"""
Component Wrappers around sklearn models
"""
import pathlib
from abc import abstractmethod
from collections import defaultdict
from copy import deepcopy
import astropy.units as u
import joblib
import numpy as np
from astropy.coordinates import AltAz
from astropy.table import QTable, Table, hstack
from astropy.utils.decorators import lazyproperty
from sklearn.metrics import accuracy_score, r2_score, roc_auc_score
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.utils import all_estimators
from tables import open_file
from tqdm import tqdm
from traitlets import TraitError, observe
from ctapipe.exceptions import TooFewEvents
from ..containers import (
ArrayEventContainer,
DispContainer,
ParticleClassificationContainer,
ReconstructedEnergyContainer,
ReconstructedGeometryContainer,
)
from ..coordinates import TelescopeFrame
from ..core import (
Component,
FeatureGenerator,
Provenance,
QualityQuery,
ToolConfigurationError,
traits,
)
from ..io import write_table
from .preprocessing import collect_features, table_to_X, telescope_to_horizontal
from .reconstructor import ReconstructionProperty, Reconstructor
from .stereo_combination import StereoCombiner
from .utils import add_defaults_and_meta
__all__ = [
"SKLearnReconstructor",
"SKLearnRegressionReconstructor",
"SKLearnClassificationReconstructor",
"EnergyRegressor",
"ParticleClassifier",
"DispReconstructor",
"CrossValidator",
]
SUPPORTED_CLASSIFIERS = dict(all_estimators("classifier"))
SUPPORTED_REGRESSORS = dict(all_estimators("regressor"))
SUPPORTED_MODELS = {**SUPPORTED_CLASSIFIERS, **SUPPORTED_REGRESSORS}
_invalid_geometry = ReconstructedGeometryContainer(
alt=u.Quantity(np.nan, unit=u.deg),
az=u.Quantity(np.nan, unit=u.deg),
is_valid=False,
)
class MLQualityQuery(QualityQuery):
"""Quality criteria for machine learning models with different defaults"""
quality_criteria = traits.List(
default_value=[
("> 50 phe", "hillas_intensity > 50"),
("Positive width", "hillas_width > 0"),
("> 3 pixels", "morphology_n_pixels > 3"),
("valid stereo reco", "HillasReconstructor_is_valid"),
],
help=QualityQuery.quality_criteria.help,
).tag(config=True)
[docs]
class SKLearnReconstructor(Reconstructor):
"""
Base Class for a Machine Learning Based Reconstructor.
Keeps a dictionary of sklearn models, the current tools are designed
to train one model per telescope type.
"""
#: Name of the target table column for training.
target: str = ""
#: Property predicted, overridden in subclass.
property = None
prefix = traits.Unicode(
default_value=None,
allow_none=True,
help="Prefix for the output of this model. If None, ``model_cls`` is used.",
).tag(config=True)
features = traits.List(
traits.Unicode(), help="Features to use for this model."
).tag(config=True)
model_config = traits.Dict({}, help="kwargs for the sklearn model.").tag(
config=True
)
model_cls = traits.Enum(
SUPPORTED_MODELS.keys(),
default_value=None,
allow_none=True,
help="Which scikit-learn model to use.",
).tag(config=True)
stereo_combiner_cls = traits.ComponentName(
StereoCombiner,
default_value="StereoMeanCombiner",
help="Which stereo combination method to use.",
).tag(config=True)
load_path = traits.Path(
default_value=None,
allow_none=True,
help="If given, load serialized model from this path.",
).tag(config=True)
def __init__(
self, subarray=None, atmosphere_profile=None, models=None, n_jobs=None, **kwargs
):
# Run the Component __init__ first to handle the configuration
# and make `self.load_path` available
Component.__init__(self, **kwargs)
if self.load_path is None:
if self.model_cls is None:
raise TraitError(
"Must provide `model_cls` if not loading model from file"
)
if subarray is None:
raise TypeError(
"__init__() missing 1 required positional argument: 'subarray'"
)
if self.prefix is None:
# Default prefix is model_cls
self.prefix = self.model_cls
super().__init__(subarray, atmosphere_profile, **kwargs)
self.feature_generator = FeatureGenerator(parent=self)
self.quality_query = MLQualityQuery(parent=self)
# to verify settings
self._new_model()
self._models = {} if models is None else models
self.unit = None
self.stereo_combiner = StereoCombiner.from_name(
self.stereo_combiner_cls,
prefix=self.prefix,
property=self.property,
parent=self,
)
else:
loaded = self.read(self.load_path)
if (
subarray is not None
and loaded.subarray.telescope_types != subarray.telescope_types
):
self.log.warning(
"Supplied subarray has different telescopes than subarray loaded from file"
)
self.__dict__.update(loaded.__dict__)
self.subarray = subarray
if self.prefix is None:
self.prefix = self.model_cls
[docs]
@abstractmethod
def __call__(self, event: ArrayEventContainer) -> None:
"""
Event-wise prediction for the EventSource-Loop.
Fills the event.dl2.<your-feature>[name] container.
Parameters
----------
event: ArrayEventContainer
"""
[docs]
@abstractmethod
def predict_table(self, key, table: Table) -> Table:
"""
Predict on a table of events.
Parameters
----------
key : Hashable
Key of the model. Currently always a `~ctapipe.instrument.TelescopeDescription`
as we train models per telescope type.
table : `~astropy.table.Table`
Table of features
Returns
-------
table : `~astropy.table.Table`
Table(s) with predictions, matches the corresponding
container definition(s)
"""
[docs]
def write(self, path, overwrite=False):
path = pathlib.Path(path)
if path.exists() and not overwrite:
raise OSError(f"Path {path} exists and overwrite=False")
with path.open("wb") as f:
Provenance().add_output_file(path, role="ml-models")
joblib.dump(self, f, compress=True)
@lazyproperty
def instrument_table(self):
return QTable(self.subarray.to_table("joined"))
def _new_model(self):
cfg = self.model_config
if self.n_jobs:
cfg["n_jobs"] = self.n_jobs
return SUPPORTED_MODELS[self.model_cls](**cfg)
def _table_to_y(self, table, mask=None):
"""
Extract target values as numpy array from input table.
"""
# make sure we use the unit that was used during training
if self.unit is not None:
return table[mask][self.target].quantity.to_value(self.unit)
return np.array(table[self.target][mask])
[docs]
def fit(self, key, table):
"""
Create and fit a new model for ``key`` using the data in ``table``.
"""
self._models[key] = self._new_model()
X, valid = table_to_X(table, self.features, self.log)
self.unit = table[self.target].unit
y = self._table_to_y(table, mask=valid)
self._models[key].fit(X, y)
@observe("n_jobs")
def _set_n_jobs(self, n_jobs):
"""
Update n_jobs of all associated models.
"""
if hasattr(self, "_models"):
for model in self._models.values():
model.n_jobs = n_jobs.new
[docs]
class SKLearnRegressionReconstructor(SKLearnReconstructor):
"""Base class for regression tasks."""
model_cls = traits.Enum(
SUPPORTED_REGRESSORS.keys(),
default_value=None,
allow_none=True,
help="Which scikit-learn regression model to use.",
).tag(config=True)
log_target = traits.Bool(
default_value=False,
help="If True, the model is trained to predict the natural logarithm.",
).tag(config=True)
def _predict(self, key, table):
if key not in self._models:
raise KeyError(
f"No model available for key {key},"
f" available models: {self._models.keys()}"
)
X, valid = table_to_X(table, self.features, self.log)
n_outputs = getattr(self._models[key], "n_outputs_", 1)
if n_outputs > 1:
shape = (len(table), n_outputs)
else:
shape = (len(table),)
prediction = np.full(shape, np.nan)
if np.any(valid):
valid_predictions = self._models[key].predict(X)
if self.log_target:
prediction[valid] = np.exp(valid_predictions)
else:
prediction[valid] = valid_predictions
if self.unit is not None:
prediction = u.Quantity(prediction, self.unit, copy=False)
return prediction, valid
def _table_to_y(self, table, mask=None):
y = super()._table_to_y(table, mask=mask)
if self.log_target:
if np.any(y <= 0):
raise ValueError("y contains negative values, cannot apply log")
return np.log(y)
return y
[docs]
class SKLearnClassificationReconstructor(SKLearnReconstructor):
"""Base class for classification tasks."""
model_cls = traits.Enum(
SUPPORTED_CLASSIFIERS.keys(),
default_value=None,
allow_none=True,
help="Which scikit-learn classification model to use.",
).tag(config=True)
invalid_class = traits.Integer(
default_value=-1,
help="The label value to fill in case no prediction could be made.",
).tag(config=True)
positive_class = traits.Integer(
default_value=1,
help=(
"The label value of the positive class in case of binary classification."
" ``prediction`` values close to 1.0 mean the event"
" belonged likely to this class."
),
).tag(config=True)
def _predict(self, key, table):
if key not in self._models:
raise KeyError(
f"No model available for key {key},"
f" available models: {self._models.keys()}"
)
X, valid = table_to_X(table, self.features, self.log)
n_outputs = getattr(self._models[key], "n_outputs_", 1)
if n_outputs > 1:
shape = (len(table), n_outputs)
else:
shape = (len(table),)
prediction = np.full(shape, self.invalid_class, dtype=np.int8)
if np.any(valid):
prediction[valid] = self._models[key].predict(X)
return prediction, valid
def _predict_score(self, key, table):
if key not in self._models:
raise KeyError(
f"No model available for key {key},"
f" available models: {self._models.keys()}"
)
X, valid = table_to_X(table, self.features, self.log)
n_classes = getattr(self._models[key], "n_classes_", 2)
n_rows = len(table)
shape = (n_rows, n_classes) if n_classes > 2 else (n_rows,)
scores = np.full(shape, np.nan)
if np.any(valid):
prediction = self._models[key].predict_proba(X)[:]
if n_classes > 2:
scores[valid] = prediction
else:
# only return one score for the positive class
scores[valid] = prediction[:, self._get_positive_index(key)]
return scores, valid
def _get_positive_index(self, key):
return np.nonzero(self._models[key].classes_ == self.positive_class)[0][0]
[docs]
class EnergyRegressor(SKLearnRegressionReconstructor):
"""
Use a scikit-learn regression model per telescope type to predict primary energy.
"""
target = "true_energy"
property = ReconstructionProperty.ENERGY
[docs]
def __call__(self, event: ArrayEventContainer) -> None:
for tel_id in event.trigger.tels_with_trigger:
table = collect_features(event, tel_id, self.instrument_table)
table = self.feature_generator(table, subarray=self.subarray)
# get_table_mask returns a table with a single row
passes_quality_checks = self.quality_query.get_table_mask(table)[0]
if passes_quality_checks:
prediction, valid = self._predict(
self.subarray.tel[tel_id],
table,
)
container = ReconstructedEnergyContainer(
energy=prediction[0],
is_valid=valid[0],
)
else:
container = ReconstructedEnergyContainer(
energy=u.Quantity(np.nan, self.unit),
is_valid=False,
)
container.prefix = f"{self.prefix}_tel"
event.dl2.tel[tel_id].energy[self.prefix] = container
self.stereo_combiner(event)
[docs]
def predict_table(self, key, table: Table) -> dict[ReconstructionProperty, Table]:
table = self.feature_generator(table, subarray=self.subarray)
n_rows = len(table)
energy = u.Quantity(np.full(n_rows, np.nan), self.unit, copy=False)
is_valid = np.full(n_rows, False)
valid = self.quality_query.get_table_mask(table)
energy[valid], is_valid[valid] = self._predict(key, table[valid])
result = Table(
{
f"{self.prefix}_tel_energy": energy,
f"{self.prefix}_tel_is_valid": is_valid,
}
)
add_defaults_and_meta(
result,
ReconstructedEnergyContainer,
prefix=self.prefix,
add_tel_prefix=True,
)
return {ReconstructionProperty.ENERGY: result}
[docs]
class ParticleClassifier(SKLearnClassificationReconstructor):
"""Predict dl2 particle classification."""
target = "true_shower_primary_id"
positive_class = traits.Integer(
default_value=0,
help="Particle id (in simtel system) of the positive class. Default is 0 for gammas.",
).tag(config=True)
property = ReconstructionProperty.PARTICLE_TYPE
[docs]
def __call__(self, event: ArrayEventContainer) -> None:
for tel_id in event.trigger.tels_with_trigger:
table = collect_features(event, tel_id, self.instrument_table)
table = self.feature_generator(table, subarray=self.subarray)
passes_quality_checks = self.quality_query.get_table_mask(table)[0]
if passes_quality_checks:
prediction, valid = self._predict_score(
self.subarray.tel[tel_id],
table,
)
container = ParticleClassificationContainer(
prediction=prediction[0],
is_valid=valid[0],
)
else:
container = ParticleClassificationContainer(
prediction=np.nan, is_valid=False
)
container.prefix = f"{self.prefix}_tel"
event.dl2.tel[tel_id].classification[self.prefix] = container
self.stereo_combiner(event)
[docs]
def predict_table(self, key, table: Table) -> dict[ReconstructionProperty, Table]:
table = self.feature_generator(table, subarray=self.subarray)
n_rows = len(table)
score = np.full(n_rows, np.nan)
is_valid = np.full(n_rows, False)
valid = self.quality_query.get_table_mask(table)
score[valid], is_valid[valid] = self._predict_score(key, table[valid])
result = Table(
{
f"{self.prefix}_tel_prediction": score,
f"{self.prefix}_tel_is_valid": is_valid,
}
)
add_defaults_and_meta(
result,
ParticleClassificationContainer,
prefix=self.prefix,
add_tel_prefix=True,
)
return {ReconstructionProperty.PARTICLE_TYPE: result}
[docs]
class DispReconstructor(Reconstructor):
"""
Predict absolute value and sign for disp origin reconstruction and
convert to altitude and azimuth prediction for each telescope.
"""
target = "true_disp"
prefix = traits.Unicode(
default_value="disp",
allow_none=False,
help="Prefix for the output of this model. If None, ``disp`` is used.",
).tag(config=True)
features = traits.List(
traits.Unicode(), help="Features to use for both models."
).tag(config=True)
log_target = traits.Bool(
default_value=False,
help=(
"If True, the norm(disp) model is trained to predict ln(norm(disp))"
" and the output is"
" ``prefix_parameter`` = ``sign_prediction`` * ``exp(norm_prediction)``."
),
).tag(config=True)
norm_config = traits.Dict({}, help="kwargs for the sklearn regressor.").tag(
config=True
)
norm_cls = traits.Enum(
SUPPORTED_REGRESSORS.keys(),
default_value=None,
allow_none=True,
help="Which scikit-learn regression model to use.",
).tag(config=True)
sign_config = traits.Dict({}, help="kwargs for the sklearn classifier.").tag(
config=True
)
sign_cls = traits.Enum(
SUPPORTED_CLASSIFIERS.keys(),
default_value=None,
allow_none=True,
help="Which scikit-learn classification model to use.",
).tag(config=True)
stereo_combiner_cls = traits.ComponentName(
StereoCombiner,
default_value="StereoMeanCombiner",
help="Which stereo combination method to use.",
).tag(config=True)
load_path = traits.Path(
default_value=None,
allow_none=True,
help="If given, load serialized model from this path.",
).tag(config=True)
def __init__(self, subarray=None, atmosphere_profile=None, models=None, **kwargs):
# Run the Component __init__ first to handle the configuration
# and make `self.load_path` available
Component.__init__(self, **kwargs)
if self.load_path is None:
if self.norm_cls is None or self.sign_cls is None:
raise TraitError(
"Must provide `norm_cls` and `sign_cls` if not loading from file"
)
if subarray is None:
raise TypeError(
"__init__() missing 1 required positional argument: 'subarray'"
)
super().__init__(subarray, atmosphere_profile, **kwargs)
self.quality_query = MLQualityQuery(parent=self)
self.feature_generator = FeatureGenerator(parent=self)
# to verify settings
self._new_models()
self._models = {} if models is None else models
self.unit = None
self.stereo_combiner = StereoCombiner.from_name(
self.stereo_combiner_cls,
prefix=self.prefix,
property=ReconstructionProperty.GEOMETRY,
parent=self,
)
else:
loaded = self.read(self.load_path)
if (
subarray is not None
and loaded.subarray.telescope_types != subarray.telescope_types
):
self.log.warning(
"Supplied subarray has different telescopes than subarray loaded from file"
)
self.__dict__.update(loaded.__dict__)
self.subarray = subarray
def _new_models(self):
norm_cfg = self.norm_config
sign_cfg = self.sign_config
if self.n_jobs:
norm_cfg["n_jobs"] = self.n_jobs
sign_cfg["n_jobs"] = self.n_jobs
norm_regressor = SUPPORTED_REGRESSORS[self.norm_cls](**norm_cfg)
sign_classifier = SUPPORTED_CLASSIFIERS[self.sign_cls](**sign_cfg)
return norm_regressor, sign_classifier
def _table_to_y(self, table, mask=None):
"""
Extract target values as numpy array from input table.
"""
# make sure we use the unit that was used during training
if self.unit is not None:
norm = table[mask][self.target].quantity.to_value(self.unit)
else:
norm = np.array(table[self.target][mask])
abs_norm = np.abs(norm)
sign_norm = np.sign(norm)
if self.log_target:
abs_norm = np.log(abs_norm)
return abs_norm, sign_norm
[docs]
def fit(self, key, table):
"""
Create and fit new models for ``key`` using the data in ``table``.
"""
self._models[key] = self._new_models()
X, valid = table_to_X(table, self.features, self.log)
self.unit = table[self.target].unit
norm, sign = self._table_to_y(table, mask=valid)
self._models[key][0].fit(X, norm)
self._models[key][1].fit(X, sign)
[docs]
def write(self, path, overwrite=False):
path = pathlib.Path(path)
if path.exists() and not overwrite:
raise OSError(f"Path {path} exists and overwrite=False")
with path.open("wb") as f:
Provenance().add_output_file(path, role="ml-models")
joblib.dump(self, f, compress=True)
[docs]
@classmethod
def read(cls, path, **kwargs):
with open(path, "rb") as f:
instance = joblib.load(f)
for attr, value in kwargs.items():
setattr(instance, attr, value)
if not isinstance(instance, cls):
raise TypeError(
f"{path} did not contain an instance of {cls}, got {instance}"
)
# FIXME: we currently don't store metadata in the joblib / pickle files, see #2603
Provenance().add_input_file(path, role="ml-models", add_meta=False)
return instance
@lazyproperty
def instrument_table(self):
return self.subarray.to_table("joined")
def _predict(self, key, table):
if key not in self._models:
raise KeyError(
f"No model available for key {key},"
f" available models: {self._models.keys()}"
)
X, valid = table_to_X(table, self.features, self.log)
prediction = np.full(len(table), np.nan)
score = np.full(len(table), np.nan)
if np.any(valid):
valid_norms = self._models[key][0].predict(X)
if self.log_target:
prediction[valid] = np.exp(valid_norms)
else:
prediction[valid] = valid_norms
sign_proba = self._models[key][1].predict_proba(X)[:, 0]
# proba is [0 and 1] where 0 => very certain -1, 1 => very certain 1
# and 0.5 means random guessing either. So we transform to a score
# where 0 means "guessing" and 1 means "very certain"
score[valid] = np.abs(2 * sign_proba - 1.0)
prediction[valid] *= np.where(sign_proba >= 0.5, 1.0, -1.0)
if self.unit is not None:
prediction = u.Quantity(prediction, self.unit, copy=False)
return prediction, score, valid
[docs]
def __call__(self, event: ArrayEventContainer) -> None:
"""
Event-wise prediction for the EventSource-Loop.
Fills the event.dl2.tel[tel_id].disp[prefix] container
and event.dl2.tel[tel_id].geometry[prefix] container.
Parameters
----------
event: ArrayEventContainer
"""
for tel_id in event.trigger.tels_with_trigger:
table = collect_features(event, tel_id, self.instrument_table)
table = self.feature_generator(table, subarray=self.subarray)
passes_quality_checks = self.quality_query.get_table_mask(table)[0]
if passes_quality_checks:
disp, sign_score, valid = self._predict(
self.subarray.tel[tel_id], table
)
if valid:
disp_container = DispContainer(
parameter=disp[0],
sign_score=sign_score[0],
)
hillas = event.dl1.tel[tel_id].parameters.hillas
psi = hillas.psi.to_value(u.rad)
fov_lon = hillas.fov_lon + disp[0] * np.cos(psi)
fov_lat = hillas.fov_lat + disp[0] * np.sin(psi)
altaz = TelescopeFrame(
fov_lon=fov_lon,
fov_lat=fov_lat,
telescope_pointing=AltAz(
alt=event.pointing.tel[tel_id].altitude,
az=event.pointing.tel[tel_id].azimuth,
),
).transform_to(AltAz())
altaz_container = ReconstructedGeometryContainer(
alt=altaz.alt, az=altaz.az, is_valid=True
)
else:
disp_container = DispContainer(
parameter=u.Quantity(np.nan, self.unit),
)
altaz_container = deepcopy(_invalid_geometry)
else:
disp_container = DispContainer(
parameter=u.Quantity(np.nan, self.unit),
)
altaz_container = deepcopy(_invalid_geometry)
disp_container.prefix = f"{self.prefix}_tel"
altaz_container.prefix = f"{self.prefix}_tel"
event.dl2.tel[tel_id].disp[self.prefix] = disp_container
event.dl2.tel[tel_id].geometry[self.prefix] = altaz_container
self.stereo_combiner(event)
[docs]
def predict_table(self, key, table: Table) -> dict[ReconstructionProperty, Table]:
"""
Predict on a table of events.
Parameters
----------
table : `~astropy.table.Table`
Table of features
Returns
-------
disp_table : `~astropy.table.Table`
Table with disp predictions, matches the corresponding
container definition
altaz_table : `~astropy.table.Table`
Table with resulting predictions of horizontal coordinates
"""
table = self.feature_generator(table, subarray=self.subarray)
n_rows = len(table)
disp = u.Quantity(np.full(n_rows, np.nan), self.unit, copy=False)
is_valid = np.full(n_rows, False)
sign_score = np.full(n_rows, np.nan)
valid = self.quality_query.get_table_mask(table)
disp[valid], sign_score[valid], is_valid[valid] = self._predict(
key, table[valid]
)
disp_result = Table(
{
f"{self.prefix}_tel_parameter": disp,
f"{self.prefix}_tel_sign_score": sign_score,
}
)
add_defaults_and_meta(
disp_result,
DispContainer,
prefix=self.prefix,
add_tel_prefix=True,
)
psi = table["hillas_psi"].quantity.to_value(u.rad)
fov_lon = table["hillas_fov_lon"].quantity + disp * np.cos(psi)
fov_lat = table["hillas_fov_lat"].quantity + disp * np.sin(psi)
# prefer to use pointing interpolated to event
if "telescope_pointing_altitude" in table.colnames:
pointing_alt = table["telescope_pointing_altitude"]
pointing_az = table["telescope_pointing_azimuth"]
else:
# fallback to fixed pointing of ob
pointing_alt = table["subarray_pointing_lat"]
pointing_az = table["subarray_pointing_lon"]
alt, az = telescope_to_horizontal(
lon=fov_lon,
lat=fov_lat,
pointing_alt=pointing_alt,
pointing_az=pointing_az,
)
altaz_result = Table(
{
f"{self.prefix}_tel_alt": alt,
f"{self.prefix}_tel_az": az,
f"{self.prefix}_tel_is_valid": is_valid,
}
)
add_defaults_and_meta(
altaz_result,
ReconstructedGeometryContainer,
prefix=self.prefix,
add_tel_prefix=True,
)
return {
ReconstructionProperty.DISP: disp_result,
ReconstructionProperty.GEOMETRY: altaz_result,
}
@observe("n_jobs")
def _set_n_jobs(self, n_jobs):
"""
Update n_jobs of all associated models.
"""
if hasattr(self, "_models"):
for disp, sign in self._models.values():
disp.n_jobs = n_jobs.new
sign.n_jobs = n_jobs.new
[docs]
class CrossValidator(Component):
"""Class to train sklearn based reconstructors in a cross validation."""
n_cross_validations = traits.Int(
default_value=5, help="Number of cross validation iterations."
).tag(config=True)
output_path = traits.Path(
default_value=None,
allow_none=True,
directory_ok=False,
help=(
"Output path for the cross validation results."
" This is a hdf5 file containing labels and predictions for"
" the events used in the cross validation which can be used to"
" create performance plots."
),
).tag(config=True)
rng_seed = traits.Int(
default_value=1337, help="Random seed for splitting the training data."
).tag(config=True)
def __init__(self, model_component, overwrite=False, **kwargs):
super().__init__(**kwargs)
self.model_component = model_component
self.rng = np.random.default_rng(self.rng_seed)
if isinstance(self.model_component, SKLearnClassificationReconstructor):
self.cross_validate = self._cross_validate_classification
self.split_data = StratifiedKFold
elif isinstance(self.model_component, SKLearnRegressionReconstructor):
self.cross_validate = self._cross_validate_regressor
self.split_data = KFold
elif isinstance(self.model_component, DispReconstructor):
self.cross_validate = self._cross_validate_disp
self.split_data = StratifiedKFold
else:
raise KeyError(
"Unsupported Model of type %s supplied", self.model_component
)
if self.output_path:
if self.output_path.exists():
if overwrite:
self.log.warning("Overwriting %s", self.output_path)
else:
raise ToolConfigurationError(
f"Output path {self.output_path} exists, but overwrite=False"
)
Provenance().add_output_file(self.output_path, role="ml-cross-validation")
self.h5file = open_file(self.output_path, mode="w")
[docs]
def close(self):
"""Close the output hdf5 file, if ``self.output_path`` is given."""
if self.output_path:
self.h5file.close()
def __enter__(self):
return self
def __exit__(self, exc_type, exc_value, traceback):
self.close()
[docs]
def __call__(self, telescope_type, table):
"""Perform cross validation for the given model."""
if self.n_cross_validations == 0:
return
if len(table) <= self.n_cross_validations:
raise TooFewEvents(f"Too few events for {telescope_type}.")
self.log.info(
"Starting cross-validation with %d folds for type %s.",
self.n_cross_validations,
telescope_type,
)
scores = defaultdict(list)
kfold = self.split_data(
n_splits=self.n_cross_validations,
shuffle=True,
# sklearn does not support numpy's new random API yet
random_state=self.rng.integers(0, 2**31 - 1),
)
if isinstance(self.model_component, DispReconstructor):
cv_it = kfold.split(table, np.sign(table[self.model_component.target]))
else:
cv_it = kfold.split(table, table[self.model_component.target])
for fold, (train_indices, test_indices) in enumerate(
tqdm(
cv_it,
total=self.n_cross_validations,
desc=f"Cross Validation for {telescope_type}",
),
):
train = table[train_indices]
test = table[test_indices]
cv_result, metrics = self.cross_validate(telescope_type, train, test)
if self.output_path:
results = Table(
data={
"cv_fold": np.full(len(cv_result), fold, dtype=np.uint8),
"tel_type": [str(telescope_type)] * len(cv_result),
"true_energy": test["true_energy"],
"true_impact_distance": test["true_impact_distance"],
},
descriptions={
"cv_fold": "Cross validation iteration",
"tel_type": "Telescope type",
},
)
results = hstack([results, cv_result], join_type="exact")
write_table(
results,
self.h5file,
f"/cv_predictions/{telescope_type}",
append=True,
)
for metric, value in metrics.items():
scores[metric].append(value)
for metric, cv_values in scores.items():
cv_values = np.array(cv_values)
with np.printoptions(precision=4):
self.log.info(
"Mean %s score from CV: %.4f ± %.4f",
metric,
cv_values.mean(),
cv_values.std(),
)
def _cross_validate_regressor(self, telescope_type, train, test):
regressor = self.model_component
regressor.fit(telescope_type, train)
prediction, _ = regressor._predict(telescope_type, test)
truth = test[regressor.target]
r2 = r2_score(truth, prediction)
result = Table(
data={
f"{regressor.prefix}_energy": prediction,
"truth": truth,
},
descriptions={
f"{regressor.prefix}_energy": "Predicted Energy",
"truth": "Simulated Energy",
},
)
return result, {"R^2": r2}
def _cross_validate_classification(self, telescope_type, train, test):
classifier = self.model_component
classifier.fit(telescope_type, train)
prediction, _ = classifier._predict_score(telescope_type, test)
truth = np.where(
test[classifier.target] == classifier.positive_class,
1,
0,
)
roc_auc = roc_auc_score(truth, prediction)
result = Table(
data={
f"{classifier.prefix}_prediction": prediction,
"truth": truth,
},
descriptions={
f"{classifier.prefix}_prediction": "Predicted gammaness score",
"truth": "Particle id (default is 1 for gammas)",
},
)
return result, {"ROC AUC": roc_auc}
def _cross_validate_disp(self, telescope_type, train, test):
models = self.model_component
models.fit(telescope_type, train)
disp, sign_score, _ = models._predict(telescope_type, test)
truth = test[models.target]
r2 = r2_score(np.abs(truth), np.abs(disp))
accuracy = accuracy_score(np.sign(truth), np.sign(disp))
result = Table(
data={
f"{models.prefix}_parameter": disp,
f"{models.prefix}_sign_score": sign_score,
"truth": truth,
},
descriptions={
f"{models.prefix}_parameter": "Predicted disp parameter",
f"{models.prefix}_sign_score": "Score for how certain the disp sign classification was",
"truth": "True disp parameter",
},
)
return result, {"R^2": r2, "accuracy": accuracy}
