/
example_extending_classification.py
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example_extending_classification.py
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"""
====================================================
Extending Auto-Sklearn with Classification Component
====================================================
The following example demonstrates how to create a new classification
component for using in auto-sklearn.
"""
from typing import Optional
from pprint import pprint
from ConfigSpace.configuration_space import ConfigurationSpace
from ConfigSpace.hyperparameters import (
CategoricalHyperparameter,
UniformIntegerHyperparameter,
UniformFloatHyperparameter,
)
import sklearn.metrics
from autosklearn.askl_typing import FEAT_TYPE_TYPE
import autosklearn.classification
import autosklearn.pipeline.components.classification
from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm
from autosklearn.pipeline.constants import (
DENSE,
SIGNED_DATA,
UNSIGNED_DATA,
PREDICTIONS,
)
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
############################################################################
# Create MLP classifier component for auto-sklearn
# ================================================
class MLPClassifier(AutoSklearnClassificationAlgorithm):
def __init__(
self,
hidden_layer_depth,
num_nodes_per_layer,
activation,
alpha,
solver,
random_state=None,
):
self.hidden_layer_depth = hidden_layer_depth
self.num_nodes_per_layer = num_nodes_per_layer
self.activation = activation
self.alpha = alpha
self.solver = solver
self.random_state = random_state
def fit(self, X, y):
self.num_nodes_per_layer = int(self.num_nodes_per_layer)
self.hidden_layer_depth = int(self.hidden_layer_depth)
self.alpha = float(self.alpha)
from sklearn.neural_network import MLPClassifier
hidden_layer_sizes = tuple(
self.num_nodes_per_layer for i in range(self.hidden_layer_depth)
)
self.estimator = MLPClassifier(
hidden_layer_sizes=hidden_layer_sizes,
activation=self.activation,
alpha=self.alpha,
solver=self.solver,
random_state=self.random_state,
)
self.estimator.fit(X, y)
return self
def predict(self, X):
if self.estimator is None:
raise NotImplementedError()
return self.estimator.predict(X)
def predict_proba(self, X):
if self.estimator is None:
raise NotImplementedError()
return self.estimator.predict_proba(X)
@staticmethod
def get_properties(dataset_properties=None):
return {
"shortname": "MLP Classifier",
"name": "MLP CLassifier",
"handles_regression": False,
"handles_classification": True,
"handles_multiclass": True,
"handles_multilabel": False,
"handles_multioutput": False,
"is_deterministic": False,
# Both input and output must be tuple(iterable)
"input": [DENSE, SIGNED_DATA, UNSIGNED_DATA],
"output": [PREDICTIONS],
}
@staticmethod
def get_hyperparameter_search_space(
feat_type: Optional[FEAT_TYPE_TYPE] = None, dataset_properties=None
):
cs = ConfigurationSpace()
hidden_layer_depth = UniformIntegerHyperparameter(
name="hidden_layer_depth", lower=1, upper=3, default_value=1
)
num_nodes_per_layer = UniformIntegerHyperparameter(
name="num_nodes_per_layer", lower=16, upper=216, default_value=32
)
activation = CategoricalHyperparameter(
name="activation",
choices=["identity", "logistic", "tanh", "relu"],
default_value="relu",
)
alpha = UniformFloatHyperparameter(
name="alpha", lower=0.0001, upper=1.0, default_value=0.0001
)
solver = CategoricalHyperparameter(
name="solver", choices=["lbfgs", "sgd", "adam"], default_value="adam"
)
cs.add_hyperparameters(
[
hidden_layer_depth,
num_nodes_per_layer,
activation,
alpha,
solver,
]
)
return cs
# Add MLP classifier component to auto-sklearn.
autosklearn.pipeline.components.classification.add_classifier(MLPClassifier)
cs = MLPClassifier.get_hyperparameter_search_space()
print(cs)
############################################################################
# Data Loading
# ============
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y)
############################################################################
# Fit MLP classifier to the data
# ==============================
clf = autosklearn.classification.AutoSklearnClassifier(
time_left_for_this_task=30,
per_run_time_limit=10,
include={"classifier": ["MLPClassifier"]},
# Bellow two flags are provided to speed up calculations
# Not recommended for a real implementation
initial_configurations_via_metalearning=0,
smac_scenario_args={"runcount_limit": 5},
)
clf.fit(X_train, y_train)
############################################################################
# Print test accuracy and statistics
# ==================================
y_pred = clf.predict(X_test)
print("accuracy: ", sklearn.metrics.accuracy_score(y_pred, y_test))
pprint(clf.show_models(), indent=4)