import json
from woodwork import logical_types
from .binary_classification_pipeline import BinaryClassificationPipeline
from .multiclass_classification_pipeline import (
MulticlassClassificationPipeline
)
from .regression_pipeline import RegressionPipeline
from .time_series_classification_pipelines import (
TimeSeriesBinaryClassificationPipeline,
TimeSeriesMulticlassClassificationPipeline
)
from .time_series_regression_pipeline import TimeSeriesRegressionPipeline
from evalml.data_checks import DataCheckActionCode
from evalml.model_family import ModelFamily
from evalml.pipelines import PipelineBase
from evalml.pipelines.components import ( # noqa: F401
CatBoostClassifier,
CatBoostRegressor,
ComponentBase,
DateTimeFeaturizer,
DelayedFeatureTransformer,
DropColumns,
DropNullColumns,
Estimator,
Imputer,
OneHotEncoder,
RandomForestClassifier,
StackedEnsembleClassifier,
StackedEnsembleRegressor,
StandardScaler,
TextFeaturizer
)
from evalml.pipelines.components.utils import all_components, get_estimators
from evalml.problem_types import (
ProblemTypes,
handle_problem_types,
is_time_series
)
from evalml.utils import get_logger, infer_feature_types
logger = get_logger(__file__)
def _get_preprocessing_components(X, y, problem_type, estimator_class):
"""Given input data, target data and an estimator class, construct a recommended preprocessing chain to be combined with the estimator and trained on the provided data.
Arguments:
X (ww.DataTable): The input data of shape [n_samples, n_features]
y (ww.DataColumn): The target data of length [n_samples]
problem_type (ProblemTypes or str): Problem type
estimator_class (class): A class which subclasses Estimator estimator for pipeline
Returns:
list[Transformer]: A list of applicable preprocessing components to use with the estimator
"""
X_pd = X.to_dataframe()
pp_components = []
all_null_cols = X_pd.columns[X_pd.isnull().all()]
if len(all_null_cols) > 0:
pp_components.append(DropNullColumns)
input_logical_types = set(X.logical_types.values())
types_imputer_handles = {logical_types.Boolean, logical_types.Categorical, logical_types.Double, logical_types.Integer}
if len(input_logical_types.intersection(types_imputer_handles)) > 0:
pp_components.append(Imputer)
text_columns = list(X.select('natural_language').columns)
if len(text_columns) > 0:
pp_components.append(TextFeaturizer)
datetime_cols = X.select(["Datetime"])
add_datetime_featurizer = len(datetime_cols.columns) > 0
if add_datetime_featurizer:
pp_components.append(DateTimeFeaturizer)
if is_time_series(problem_type):
pp_components.append(DelayedFeatureTransformer)
categorical_cols = X.select('category')
if len(categorical_cols.columns) > 0 and estimator_class not in {CatBoostClassifier, CatBoostRegressor}:
pp_components.append(OneHotEncoder)
if estimator_class.model_family == ModelFamily.LINEAR_MODEL:
pp_components.append(StandardScaler)
return pp_components
def _get_pipeline_base_class(problem_type):
"""Returns pipeline base class for problem_type"""
if problem_type == ProblemTypes.BINARY:
return BinaryClassificationPipeline
elif problem_type == ProblemTypes.MULTICLASS:
return MulticlassClassificationPipeline
elif problem_type == ProblemTypes.REGRESSION:
return RegressionPipeline
elif problem_type == ProblemTypes.TIME_SERIES_REGRESSION:
return TimeSeriesRegressionPipeline
elif problem_type == ProblemTypes.TIME_SERIES_BINARY:
return TimeSeriesBinaryClassificationPipeline
else:
return TimeSeriesMulticlassClassificationPipeline
[docs]def make_pipeline(X, y, estimator, problem_type, custom_hyperparameters=None):
"""Given input data, target data, an estimator class and the problem type,
generates a pipeline class with a preprocessing chain which was recommended based on the inputs.
The pipeline will be a subclass of the appropriate pipeline base class for the specified problem_type.
Arguments:
X (pd.DataFrame, ww.DataTable): The input data of shape [n_samples, n_features]
y (pd.Series, ww.DataColumn): The target data of length [n_samples]
estimator (Estimator): Estimator for pipeline
problem_type (ProblemTypes or str): Problem type for pipeline to generate
custom_hyperparameters (dictionary): Dictionary of custom hyperparameters,
with component name as key and dictionary of parameters as the value
Returns:
class: PipelineBase subclass with dynamically generated preprocessing components and specified estimator
"""
X = infer_feature_types(X)
y = infer_feature_types(y)
problem_type = handle_problem_types(problem_type)
if estimator not in get_estimators(problem_type):
raise ValueError(f"{estimator.name} is not a valid estimator for problem type")
preprocessing_components = _get_preprocessing_components(X, y, problem_type, estimator)
complete_component_graph = preprocessing_components + [estimator]
if custom_hyperparameters and not isinstance(custom_hyperparameters, dict):
raise ValueError(f"if custom_hyperparameters provided, must be dictionary. Received {type(custom_hyperparameters)}")
hyperparameters = custom_hyperparameters
base_class = _get_pipeline_base_class(problem_type)
class GeneratedPipeline(base_class):
custom_name = f"{estimator.name} w/ {' + '.join([component.name for component in preprocessing_components])}"
component_graph = complete_component_graph
custom_hyperparameters = hyperparameters
return GeneratedPipeline
[docs]def make_pipeline_from_components(component_instances, problem_type, custom_name=None, random_seed=0):
"""Given a list of component instances and the problem type, an pipeline instance is generated with the component instances.
The pipeline will be a subclass of the appropriate pipeline base class for the specified problem_type. The pipeline will be
untrained, even if the input components are already trained. A custom name for the pipeline can optionally be specified;
otherwise the default pipeline name will be 'Templated Pipeline'.
Arguments:
component_instances (list): a list of all of the components to include in the pipeline
problem_type (str or ProblemTypes): problem type for the pipeline to generate
custom_name (string): a name for the new pipeline
random_seed (int): Random seed used to intialize the pipeline.
Returns:
Pipeline instance with component instances and specified estimator created from given random state.
Example:
>>> components = [Imputer(), StandardScaler(), RandomForestClassifier()]
>>> pipeline = make_pipeline_from_components(components, problem_type="binary")
>>> pipeline.describe()
"""
for i, component in enumerate(component_instances):
if not isinstance(component, ComponentBase):
raise TypeError("Every element of `component_instances` must be an instance of ComponentBase")
if i == len(component_instances) - 1 and not isinstance(component, Estimator):
raise ValueError("Pipeline needs to have an estimator at the last position of the component list")
if custom_name and not isinstance(custom_name, str):
raise TypeError("Custom pipeline name must be a string")
pipeline_name = custom_name
problem_type = handle_problem_types(problem_type)
class TemplatedPipeline(_get_pipeline_base_class(problem_type)):
custom_name = pipeline_name
component_graph = [c.__class__ for c in component_instances]
return TemplatedPipeline({c.name: c.parameters for c in component_instances}, random_seed=random_seed)
[docs]def generate_pipeline_code(element):
"""Creates and returns a string that contains the Python imports and code required for running the EvalML pipeline.
Arguments:
element (pipeline instance): The instance of the pipeline to generate string Python code
Returns:
String representation of Python code that can be run separately in order to recreate the pipeline instance.
Does not include code for custom component implementation.
"""
# hold the imports needed and add code to end
code_strings = ['import json']
if not isinstance(element, PipelineBase):
raise ValueError("Element must be a pipeline instance, received {}".format(type(element)))
if isinstance(element.component_graph, dict):
raise ValueError("Code generation for nonlinear pipelines is not supported yet")
component_graph_string = ',\n\t\t'.join([com.__class__.__name__ if com.__class__ not in all_components() else "'{}'".format(com.name) for com in element])
code_strings.append("from {} import {}".format(element.__class__.__bases__[0].__module__, element.__class__.__bases__[0].__name__))
# check for other attributes associated with pipeline (ie name, custom_hyperparameters)
pipeline_list = []
for k, v in sorted(list(filter(lambda item: item[0][0] != '_', element.__class__.__dict__.items())), key=lambda x: x[0]):
if k == 'component_graph':
continue
pipeline_list += ["{} = '{}'".format(k, v)] if isinstance(v, str) else ["{} = {}".format(k, v)]
pipeline_string = "\t" + "\n\t".join(pipeline_list) + "\n" if len(pipeline_list) else ""
try:
ret = json.dumps(element.parameters, indent='\t')
except TypeError:
raise TypeError(f"Value {element.parameters} cannot be JSON-serialized")
# create the base string for the pipeline
base_string = "\nclass {0}({1}):\n" \
"\tcomponent_graph = [\n\t\t{2}\n\t]\n" \
"{3}" \
"\nparameters = json.loads(\"\"\"{4}\"\"\")\n" \
"pipeline = {0}(parameters)" \
.format(element.__class__.__name__,
element.__class__.__bases__[0].__name__,
component_graph_string,
pipeline_string,
ret)
code_strings.append(base_string)
return "\n".join(code_strings)
def _make_stacked_ensemble_pipeline(input_pipelines, problem_type, n_jobs=-1, random_seed=0):
"""
Creates a pipeline with a stacked ensemble estimator.
Arguments:
input_pipelines (list(PipelineBase or subclass obj)): List of pipeline instances to use as the base estimators for the stacked ensemble.
This must not be None or an empty list or else EnsembleMissingPipelinesError will be raised.
problem_type (ProblemType): problem type of pipeline
n_jobs (int or None): Integer describing level of parallelism used for pipelines.
None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used.
Defaults to -1.
Returns:
Pipeline with appropriate stacked ensemble estimator.
"""
if problem_type in [ProblemTypes.BINARY, ProblemTypes.MULTICLASS]:
return make_pipeline_from_components([StackedEnsembleClassifier(input_pipelines, n_jobs=n_jobs)], problem_type,
custom_name="Stacked Ensemble Classification Pipeline",
random_seed=random_seed)
else:
return make_pipeline_from_components([StackedEnsembleRegressor(input_pipelines, n_jobs=n_jobs)], problem_type,
custom_name="Stacked Ensemble Regression Pipeline",
random_seed=random_seed)
def _make_component_list_from_actions(actions):
"""
Creates a list of components from the input DataCheckAction list
Arguments:
actions (list(DataCheckAction)): List of DataCheckAction objects used to create list of components
Returns:
List of components used to address the input actions
"""
components = []
for action in actions:
if action.action_code == DataCheckActionCode.DROP_COL:
components.append(DropColumns(columns=action.details["columns"]))
return components