"""Base class for EvalML engines."""
import sys
import time
import traceback
from abc import ABC, abstractmethod
from collections import OrderedDict
import numpy as np
import pandas as pd
import woodwork as ww
from evalml.automl.utils import tune_binary_threshold
from evalml.exceptions import PipelineScoreError
from evalml.pipelines.components.ensemble.sklearn_stacked_ensemble_base import (
SklearnStackedEnsembleBase,
)
from evalml.preprocessing import split_data
from evalml.problem_types import is_binary, is_classification, is_multiclass
[docs]class EngineComputation(ABC):
"""Wrapper around the result of a (possibly asynchronous) engine computation."""
[docs] @abstractmethod
def get_result(self):
"""Gets the computation result. Will block until the computation is finished.
Raises Exception: If computation fails. Returns traceback.
"""
[docs] @abstractmethod
def done(self):
"""Whether the computation is done."""
[docs] @abstractmethod
def cancel(self):
"""Cancel the computation."""
[docs]class JobLogger:
"""Mimic the behavior of a python logging.Logger but stores all messages rather than actually logging them.
This is used during engine jobs so that log messages are recorded
after the job completes. This is desired so that all of the messages
for a single job are grouped together in the log.
"""
def __init__(self):
self.logs = []
[docs] def info(self, msg):
"""Store message at the info level."""
self.logs.append(("info", msg))
[docs] def debug(self, msg):
"""Store message at the debug level."""
self.logs.append(("debug", msg))
[docs] def warning(self, msg):
"""Store message at the warning level."""
self.logs.append(("warning", msg))
[docs] def error(self, msg):
"""Store message at the error level."""
self.logs.append(("error", msg))
[docs] def write_to_logger(self, logger):
"""Write all the messages to the logger, first in, first out (FIFO) order."""
logger_method = {
"info": logger.info,
"debug": logger.debug,
"warning": logger.warning,
"error": logger.warning,
}
for level, message in self.logs:
method = logger_method[level]
method(message)
[docs]class EngineBase(ABC):
"""Base class for EvalML engines."""
[docs] @staticmethod
def setup_job_log():
"""Set up logger for job."""
return JobLogger()
[docs] @abstractmethod
def submit_evaluation_job(self, automl_config, pipeline, X, y):
"""Submit job for pipeline evaluation during AutoMLSearch."""
[docs] @abstractmethod
def submit_training_job(self, automl_config, pipeline, X, y):
"""Submit job for pipeline training."""
[docs] @abstractmethod
def submit_scoring_job(
self, automl_config, pipeline, X, y, objectives, X_train=None, y_train=None
):
"""Submit job for pipeline scoring."""
[docs]def train_pipeline(pipeline, X, y, automl_config, schema=True):
"""Train a pipeline and tune the threshold if necessary.
Args:
pipeline (PipelineBase): Pipeline to train.
X (pd.DataFrame): Features to train on.
y (pd.Series): Target to train on.
automl_config (AutoMLSearch): The AutoMLSearch object, used to access config and the error callback.
schema (bool): Whether to use the schemas for X and y. Defaults to True.
Returns:
pipeline (PipelineBase): A trained pipeline instance.
"""
X_threshold_tuning = None
y_threshold_tuning = None
if automl_config.X_schema and schema:
X.ww.init(schema=automl_config.X_schema)
if automl_config.y_schema and schema:
y.ww.init(schema=automl_config.y_schema)
threshold_tuning_objective = automl_config.objective
if (
is_binary(automl_config.problem_type)
and automl_config.optimize_thresholds
and automl_config.objective.score_needs_proba
and automl_config.alternate_thresholding_objective is not None
):
# use the alternate_thresholding_objective
threshold_tuning_objective = automl_config.alternate_thresholding_objective
if (
automl_config.optimize_thresholds
and pipeline.can_tune_threshold_with_objective(threshold_tuning_objective)
):
X, X_threshold_tuning, y, y_threshold_tuning = split_data(
X, y, pipeline.problem_type, test_size=0.2, random_seed=pipeline.random_seed
)
cv_pipeline = pipeline.clone()
cv_pipeline.fit(X, y)
tune_binary_threshold(
cv_pipeline,
threshold_tuning_objective,
cv_pipeline.problem_type,
X_threshold_tuning,
y_threshold_tuning,
)
return cv_pipeline
[docs]def train_and_score_pipeline(
pipeline, automl_config, full_X_train, full_y_train, logger
):
"""Given a pipeline, config and data, train and score the pipeline and return the CV or TV scores.
Args:
pipeline (PipelineBase): The pipeline to score.
automl_config (AutoMLSearch): The AutoMLSearch object, used to access config and the error callback.
full_X_train (pd.DataFrame): Training features.
full_y_train (pd.Series): Training target.
logger: Logger object to write to.
Raises:
Exception: If there are missing target values in the training set after data split.
Returns:
tuple of three items: First - A dict containing cv_score_mean, cv_scores, training_time and a cv_data structure with details.
Second - The pipeline class we trained and scored. Third - the job logger instance with all the recorded messages.
"""
start = time.time()
cv_data = []
logger.info("\tStarting cross validation")
# Encode target for classification problems so that we can support float targets. This is okay because we only use split to get the indices to split on
if is_classification(automl_config.problem_type):
y_mapping = {
original_target: encoded_target
for (encoded_target, original_target) in enumerate(
full_y_train.value_counts().index
)
}
full_y_train = ww.init_series(full_y_train.map(y_mapping))
cv_pipeline = pipeline
for i, (train, valid) in enumerate(
automl_config.data_splitter.split(full_X_train, full_y_train)
):
if isinstance(pipeline.estimator, SklearnStackedEnsembleBase) and i > 0:
# Stacked ensembles do CV internally, so we do not run CV here for performance reasons.
logger.debug(
f"Skipping fold {i} because CV for scikit-learn based stacked ensembles is not supported."
)
break
logger.debug(f"\t\tTraining and scoring on fold {i}")
X_train, X_valid = full_X_train.ww.iloc[train], full_X_train.ww.iloc[valid]
y_train, y_valid = full_y_train.ww.iloc[train], full_y_train.ww.iloc[valid]
if is_binary(automl_config.problem_type) or is_multiclass(
automl_config.problem_type
):
diff_train = set(np.setdiff1d(full_y_train, y_train))
diff_valid = set(np.setdiff1d(full_y_train, y_valid))
diff_string = (
f"Missing target values in the training set after data split: {diff_train}. "
if diff_train
else ""
)
diff_string += (
f"Missing target values in the validation set after data split: {diff_valid}."
if diff_valid
else ""
)
if diff_string:
raise Exception(diff_string)
objectives_to_score = [
automl_config.objective
] + automl_config.additional_objectives
try:
logger.debug(f"\t\t\tFold {i}: starting training")
cv_pipeline = train_pipeline(
pipeline, X_train, y_train, automl_config, schema=False
)
logger.debug(f"\t\t\tFold {i}: finished training")
if (
automl_config.optimize_thresholds
and is_binary(automl_config.problem_type)
and cv_pipeline.threshold is not None
):
logger.debug(
f"\t\t\tFold {i}: Optimal threshold found ({cv_pipeline.threshold:.3f})"
)
logger.debug(f"\t\t\tFold {i}: Scoring trained pipeline")
scores = cv_pipeline.score(
X_valid,
y_valid,
objectives=objectives_to_score,
X_train=X_train,
y_train=y_train,
)
logger.debug(
f"\t\t\tFold {i}: {automl_config.objective.name} score: {scores[automl_config.objective.name]:.3f}"
)
score = scores[automl_config.objective.name]
except Exception as e:
if automl_config.error_callback is not None:
automl_config.error_callback(
exception=e,
traceback=traceback.format_tb(sys.exc_info()[2]),
automl=automl_config,
fold_num=i,
pipeline=pipeline,
)
if isinstance(e, PipelineScoreError):
nan_scores = {objective: np.nan for objective in e.exceptions}
scores = {**nan_scores, **e.scored_successfully}
scores = OrderedDict(
{
o.name: scores[o.name]
for o in [automl_config.objective]
+ automl_config.additional_objectives
}
)
score = scores[automl_config.objective.name]
else:
score = np.nan
scores = OrderedDict(
zip(
[n.name for n in automl_config.additional_objectives],
[np.nan] * len(automl_config.additional_objectives),
)
)
ordered_scores = OrderedDict()
ordered_scores.update({automl_config.objective.name: score})
ordered_scores.update(scores)
ordered_scores.update({"# Training": y_train.shape[0]})
ordered_scores.update({"# Validation": y_valid.shape[0]})
evaluation_entry = {
"all_objective_scores": ordered_scores,
"mean_cv_score": score,
"binary_classification_threshold": None,
}
if (
is_binary(automl_config.problem_type)
and cv_pipeline is not None
and cv_pipeline.threshold is not None
):
evaluation_entry["binary_classification_threshold"] = cv_pipeline.threshold
cv_data.append(evaluation_entry)
training_time = time.time() - start
cv_scores = pd.Series([fold["mean_cv_score"] for fold in cv_data])
cv_score_mean = cv_scores.mean()
logger.info(
f"\tFinished cross validation - mean {automl_config.objective.name}: {cv_score_mean:.3f}"
)
return {
"scores": {
"cv_data": cv_data,
"training_time": training_time,
"cv_scores": cv_scores,
"cv_score_mean": cv_score_mean,
},
"pipeline": cv_pipeline,
"logger": logger,
}
[docs]def evaluate_pipeline(pipeline, automl_config, X, y, logger):
"""Function submitted to the submit_evaluation_job engine method.
Args:
pipeline (PipelineBase): The pipeline to score.
automl_config (AutoMLConfig): The AutoMLSearch object, used to access config and the error callback.
X (pd.DataFrame): Training features.
y (pd.Series): Training target.
logger: Logger object to write to.
Returns:
tuple of three items: First - A dict containing cv_score_mean, cv_scores, training_time and a cv_data structure with details.
Second - The pipeline class we trained and scored. Third - the job logger instance with all the recorded messages.
"""
logger.info(f"{pipeline.name}:")
X.ww.init(schema=automl_config.X_schema)
y.ww.init(schema=automl_config.y_schema)
return train_and_score_pipeline(
pipeline,
automl_config=automl_config,
full_X_train=X,
full_y_train=y,
logger=logger,
)
[docs]def score_pipeline(
pipeline, X, y, objectives, X_train=None, y_train=None, X_schema=None, y_schema=None
):
"""Wrap around pipeline.score method to make it easy to score pipelines with dask.
Args:
pipeline (PipelineBase): The pipeline to score.
X (pd.DataFrame): Features to score on.
y (pd.Series): Target used to calculate scores.
objectives (list[ObjectiveBase]): List of objectives to score on.
X_train (pd.DataFrame): Training features. Used for feature engineering in time series.
y_train (pd.Series): Training target. Used for feature engineering in time series.
X_schema (ww.TableSchema): Schema for features. Defaults to None.
y_schema (ww.ColumnSchema): Schema for columns. Defaults to None.
Returns:
dict: Dictionary object containing pipeline scores.
"""
if X_schema:
X.ww.init(schema=X_schema)
if y_schema:
y.ww.init(schema=y_schema)
return pipeline.score(X, y, objectives, X_train=X_train, y_train=y_train)