Skip to content

API: Pipeline

skyulf.pipeline.SkyulfPipeline

End-to-end ML Pipeline.

Encapsulates: 1. Feature Engineering (Preprocessing) 2. Modeling (Training/Inference)

Source code in skyulf-core\skyulf\pipeline.py 
 31
 32
 33
 34
 35
 36
 37
 38
 39
 40
 41
 42
 43
 44
 45
 46
 47
 48
 49
 50
 51
 52
 53
 54
 55
 56
 57
 58
 59
 60
 61
 62
 63
 64
 65
 66
 67
 68
 69
 70
 71
 72
 73
 74
 75
 76
 77
 78
 79
 80
 81
 82
 83
 84
 85
 86
 87
 88
 89
 90
 91
 92
 93
 94
 95
 96
 97
 98
 99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
class SkyulfPipeline:
    """
    End-to-end ML Pipeline.

    Encapsulates:
    1. Feature Engineering (Preprocessing)
    2. Modeling (Training/Inference)
    """

    def __init__(self, config: Dict[str, Any]):
        """
        Initialize the pipeline.

        Args:
            config: Pipeline configuration dictionary.
                    Must contain 'preprocessing' (list) and 'modeling' (dict).
        """
        self.config = config
        self.preprocessing_steps = config.get("preprocessing", [])
        self.modeling_config = config.get("modeling", {})

        self.feature_engineer = FeatureEngineer(self.preprocessing_steps)
        self.model_estimator: Optional[StatefulEstimator] = None

        # Initialize model estimator if config is present
        if self.modeling_config:
            self._init_model_estimator()

    def _init_model_estimator(self):
        """Initialize the StatefulEstimator based on config."""
        model_type = self.modeling_config.get("type")
        node_id = self.modeling_config.get("node_id", "model_node")

        calculator: Optional[BaseModelCalculator] = None
        applier: Optional[BaseModelApplier] = None

        # Try Registry first
        try:
            calculator = NodeRegistry.get_calculator(model_type)()
            applier = NodeRegistry.get_applier(model_type)()
        except ValueError:
            pass

        if calculator is None:
            # Map model types to classes
            if model_type == "logistic_regression":
                calculator = LogisticRegressionCalculator()
                applier = LogisticRegressionApplier()
            elif model_type == "random_forest_classifier":
                calculator = RandomForestClassifierCalculator()
                applier = RandomForestClassifierApplier()
            elif model_type == "ridge_regression":
                calculator = RidgeRegressionCalculator()
                applier = RidgeRegressionApplier()
            elif model_type == "random_forest_regressor":
                calculator = RandomForestRegressorCalculator()
                applier = RandomForestRegressorApplier()
            elif model_type == "hyperparameter_tuner":
                # Tuner wraps another model
                base_model_config = self.modeling_config.get("base_model", {})
                base_model_type = base_model_config.get("type")

                base_calc: Optional[BaseModelCalculator] = None
                base_applier: Optional[BaseModelApplier] = None

                # Try Registry for base model
                try:
                    base_calc = NodeRegistry.get_calculator(base_model_type)()
                    base_applier = NodeRegistry.get_applier(base_model_type)()
                except ValueError:
                    pass

                if base_calc is None:
                    if base_model_type == "logistic_regression":
                        base_calc = LogisticRegressionCalculator()
                        base_applier = LogisticRegressionApplier()
                    elif base_model_type == "random_forest_classifier":
                        base_calc = RandomForestClassifierCalculator()
                        base_applier = RandomForestClassifierApplier()
                    elif base_model_type == "ridge_regression":
                        base_calc = RidgeRegressionCalculator()
                        base_applier = RidgeRegressionApplier()
                    elif base_model_type == "random_forest_regressor":
                        base_calc = RandomForestRegressorCalculator()
                        base_applier = RandomForestRegressorApplier()

                if base_calc and base_applier:
                    calculator = TuningCalculator(base_calc)
                    applier = TuningApplier(base_applier)
                else:
                    raise ValueError(
                        f"Unknown base model type for tuner: {base_model_type}"
                    )

        if calculator is None:
            raise ValueError(f"Unknown model type: {model_type}")

        self.model_estimator = StatefulEstimator(
            node_id=node_id, calculator=calculator, applier=applier
        )

    def fit(
        self, data: Union[pd.DataFrame, SkyulfDataFrame, SplitDataset], target_column: str
    ) -> Dict[str, Any]:
        """
        Fit the pipeline.

        Args:
            data: Input data (DataFrame or SplitDataset).
            target_column: Name of the target column.

        Returns:
            Dictionary containing execution metrics.
        """
        metrics = {}

        # 1. Feature Engineering
        logger.info("Starting Feature Engineering...")
        transformed_data, fe_metrics = self.feature_engineer.fit_transform(data)
        metrics["preprocessing"] = fe_metrics

        # 2. Modeling
        if self.model_estimator:
            logger.info("Starting Model Training...")

            # Ensure transformed_data is SplitDataset for modeling
            if isinstance(transformed_data, SplitDataset):
                dataset = transformed_data
            else:
                # If we only have a DataFrame, we can't really evaluate properly without a split
                # But we can fit on it.
                # Ideally, the user should provide a SplitDataset or use a Splitter node in preprocessing.
                # If preprocessing didn't split, we wrap it.
                engine = get_engine(transformed_data)
                empty_df = engine.create_dataframe({})
                dataset = SplitDataset(
                    train=transformed_data, test=empty_df, validation=None
                )

            # Fit the model
            # Note: fit_predict updates self.model_estimator.model in-memory
            _ = self.model_estimator.fit_predict(
                dataset=dataset,
                target_column=target_column,
                config=self.modeling_config,
            )

            # Evaluate
            # We can run evaluation if we have test/validation sets
            try:
                eval_report = self.model_estimator.evaluate(
                    dataset=dataset, target_column=target_column
                )
                metrics["modeling"] = eval_report
            except Exception as e:
                logger.warning(f"Evaluation failed: {e}")
                metrics["modeling_error"] = str(e)

        return metrics

    def predict(self, data: Union[pd.DataFrame, SkyulfDataFrame]) -> Any:
        """
        Generate predictions.

        Args:
            data: Input DataFrame.

        Returns:
            Series of predictions.
        """
        # 1. Feature Engineering (Transform only)
        transformed_data = self.feature_engineer.transform(data)

        # 2. Modeling
        if self.model_estimator and self.model_estimator.model is not None:
            return self.model_estimator.applier.predict(
                transformed_data, self.model_estimator.model
            )
        else:
            raise ValueError("Pipeline not fitted or no model configured.")

    def save(self, path: str):
        """Save the pipeline to a file."""
        # We can use pickle to save the whole object since we removed external dependencies
        with open(path, "wb") as f:
            pickle.dump(self, f)

    @classmethod
    def load(cls, path: str) -> "SkyulfPipeline":
        """Load the pipeline from a file."""
        with open(path, "rb") as f:
            return pickle.load(f)  # type: ignore

__init__(config)

Initialize the pipeline.

Parameters:

Name Type Description Default
config Dict[str, Any]

Pipeline configuration dictionary. Must contain 'preprocessing' (list) and 'modeling' (dict).

 required
Source code in skyulf-core\skyulf\pipeline.py 
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
def __init__(self, config: Dict[str, Any]):
    """
    Initialize the pipeline.

    Args:
        config: Pipeline configuration dictionary.
                Must contain 'preprocessing' (list) and 'modeling' (dict).
    """
    self.config = config
    self.preprocessing_steps = config.get("preprocessing", [])
    self.modeling_config = config.get("modeling", {})

    self.feature_engineer = FeatureEngineer(self.preprocessing_steps)
    self.model_estimator: Optional[StatefulEstimator] = None

    # Initialize model estimator if config is present
    if self.modeling_config:
        self._init_model_estimator()

fit(data, target_column)

Fit the pipeline.

Parameters:

Name Type Description Default
data Union[DataFrame, SkyulfDataFrame, SplitDataset]

Input data (DataFrame or SplitDataset).

 required
target_column str

Name of the target column.

 required

Returns:

Type Description
Dict[str, Any]

Dictionary containing execution metrics.
Source code in skyulf-core\skyulf\pipeline.py 
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
def fit(
    self, data: Union[pd.DataFrame, SkyulfDataFrame, SplitDataset], target_column: str
) -> Dict[str, Any]:
    """
    Fit the pipeline.

    Args:
        data: Input data (DataFrame or SplitDataset).
        target_column: Name of the target column.

    Returns:
        Dictionary containing execution metrics.
    """
    metrics = {}

    # 1. Feature Engineering
    logger.info("Starting Feature Engineering...")
    transformed_data, fe_metrics = self.feature_engineer.fit_transform(data)
    metrics["preprocessing"] = fe_metrics

    # 2. Modeling
    if self.model_estimator:
        logger.info("Starting Model Training...")

        # Ensure transformed_data is SplitDataset for modeling
        if isinstance(transformed_data, SplitDataset):
            dataset = transformed_data
        else:
            # If we only have a DataFrame, we can't really evaluate properly without a split
            # But we can fit on it.
            # Ideally, the user should provide a SplitDataset or use a Splitter node in preprocessing.
            # If preprocessing didn't split, we wrap it.
            engine = get_engine(transformed_data)
            empty_df = engine.create_dataframe({})
            dataset = SplitDataset(
                train=transformed_data, test=empty_df, validation=None
            )

        # Fit the model
        # Note: fit_predict updates self.model_estimator.model in-memory
        _ = self.model_estimator.fit_predict(
            dataset=dataset,
            target_column=target_column,
            config=self.modeling_config,
        )

        # Evaluate
        # We can run evaluation if we have test/validation sets
        try:
            eval_report = self.model_estimator.evaluate(
                dataset=dataset, target_column=target_column
            )
            metrics["modeling"] = eval_report
        except Exception as e:
            logger.warning(f"Evaluation failed: {e}")
            metrics["modeling_error"] = str(e)

    return metrics

load(path) classmethod

Load the pipeline from a file.

Source code in skyulf-core\skyulf\pipeline.py 
218
219
220
221
222
@classmethod
def load(cls, path: str) -> "SkyulfPipeline":
    """Load the pipeline from a file."""
    with open(path, "rb") as f:
        return pickle.load(f)  # type: ignore

predict(data)

Generate predictions.

Parameters:

Name Type Description Default
data Union[DataFrame, SkyulfDataFrame]

Input DataFrame.

 required

Returns:

Type Description
Any

Series of predictions.
Source code in skyulf-core\skyulf\pipeline.py 
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
def predict(self, data: Union[pd.DataFrame, SkyulfDataFrame]) -> Any:
    """
    Generate predictions.

    Args:
        data: Input DataFrame.

    Returns:
        Series of predictions.
    """
    # 1. Feature Engineering (Transform only)
    transformed_data = self.feature_engineer.transform(data)

    # 2. Modeling
    if self.model_estimator and self.model_estimator.model is not None:
        return self.model_estimator.applier.predict(
            transformed_data, self.model_estimator.model
        )
    else:
        raise ValueError("Pipeline not fitted or no model configured.")

save(path)

Save the pipeline to a file.

Source code in skyulf-core\skyulf\pipeline.py 
212
213
214
215
216
def save(self, path: str):
    """Save the pipeline to a file."""
    # We can use pickle to save the whole object since we removed external dependencies
    with open(path, "wb") as f:
        pickle.dump(self, f)