hyperactive 0.4.1.1

A hyperparameter optimization toolbox for convenient and fast prototyping

Hyperactive

A hyperparameter optimization toolbox for convenient and fast prototyping

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Overview | Performance | Installation | Examples | Advanced Features | Hyperactive API | License

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Overview

• Optimize hyperparameters of machine- or deep-learning models, using a simple API.
• Choose from a variety of different optimization techniques to improve your model.
• Never lose progress of previous optimizations: Just pass one or more models as start points and continue optimizing.
• Use transfer learning during the optimization process to build a more accurate model, while saving training and optimization time.
• Utilize multiprocessing for machine learning or your gpu for deep learning models.

Optimization Techniques	Supported Packages
Local Search: Hill Climbing Stochastic Hill Climbing Tabu Search Random Methods: Random Search Random Restart Hill Climbing Random Annealing Markov Chain Monte Carlo: Simulated Annealing Stochastic Tunneling Parallel Tempering Population Methods: Particle Swarm Optimizer Evolution Strategy Sequential Methods: Bayesian Optimization	Machine Learning: Scikit-learn XGBoost Deep Learning: Keras Distribution: Multiprocessing

Performance

The bar chart below shows, that the optimization process itself represents only a small fraction (<0.6%) of the computation time. The 'No Opt'-bar shows the training time of a default Gradient-Boosting-Classifier normalized to 1. The other bars show the computation time relative to 'No Opt'. Each optimizer did 30 runs of 300 iterations, to get a good statistic.

Installation

Hyperactive is developed and tested in python 3:

Hyperactive is available on PyPi:

pip install hyperactive

Examples

Basic sklearn example:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

from hyperactive import SimulatedAnnealingOptimizer

iris_data = load_iris()
X = iris_data.data
y = iris_data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

# this defines the model and hyperparameter search space
search_config = {
    "sklearn.ensemble.RandomForestClassifier": {
        "n_estimators": range(10, 100, 10),
        "max_depth": [3, 4, 5, 6],
        "criterion": ["gini", "entropy"],
        "min_samples_split": range(2, 21),
        "min_samples_leaf": range(2, 21),
    }
}

Optimizer = SimulatedAnnealingOptimizer(search_config, n_iter=100, n_jobs=4)

# search best hyperparameter for given data
Optimizer.fit(X_train, y_train)

# predict from test data
prediction = Optimizer.predict(X_test)

# calculate accuracy score
score = Optimizer.score(X_test, y_test)

Example with a multi-layer-perceptron in keras:

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from hyperactive import ParticleSwarmOptimizer

breast_cancer_data = load_breast_cancer()

X = breast_cancer_data.data
y = breast_cancer_data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

# this defines the structure of the model and the search space in each layer
search_config = {
    "keras.compile.0": {"loss": ["binary_crossentropy"], "optimizer": ["adam"]},
    "keras.fit.0": {"epochs": [3], "batch_size": [100], "verbose": [0]},
    "keras.layers.Dense.1": {
        "units": range(5, 15),
        "activation": ["relu"],
        "kernel_initializer": ["uniform"],
    },
    "keras.layers.Dense.2": {
        "units": range(5, 15),
        "activation": ["relu"],
        "kernel_initializer": ["uniform"],
    },
    "keras.layers.Dense.3": {"units": [1], "activation": ["sigmoid"]},
}

Optimizer = ParticleSwarmOptimizer(
    search_config, n_iter=3, metric=["mean_absolute_error"], verbosity=0
)
# search best hyperparameter for given data
Optimizer.fit(X_train, y_train)

Example with a convolutional neural network in keras:

import numpy as np

from keras.datasets import mnist
from keras.utils import to_categorical

from hyperactive import RandomSearchOptimizer

(X_train, y_train), (X_test, y_test) = mnist.load_data()

X_train = X_train.reshape(60000, 28, 28, 1)
X_test = X_test.reshape(10000, 28, 28, 1)

y_train = to_categorical(y_train)
y_test = to_categorical(y_test)


# this defines the structure of the model and the search space in each layer
search_config = {
    "keras.compile.0": {"loss": ["categorical_crossentropy"], "optimizer": ["adam"]},
    "keras.fit.0": {"epochs": [20], "batch_size": [500], "verbose": [2]},
    "keras.layers.Conv2D.1": {
        "filters": [32, 64, 128],
        "kernel_size": range(3, 4),
        "activation": ["relu"],
        "input_shape": [(28, 28, 1)],
    },
    "keras.layers.MaxPooling2D.2": {"pool_size": [(2, 2)]},
    "keras.layers.Conv2D.3": {
        "filters": [16, 32, 64],
        "kernel_size": [3],
        "activation": ["relu"],
    },
    "keras.layers.MaxPooling2D.4": {"pool_size": [(2, 2)]},
    "keras.layers.Flatten.5": {},
    "keras.layers.Dense.6": {"units": range(30, 200, 10), "activation": ["softmax"]},
    "keras.layers.Dropout.7": {"rate": list(np.arange(0.4, 0.8, 0.1))},
    "keras.layers.Dense.8": {"units": [10], "activation": ["softmax"]},
}

Optimizer = RandomSearchOptimizer(search_config, n_iter=20)

# search best hyperparameter for given data
Optimizer.fit(X_train, y_train)

# predict from test data
prediction = Optimizer.predict(X_test)

# calculate accuracy score
score = Optimizer.score(X_test, y_test)

Advanced Features

The features listed below can be activated during the instantiation of the optimizer (see API) and works with every optimizer in the hyperactive package.

Memory

After the evaluation of a model the position (in the hyperparameter search dictionary) and the cross-validation score are written to a dictionary. If the optimizer tries to evaluate this position again it can quickly lookup if a score for this position is present and use it instead of going through the extensive training and prediction process.

Scatter-Initialization

This technique was inspired by the 'Hyperband Optimization' and aims to find a good initial position for the optimization. It does so by evaluating n random positions with a training subset of 1/n the size of the original dataset. The position that achieves the best score is used as the starting position for the optimization.

Multiprocessing

The multiprocessing in hyperactive works by creating additional searches, that run in parallel without any shared memory. This provides the possibility of hyperparameter-tuning of different models at the same time. If one single model should be tuned as fast as possible n_jobs in the optimizer should be set to '1', while n_jobs (of the model) in the search_config should be set to '-1'.

Two searches with eight cpu-cores:

import numpy as np

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

from hyperactive import RandomSearchOptimizer

iris_data = load_iris()
X = iris_data.data
y = iris_data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

# this defines the model and hyperparameter search space
search_config = {
    "sklearn.ensemble.RandomForestClassifier": {
        "n_estimators": range(10, 100, 10),
        "max_depth": [3, 4, 5, 6],
        "criterion": ["gini", "entropy"],
        "n_jobs": [4],
    },
    "sklearn.ensemble.GradientBoostingClassifier": {
        "n_estimators": range(10, 100, 10),
        "max_depth": range(1, 11),
        "min_samples_split": range(2, 21),
        "n_jobs": [4],
    },
}

Optimizer = RandomSearchOptimizer(search_config, n_iter=300, n_jobs=2, verbosity=0)

# search best hyperparameter for given data
Optimizer.fit(X_train, y_train)

One search with all cpu-cores:

import numpy as np

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

from hyperactive import RandomSearchOptimizer

iris_data = load_iris()
X = iris_data.data
y = iris_data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

# this defines the model and hyperparameter search space
search_config = {
    "sklearn.ensemble.RandomForestClassifier": {
        "n_estimators": range(10, 100, 10),
        "max_depth": [3, 4, 5, 6],
        "criterion": ["gini", "entropy"],
        "n_jobs": [-1],
    },
}

Optimizer = RandomSearchOptimizer(search_config, n_iter=300, n_jobs=1, verbosity=0)

# search best hyperparameter for given data
Optimizer.fit(X_train, y_train)

Multiple searches with all cpu-cores:

import numpy as np

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split

from hyperactive import RandomSearchOptimizer

iris_data = load_iris()
X = iris_data.data
y = iris_data.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

# this defines the model and hyperparameter search space
search_config = {
    "sklearn.ensemble.RandomForestClassifier": {
        "n_estimators": range(10, 100, 10),
        "max_depth": [3, 4, 5, 6],
        "criterion": ["gini", "entropy"],
        "min_samples_split": range(2, 21),
        "min_samples_leaf": range(2, 21),
    },
    "sklearn.neighbors.KNeighborsClassifier": {
        "n_neighbors": range(1, 10),
        "weights": ["uniform", "distance"],
        "p": [1, 2],
    },
    "sklearn.ensemble.GradientBoostingClassifier": {
        "n_estimators": range(10, 100, 10),
        "learning_rate": [1e-3, 1e-2, 1e-1, 0.5, 1.0],
        "max_depth": range(1, 11),
        "min_samples_split": range(2, 21),
        "min_samples_leaf": range(1, 21),
        "subsample": np.arange(0.05, 1.01, 0.05),
        "max_features": np.arange(0.05, 1.01, 0.05),
    },
    "sklearn.tree.DecisionTreeClassifier": {
        "criterion": ["gini", "entropy"],
        "max_depth": range(1, 11),
        "min_samples_split": range(2, 21),
        "min_samples_leaf": range(1, 21),
    },
}

Optimizer = RandomSearchOptimizer(search_config, n_iter=300, n_jobs=-1, verbosity=0)

Transfer-Learning

In the current implementation transfer-learning works by using a predefined model (with optional pretrained weights) provided by the keras package. The import path can be inserted as a layer (with its parameters in an sub-dictionary), like in a regular search dictionary. The following snippet provides an example:

Transfer-learning example:

from keras.datasets import cifar10
from keras.utils import to_categorical

from hyperactive import SimulatedAnnealingOptimizer

(X_train, y_train), (X_test, y_test) = cifar10.load_data()

y_train = to_categorical(y_train)
y_test = to_categorical(y_test)


# this defines the structure of the model and the search space in each layer
search_config = {
    "keras.compile.0": {"loss": ["binary_crossentropy"], "optimizer": ["adam"]},
    "keras.fit.0": {"epochs": [1], "batch_size": [300], "verbose": [0]},
    # just add the pretrained model as a layer like this:
    "keras.applications.MobileNet.1": {
        "weights": ["imagenet"],
        "input_shape": [(32, 32, 3)],
        "include_top": [False],
    },
    "keras.layers.Flatten.2": {},
    "keras.layers.Dense.3": {
        "units": range(5, 15),
        "activation": ["relu"],
        "kernel_initializer": ["uniform"],
    },
    "keras.layers.Dense.4": {"units": [10], "activation": ["sigmoid"]},
}


Optimizer = SimulatedAnnealingOptimizer(
    search_config, n_iter=3, warm_start=False, verbosity=0
)

Warm-Start

When a search is finished the warm-start-dictionary for the best position in the hyperparameter search space (and its metric) is printed in the command line (at verbosity=1). If multiple searches ran in parallel the warm-start-dictionaries are sorted by the best metric in decreasing order. If the start position in the warm-start-dictionary is not within the search space defined in the search_config an error will occure.

Warm-start example for sklearn model:

start_point = {
    "sklearn.ensemble.RandomForestClassifier.0": {
        "n_estimators": [30],
        "max_depth": [6],
        "criterion": ["entropy"],
        "min_samples_split": [12],
        "min_samples_leaf": [16],
    },
    "sklearn.ensemble.RandomForestClassifier.1": {
        "n_estimators": [50],
        "max_depth": [3],
        "criterion": ["entropy"],
    },
}

Warm-start example for keras model (cnn):

start_point = {
    "keras.compile.0": {"loss": ["categorical_crossentropy"], "optimizer": ["adam"]},
    "keras.fit.0": {"epochs": [3], "batch_size": [500], "verbose": [0]},
    "keras.layers.Conv2D.1": {
        "filters": [64],
        "kernel_size": [3],
        "activation": ["relu"],
        "input_shape": [(28, 28, 1)],
    },
    "keras.layers.MaxPooling2D.2": {"pool_size": [(2, 2)]},
    "keras.layers.Conv2D.3": {
        "filters": [32],
        "kernel_size": [3],
        "activation": ["relu"],
        "input_shape": [(28, 28, 1)],
    },
    "keras.layers.MaxPooling2D.4": {"pool_size": [(2, 2)]},
    "keras.layers.Conv2D.5": {
        "filters": [32],
        "kernel_size": [3],
        "activation": ["relu"],
        "input_shape": [(28, 28, 1)],
    },
    "keras.layers.MaxPooling2D.6": {"pool_size": [(2, 2)]},
    "keras.layers.Flatten.7": {},
    "keras.layers.Dense.8": {"units": [50], "activation": ["softmax"]},
    "keras.layers.Dropout.9": {"rate": [0.4]},
"keras.layers.Dense.10": {"units": [10], "activation": ["softmax"]},
}

Hyperactive API

Classes:

HillClimbingOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False, eps=1, r=1e-6)
StochasticHillClimbingOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False)
TabuOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False, eps=1, tabu_memory=[3, 6, 9])

RandomSearchOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False)
RandomRestartHillClimbingOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False, n_restarts=10)
RandomAnnealingOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False, eps=100, t_rate=0.98)

SimulatedAnnealingOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False, eps=1, t_rate=0.98)
StochasticTunnelingOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False, eps=1, t_rate=0.98, n_neighbours=1, gamma=1)
ParallelTemperingOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False, eps=1, t_rate=0.98, n_neighbours=1, system_temps=[0.1, 0.2, 0.01], n_swaps=10)

ParticleSwarmOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False, n_part=4, w=0.5, c_k=0.5, c_s=0.9)
EvolutionStrategyOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False, individuals=10, mutation_rate=0.7, crossover_rate=0.3)

BayesianOptimizer(search_config, n_iter, metric="accuracy", n_jobs=1, cv=5, verbosity=1, random_state=None, warm_start=False, memory=True, scatter_init=False)

General positional argument:

Argument	Type	Description
search_config	dict	hyperparameter search space to explore by the optimizer
n_iter	int	number of iterations to perform

General keyword arguments:

Argument	Type	Default	Description
metric	str	"accuracy"	metric for model evaluation
n_jobs	int	1	number of jobs to run in parallel (-1 for maximum)
cv	int	5	cross-validation
verbosity	int	1	Shows model and metric information
random_state	int	None	The seed for random number generator
warm_start	dict	None	Hyperparameter configuration to start from
memory	bool	True	Stores explored evaluations in a dictionary to save computing time
scatter_init	int	False	Chooses better initial position by training on multiple random positions with smaller training dataset (split into int subsets)

Specific keyword arguments:

Hill Climbing

Argument	Type	Default	Description
eps	int	1	epsilon

Stochastic Hill Climbing

Argument	Type	Default	Description
eps	int	1	epsilon
r	float	1e-6	acceptance factor

Tabu Search

Argument	Type	Default	Description
eps	int	1	epsilon
tabu_memory	list	[3, 6, 9]	length of short/mid/long-term memory

Random Restart Hill Climbing

Argument	Type	Default	Description
eps	int	1	epsilon
n_restarts	int	10	number of restarts

Random Annealing

Argument	Type	Default	Description
eps	int	100	epsilon
t_rate	float	0.98	cooling rate

Simulated Annealing

Argument	Type	Default	Description
eps	int	1	epsilon
t_rate	float	0.98	cooling rate

Stochastic Tunneling

Argument	Type	Default	Description
eps	int	1	epsilon
t_rate	float	0.98	cooling rate
gamma	float	1	tunneling factor

Parallel Tempering

Argument	Type	Default	Description
eps	int	1	epsilon
t_rate	float	0.98	cooling rate
system_temps	list	[0.1, 0.2, 0.01]	initial temperatures (number of elements defines number of systems)
n_swaps	int	10	number of swaps

Particle Swarm Optimization

Argument	Type	Default	Description
n_part	int	1	number of particles
w	float	0.5	intertia factor
c_k	float	0.8	cognitive factor
c_s	float	0.9	social factor

Evolution Strategy

Argument	Type	Default	Description
individuals	int	10	number of individuals
mutation_rate	float	0.7	mutation rate
crossover_rate	float	0.3	crossover rate

Bayesian Optimization

Argument	Type	Default	Description
kernel	class	Matern	Kernel used for the gaussian process

General methods:

fit(self, X_train, y_train)

Argument	Type	Description
X_train	array-like	training input features
y_train	array-like	training target

predict(self, X_test)

Argument	Type	Description
X_test	array-like	testing input features

score(self, X_test, y_test)

Argument	Type	Description
X_test	array-like	testing input features
y_test	array-like	true values

export(self, filename)

Argument	Type	Description
filename	str	file name and path for model export

Available Metrics:

Scikit-learn

Machine Learning Scores	Machine Learning Losses
accuracy_score	brier_score_loss
balanced_accuracy_score	log_loss
average_precision_score	max_error
f1_score	mean_absolute_error
recall_score	mean_squared_error
jaccard_score	mean_squared_log_error
roc_auc_score	median_absolute_error
explained_variance_score

Keras

Deep Learning Scores	Deep Learning Losses
accuracy	mean_squared_error
binary_accuracy	mean_absolute_error
categorical_accuracy	mean_absolute_percentage_error
sparse_categorical_accuracy	mean_squared_logarithmic_error
top_k_categorical_accuracy	squared_hinge
sparse_top_k_categorical_accuracy	hinge
	categorical_hinge
	logcosh
	categorical_crossentropy
	sparse_categorical_crossentropy
	binary_crossentropy
	kullback_leibler_divergence
	poisson
	cosine_proximity

License