log=True, ) return data - categorical_features, numeric_features = self._get_feature_types( - data.train.drop(columns=data.columns_to_ignore_for_training), target_column_name - ) + categorical_features, numeric_features = self._get_feature_types(data.X_train) data.X_train, data.y_train, data._encoder = self._apply_encoding( - data.train, - target_column_name, - data.columns_to_ignore_for_training, - categorical_features, - numeric_features, + X=data.X_train, + y=data.y_train, + categorical_features=categorical_features, + numeric_features=numeric_features, fit_encoders=True, log=True, ) if data.validation is not None: data.X_validation, data.y_validation, _ = self._apply_encoding( - data.validation, - target_column_name, - data.columns_to_ignore_for_training, - categorical_features, - numeric_features, + X=data.X_validation, + y=data.y_validation, + categorical_features=categorical_features, + numeric_features=numeric_features, saved_encoder=data._encoder, ) if data.test is not None: data.X_test, data.y_test, _ = self._apply_encoding( - data.test, - target_column_name, - data.columns_to_ignore_for_training, - categorical_features, - numeric_features, + X=data.X_test, + y=data.y_test, + categorical_features=categorical_features, + numeric_features=numeric_features, saved_encoder=data._encoder, ) @@ -106,9 +96,8 @@ def execute(self, data: DataContainer) -> DataContainer: def _apply_encoding( self, - df: pd.DataFrame, - target_column_name: str, - columns_to_ignore_for_training: List[str], + X: pd.DataFrame, + y: pd.Series, categorical_features: List[str], numeric_features: List[str], fit_encoders: bool = False, @@ -119,12 +108,10 @@ def _apply_encoding( Parameters ---------- - df : pd.DataFrame - The DataFrame to encode - target_column_name : str - The target column name - columns_to_ignore_for_training : List[str] - Columns to ignore for training + X : pd.DataFrame + The DataFrame with features to encode + y : pd.Series + The target series categorical_features : List[str] Categorical features numeric_features : List[str] @@ -144,12 +131,10 @@ def _apply_encoding( if not fit_encoders and not saved_encoder: raise ValueError("saved_encoder must be provided when fit_encoders is False.") - df = df.drop(columns=columns_to_ignore_for_training) - low_cardinality_features, high_cardinality_features = self._split_categorical_features( - df, categorical_features + X, categorical_features ) - original_numeric_dtypes = {col: df[col].dtype for col in numeric_features} + original_numeric_dtypes = {col: X[col].dtype for col in numeric_features} if fit_encoders: # Save the encoder for prediction @@ -163,13 +148,13 @@ def _apply_encoding( assert encoder is not None encoded_data, targets = self._transform_data( - df, - target_column_name, + X, + y, encoder, fit_encoders, ) - encoded_data = self._restore_column_order(df, encoded_data) + encoded_data = self._restore_column_order(X, encoded_data) encoded_data = self._restore_numeric_dtypes(encoded_data, original_numeric_dtypes) encoded_data = self._convert_float64_to_float32(encoded_data) @@ -189,20 +174,20 @@ def _apply_encoding( return encoded_data, targets, encoder - def _get_feature_types( - self, df: pd.DataFrame, target_column_name: str - ) -> Tuple[List[str], List[str]]: - """Get categorical and numeric feature lists.""" - categorical_features = [ - col - for col in df.columns - if df[col].dtype in ["object", "category"] and col != target_column_name - ] - numeric_features = [ - col - for col in df.columns - if col not in categorical_features and col != target_column_name - ] + def _get_feature_types(self, X: pd.DataFrame) -> Tuple[List[str], List[str]]: + """Get categorical and numeric feature lists. + Parameters + ---------- + X : pd.DataFrame + The features DataFrame + + Returns + ------- + Tuple[List[str], List[str]] + Categorical and numeric features + """ + categorical_features = [col for col in X.columns if X[col].dtype in ["object", "category"]] + numeric_features = [col for col in X.columns if col not in categorical_features] return categorical_features, numeric_features @@ -297,19 +282,27 @@ def _create_column_transformer( def _transform_data( self, - df: pd.DataFrame, - target_column_name: str, + X: pd.DataFrame, + y: pd.Series, column_transformer: ColumnTransformer, is_train: bool = False, ) -> tuple[pd.DataFrame, Optional[pd.Series]]: - """Transform the data using the ColumnTransformer.""" - if target_column_name in df.columns: - X = df.drop(columns=[target_column_name]) # Drop the target column - y = df[target_column_name] # Target column for training data - else: - X = df # All columns for prediction data - y = None # No target in the prediction data - + """Transform the data using the ColumnTransformer. + Parameters + ---------- + X : pd.DataFrame + The DataFrame with features to transform + y : pd.Series + The target series + column_transformer : ColumnTransformer + The ColumnTransformer + is_train : bool, optional + Whether the data is for training, by default False + Returns + ------- + tuple[pd.DataFrame, Optional[pd.Series]] + The transformed data and the target column + """ if is_train: self.logger.info("Fitting encoders") column_transformer.fit(X, y) @@ -318,7 +311,7 @@ def _transform_data( self.logger.debug(f"Transformed data shape: {transformed_data.shape}") return ( pd.DataFrame( - transformed_data, columns=column_transformer.get_feature_names_out(), index=df.index + transformed_data, columns=column_transformer.get_feature_names_out(), index=X.index ), y, ) diff --git a/ml_garden/core/steps/fit_model.py b/ml_garden/core/steps/fit_model.py index f57de08..9437c4b 100644 --- a/ml_garden/core/steps/fit_model.py +++ b/ml_garden/core/steps/fit_model.py @@ -4,6 +4,7 @@ import numpy as np import optuna +import pandas as pd from sklearn.metrics import ( accuracy_score, f1_score, @@ -300,4 +301,14 @@ def predict(self, data: DataContainer) -> DataContainer: self.logger.info(f"Predicting with {self.model_class.__name__} model") data.flow[data.prediction_column] = data.model.predict(data.X_prediction) data.predictions = data.flow[data.prediction_column] + + # If the task is classification, also get the prediction probabilities + if data.task == Task.CLASSIFICATION: + proba_df = data.model.predict_proba(data.X_prediction) + proba_df.columns = [f"proba_{col}" for col in proba_df.columns] + + # Concatenate the probabilities DataFrame with the existing DataFrame + data.flow = pd.concat([data.flow, proba_df], axis=1) + data.predict_proba = proba_df + return data diff --git a/ml_garden/core/steps/tabular_split.py b/ml_garden/core/steps/tabular_split.py index 7a24b63..316fea5 100644 --- a/ml_garden/core/steps/tabular_split.py +++ b/ml_garden/core/steps/tabular_split.py @@ -1,5 +1,5 @@ from math import isclose -from typing import Optional +from typing import Optional, Tuple import pandas as pd from sklearn.model_selection import train_test_split @@ -13,7 +13,7 @@ class TabularSplitStep(PipelineStep): """Split the data.""" - used_for_prediction = False + used_for_prediction = True used_for_training = True def __init__( @@ -81,6 +81,39 @@ def __init__( "validation_percentage must be provided when test_percentage is specified." ) + def _set_X_y(self, data: DataContainer) -> None: + """ + Set X and y for train, validation, and test sets. + + This method sets X_train, y_train, X_validation, y_validation, X_test, and y_test + in the DataContainer object. + + Parameters + ---------- + data : DataContainer + The DataContainer object containing the split data. + + Returns + ------- + None + """ + + def split_X_y(df: pd.DataFrame) -> Tuple[pd.DataFrame, pd.Series]: + df = df.drop(columns=data.columns_to_ignore_for_training) + if data.target is None: + return df, None + return df.drop(columns=[data.target]), df[data.target] + + data.X_train, data.y_train = split_X_y(data.train) + data.X_validation, data.y_validation = split_X_y(data.validation) + + if data.test is not None: + data.X_test, data.y_test = split_X_y(data.test) + else: + data.X_test, data.y_test = None, None + + self.logger.debug("X and y sets have been created for train, validation, and test data.") + def execute(self, data: DataContainer) -> DataContainer: """ Execute the random train-validation-test split. @@ -96,6 +129,13 @@ def execute(self, data: DataContainer) -> DataContainer: Where df is the DataFrame used as input to the SplitStep """ + if not data.is_train: + data.X_prediction = data.flow + if data.columns_to_ignore_for_training: + data.X_prediction = data.X_prediction.drop( + columns=data.columns_to_ignore_for_training + ) + return data self.logger.info("Splitting tabular data...") df = data.flow @@ -240,4 +280,7 @@ def execute(self, data: DataContainer) -> DataContainer: f"Number of rows in test set: {test_rows} | {test_rows / total_rows:.2%}" ) + # Set X and y for train, validation and test datasets + self._set_X_y(data) + return data diff --git a/ml_garden/implementation/tabular/autogluon/__init__.py b/ml_garden/implementation/tabular/autogluon/__init__.py new file mode 100644 index 0000000..96aee68 --- /dev/null +++ b/ml_garden/implementation/tabular/autogluon/__init__.py @@ -0,0 +1 @@ +from .model import AutoGluon # noqa: F401 diff --git a/ml_garden/implementation/tabular/autogluon/model.py b/ml_garden/implementation/tabular/autogluon/model.py new file mode 100644 index 0000000..88da3a7 --- /dev/null +++ b/ml_garden/implementation/tabular/autogluon/model.py @@ -0,0 +1,42 @@ +import logging + +import pandas as pd +from autogluon.tabular import TabularPredictor + +from ml_garden.core.constants import Task +from ml_garden.core.model import Model + +logger = logging.getLogger(__file__) + + +class AutoGluon(Model): + TASKS = [Task.REGRESSION, Task.CLASSIFICATION] + + def __init__(self, **params): + self.params = params + self.model: TabularPredictor = None + + def fit(self, X: pd.DataFrame, y: pd.Series, eval_set=None, verbose=True) -> None: + train_data = X.copy() + train_data["label"] = y + if eval_set is not None and len(eval_set) > 0: + eval_X, eval_y = eval_set[0] + val_data = eval_X.copy() + val_data["label"] = eval_y + self.model = TabularPredictor(label="label", verbosity=3 if verbose else 0).fit( + train_data=train_data, tuning_data=val_data, **self.params + ) + else: + self.model = TabularPredictor(label="label", verbosity=3 if verbose else 0).fit( + train_data=train_data, **self.params + ) + + def predict(self, X: pd.DataFrame) -> pd.Series: + predictions = self.model.predict(X) + return pd.Series(predictions, index=X.index) + + def predict_proba(self, X: pd.DataFrame) -> pd.DataFrame: + if self.model.problem_type == "regression": + raise ValueError("predict_proba is not available for regression tasks.") + probabilities = self.model.predict_proba(X) + return pd.DataFrame(probabilities, index=X.index) diff --git a/ml_garden/implementation/tabular/xgboost/model.py b/ml_garden/implementation/tabular/xgboost/model.py index f757183..a803c57 100644 --- a/ml_garden/implementation/tabular/xgboost/model.py +++ b/ml_garden/implementation/tabular/xgboost/model.py @@ -75,4 +75,5 @@ def predict_proba(self, X: pd.DataFrame) -> pd.DataFrame: pd.DataFrame Predicted class probabilities for the input features. 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["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linke test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] -all-models = ["xgboost"] +all-models = ["autogluon", "boto3", "botocore", "xgboost"] +autogluon = ["autogluon", "boto3", "botocore"] xgboost = ["xgboost"] [metadata] lock-version = "2.0" -python-versions = "^3.9" -content-hash = "73d15c9af2a0b2ca4497d00d96e56cec910eec1b3e7aeec706c7f6dac7e21cf9" +python-versions = ">=3.9,<3.12" +content-hash = "778e06c51205db94466d893b6331bb9278c823e170930f60da2b1c32fa8d335b" diff --git a/pyproject.toml b/pyproject.toml index a1bc91b..c9cbd08 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -16,24 +16,28 @@ readme = "README.md" packages = [{ include = "ml_garden" }] [tool.poetry.dependencies] -python = 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a/tests/core/steps/test_calculate_features.py +++ b/tests/core/steps/test_calculate_features.py @@ -62,7 +62,7 @@ def input_data() -> pd.DataFrame: def data(input_data: pd.DataFrame) -> DataContainer: data = DataContainer({"is_train": True}) data.columns_to_ignore_for_training = [] - data.train = input_data + data.X_train = input_data return data @@ -72,7 +72,7 @@ def test_skipping_with_no_parameters(data: DataContainer): result = calculate_features_step.execute(data) assert isinstance(result, DataContainer) - assert result.train.equals(data.train) + assert result.X_train.equals(data.X_train) def test_feature_names(data: DataContainer): @@ -87,22 +87,22 @@ def test_feature_names(data: DataContainer): result = calculate_features_step.execute(data) assert isinstance(result, DataContainer) - assert "creation_date_year" in result.train.columns - assert "creation_date_month" in result.train.columns - assert "creation_date_day" in result.train.columns - assert "creation_date_hour" in result.train.columns - assert "creation_date_minute" in result.train.columns - assert "creation_date_second" in result.train.columns - assert "creation_date_weekday" in result.train.columns - assert "creation_date_dayofyear" in result.train.columns - assert "deletion_date_year" in result.train.columns - assert "deletion_date_month" in result.train.columns - assert "deletion_date_day" in result.train.columns - assert "deletion_date_hour" in result.train.columns - assert "deletion_date_minute" in result.train.columns - assert "deletion_date_second" in result.train.columns - assert "deletion_date_weekday" in result.train.columns - assert "deletion_date_dayofyear" in result.train.columns + assert "creation_date_year" in result.X_train.columns + assert "creation_date_month" in result.X_train.columns + assert "creation_date_day" in result.X_train.columns + assert "creation_date_hour" in result.X_train.columns + assert "creation_date_minute" in result.X_train.columns + assert "creation_date_second" in result.X_train.columns + assert "creation_date_weekday" in result.X_train.columns + assert "creation_date_dayofyear" in result.X_train.columns + assert "deletion_date_year" in result.X_train.columns + assert "deletion_date_month" in result.X_train.columns + assert "deletion_date_day" in result.X_train.columns + assert "deletion_date_hour" in result.X_train.columns + assert "deletion_date_minute" in result.X_train.columns + assert "deletion_date_second" in result.X_train.columns + assert "deletion_date_weekday" in result.X_train.columns + assert "deletion_date_dayofyear" in result.X_train.columns def test_date_columns_are_ignored_for_training(data: DataContainer): @@ -117,8 +117,8 @@ def test_date_columns_are_ignored_for_training(data: DataContainer): result = calculate_features_step.execute(data) assert isinstance(result, DataContainer) - assert "creation_date" in result.columns_to_ignore_for_training - assert "deletion_date" in result.columns_to_ignore_for_training + assert "creation_date" not in result.X_train.columns + assert "deletion_date" not in result.X_train.columns def test_output_dtypes(data: DataContainer): @@ -133,14 +133,14 @@ def test_output_dtypes(data: DataContainer): result = calculate_features_step.execute(data) assert isinstance(result, DataContainer) - assert result.train["creation_date_year"].dtype == np.dtype("uint16") - assert result.train["creation_date_month"].dtype == np.dtype("uint8") - assert result.train["creation_date_day"].dtype == np.dtype("uint8") - assert result.train["creation_date_hour"].dtype == np.dtype("uint8") - assert result.train["creation_date_minute"].dtype == np.dtype("uint8") - assert result.train["creation_date_second"].dtype == np.dtype("uint8") - assert result.train["creation_date_weekday"].dtype == np.dtype("uint8") - assert result.train["creation_date_dayofyear"].dtype == np.dtype("uint16") + assert result.X_train["creation_date_year"].dtype == np.dtype("uint16") + assert result.X_train["creation_date_month"].dtype == np.dtype("uint8") + assert result.X_train["creation_date_day"].dtype == np.dtype("uint8") + assert result.X_train["creation_date_hour"].dtype == np.dtype("uint8") + assert result.X_train["creation_date_minute"].dtype == np.dtype("uint8") + assert result.X_train["creation_date_second"].dtype == np.dtype("uint8") + assert result.X_train["creation_date_weekday"].dtype == np.dtype("uint8") + assert result.X_train["creation_date_dayofyear"].dtype == np.dtype("uint16") def test_output_values(data: DataContainer): @@ -155,28 +155,28 @@ def test_output_values(data: DataContainer): result = calculate_features_step.execute(data) assert isinstance(result, DataContainer) - assert result.train["creation_date_year"].equals( + assert result.X_train["creation_date_year"].equals( pd.Series([2023, 2023, 2023, 2023, 2023, 2023, 2024, 2024], dtype="uint16") ) - assert result.train["creation_date_month"].equals( + assert result.X_train["creation_date_month"].equals( pd.Series([1, 1, 1, 1, 1, 11, 2, 3], dtype="uint8") ) - assert result.train["creation_date_day"].equals( + assert result.X_train["creation_date_day"].equals( pd.Series([1, 2, 3, 4, 5, 1, 28, 28], dtype="uint8") ) - assert result.train["creation_date_hour"].equals( + assert result.X_train["creation_date_hour"].equals( pd.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="uint8") ) - assert result.train["creation_date_minute"].equals( + assert result.X_train["creation_date_minute"].equals( pd.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="uint8") ) - assert result.train["creation_date_second"].equals( + assert result.X_train["creation_date_second"].equals( pd.Series([0, 0, 0, 0, 0, 0, 0, 0], dtype="uint8") ) - assert result.train["creation_date_weekday"].equals( + assert result.X_train["creation_date_weekday"].equals( pd.Series([6, 0, 1, 2, 3, 2, 2, 3], dtype="uint8") ) - assert result.train["creation_date_dayofyear"].equals( + assert result.X_train["creation_date_dayofyear"].equals( pd.Series([1, 2, 3, 4, 5, 305, 59, 88], dtype="uint16") ) @@ -214,7 +214,7 @@ def test_init_with_unsupported_features(): def test_execute_with_prediction(data: DataContainer): data.is_train = False - data.flow = data.train.copy() + data.X_prediction = data.X_train.copy() datetime_columns = ["creation_date"] features = ["year", "month", "day"] @@ -226,6 +226,6 @@ def test_execute_with_prediction(data: DataContainer): result = calculate_features_step.execute(data) assert isinstance(result, DataContainer) - assert "creation_date_year" in result.flow.columns - assert "creation_date_month" in result.flow.columns - assert "creation_date_day" in result.flow.columns + assert "creation_date_year" in result.X_prediction.columns + assert "creation_date_month" in result.X_prediction.columns + assert "creation_date_day" in result.X_prediction.columns diff --git a/tests/core/steps/test_encoding.py b/tests/core/steps/test_encoding.py index 3fea8b7..3b60661 100644 --- a/tests/core/steps/test_encoding.py +++ b/tests/core/steps/test_encoding.py @@ -29,7 +29,8 @@ def train_data() -> pd.DataFrame: def train_data_container(train_data: pd.DataFrame) -> DataContainer: data = DataContainer({"target": "target", "is_train": True}) data.columns_to_ignore_for_training = [] - data.train = train_data + data.X_train = train_data.drop(columns=["target"]) + data.y_train = train_data["target"] return data @@ -45,10 +46,10 @@ def test_check_numeric_passthrough(train_data_container: DataContainer): assert column in result.X_train.columns # Check that the dtype of the numeric column remains the same - assert result.X_train[column].dtype == train_data_container.train[column].dtype + assert result.X_train[column].dtype == train_data_container.X_train[column].dtype # Check that the values of the numeric column remain unchanged - pdt.assert_series_equal(result.X_train[column], train_data_container.train[column]) + pdt.assert_series_equal(result.X_train[column], train_data_container.X_train[column]) def test_check_ordinal_encoding(train_data_container: DataContainer): @@ -64,7 +65,7 @@ def test_check_ordinal_encoding(train_data_container: DataContainer): # Check that the number of unique encoded values matches the number of unique categories assert len(result.X_train["category_low"].unique()) == len( - train_data_container.train["category_low"].unique() + train_data_container.X_train["category_low"].unique() )