2. Hyperparameter search for classification with Tabular data (Keras)#

In this tutorial we present how to use hyperparameter optimization on a basic example from the Keras documentation.

Reference: This tutorial is based on materials from the Keras Documentation: Structured data classification from scratch

Let us start with installing DeepHyper!

Warning

This tutorial should be run with tensorflow>=2.6.

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!pip install deephyper
!pip install ray

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Note

The following environment variables can be used to avoid the logging of some Tensorflow DEBUG, INFO and WARNING statements.

[1]:

import os


os.environ["TF_CPP_MIN_LOG_LEVEL"] = str(3)
os.environ["AUTOGRAPH_VERBOSITY"] = str(0)

2.1. Imports#

Warning

It is important to follow the import strategy import tensorflow as tf to prevent serialization errors that will crash the search.

The import strategy from the original Keras tutorial (shown below),

from tensorflow import keras
from tensorflow.keras import layers
...
from tensorflow.keras.layers import IntegerLookup
from tensorflow.keras.layers import Normalization
from tensorflow.keras.layers import StringLookup

resulted in non-serializable data, preventing the search from executing.

[2]:

import pandas as pd
import tensorflow as tf

Note

The following can be used to detect if GPU devices are available on the current host. Therefore, this notebook will automatically adapt the parallel execution based on the resources available locally. However, this simple code will not detect the ressources from multiple nodes.

[3]:

from tensorflow.python.client import device_lib


def get_available_gpus():
    local_device_protos = device_lib.list_local_devices()
    return [x.name for x in local_device_protos if x.device_type == "GPU"]


n_gpus = len(get_available_gpus())
if n_gpus > 1:
    n_gpus -= 1

is_gpu_available = n_gpus > 0

if is_gpu_available:
    print(f"{n_gpus} GPU{'s are' if n_gpus > 1 else ' is'} available.")
else:
    print("No GPU available")

No GPU available

2.2. The dataset (from Keras.io)#

The dataset is provided by the Cleveland Clinic Foundation for Heart Disease. It’s a CSV file with 303 rows. Each row contains information about a patient (a sample), and each column describes an attribute of the patient (a feature). We use the features to predict whether a patient has a heart disease (binary classification).

Here’s the description of each feature:

Column	Description	Feature Type
Age	Age in years	Numerical
Sex	(1 = male; 0 = female)	Categorical
CP	Chest pain type (0, 1, 2, 3, 4)	Categorical
Trestbpd	Resting blood pressure (in mm Hg on admission)	Numerical
Chol	Serum cholesterol in mg/dl	Numerical
FBS	fasting blood sugar in 120 mg/dl (1 = true; 0 = false)	Categorical
RestECG	Resting electrocardiogram results (0, 1, 2)	Categorical
Thalach	Maximum heart rate achieved	Numerical
Exang	Exercise induced angina (1 = yes; 0 = no)	Categorical
Oldpeak	ST depression induced by exercise relative to rest	Numerical
Slope	Slope of the peak exercise ST segment	Numerical
CA	Number of major vessels (0-3) colored by fluoroscopy	Both numerical & categorical
Thal	3 = normal; 6 = fixed defect; 7 = reversible defect	Categorical
Target	Diagnosis of heart disease (1 = true; 0 = false)	Target

[4]:

def load_data():
    file_url = "http://storage.googleapis.com/download.tensorflow.org/data/heart.csv"
    dataframe = pd.read_csv(file_url)

    val_dataframe = dataframe.sample(frac=0.2, random_state=1337)
    train_dataframe = dataframe.drop(val_dataframe.index)

    return train_dataframe, val_dataframe


def dataframe_to_dataset(dataframe):
    dataframe = dataframe.copy()
    labels = dataframe.pop("target")
    ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
    ds = ds.shuffle(buffer_size=len(dataframe))
    return ds

2.3. Preprocessing & encoding of features#

The next cells use tf.keras.layers.Normalization() to apply standard scaling on the features.

Then, the tf.keras.layers.StringLookup and tf.keras.layers.IntegerLookup are used to encode categorical variables.

[5]:

def encode_numerical_feature(feature, name, dataset):
    # Create a Normalization layer for our feature
    normalizer = tf.keras.layers.Normalization()

    # Prepare a Dataset that only yields our feature
    feature_ds = dataset.map(lambda x, y: x[name])
    feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))

    # Learn the statistics of the data
    normalizer.adapt(feature_ds)

    # Normalize the input feature
    encoded_feature = normalizer(feature)
    return encoded_feature


def encode_categorical_feature(feature, name, dataset, is_string):
    lookup_class = (
        tf.keras.layers.StringLookup if is_string else tf.keras.layers.IntegerLookup
    )
    # Create a lookup layer which will turn strings into integer indices
    lookup = lookup_class(output_mode="binary")

    # Prepare a Dataset that only yields our feature
    feature_ds = dataset.map(lambda x, y: x[name])
    feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))

    # Learn the set of possible string values and assign them a fixed integer index
    lookup.adapt(feature_ds)

    # Turn the string input into integer indices
    encoded_feature = lookup(feature)
    return encoded_feature

2.4. Define the run-function#

The run-function defines how the objective that we want to maximize is computed. It takes a config dictionary as input and often returns a scalar value that we want to maximize. The config contains a sample value of hyperparameters that we want to tune. In this example we will search for:

units (default value: 32)
activation (default value: "relu")
dropout_rate (default value: 0.5)
num_epochs (default value: 50)
batch_size (default value: 32)
learning_rate (default value: 1e-3)

A hyperparameter value can be acessed easily in the dictionary through the corresponding key, for example config["units"].

[6]:

def run(config: dict):
    tf.autograph.set_verbosity(0)
    # Load data and split into validation set
    train_dataframe, val_dataframe = load_data()
    train_ds = dataframe_to_dataset(train_dataframe)
    val_ds = dataframe_to_dataset(val_dataframe)
    train_ds = train_ds.batch(config["batch_size"])
    val_ds = val_ds.batch(config["batch_size"])

    # Categorical features encoded as integers
    sex = tf.keras.Input(shape=(1,), name="sex", dtype="int64")
    cp = tf.keras.Input(shape=(1,), name="cp", dtype="int64")
    fbs = tf.keras.Input(shape=(1,), name="fbs", dtype="int64")
    restecg = tf.keras.Input(shape=(1,), name="restecg", dtype="int64")
    exang = tf.keras.Input(shape=(1,), name="exang", dtype="int64")
    ca = tf.keras.Input(shape=(1,), name="ca", dtype="int64")

    # Categorical feature encoded as string
    thal = tf.keras.Input(shape=(1,), name="thal", dtype="string")

    # Numerical features
    age = tf.keras.Input(shape=(1,), name="age")
    trestbps = tf.keras.Input(shape=(1,), name="trestbps")
    chol = tf.keras.Input(shape=(1,), name="chol")
    thalach = tf.keras.Input(shape=(1,), name="thalach")
    oldpeak = tf.keras.Input(shape=(1,), name="oldpeak")
    slope = tf.keras.Input(shape=(1,), name="slope")

    all_inputs = [
        sex,
        cp,
        fbs,
        restecg,
        exang,
        ca,
        thal,
        age,
        trestbps,
        chol,
        thalach,
        oldpeak,
        slope,
    ]

    # Integer categorical features
    sex_encoded = encode_categorical_feature(sex, "sex", train_ds, False)
    cp_encoded = encode_categorical_feature(cp, "cp", train_ds, False)
    fbs_encoded = encode_categorical_feature(fbs, "fbs", train_ds, False)
    restecg_encoded = encode_categorical_feature(restecg, "restecg", train_ds, False)
    exang_encoded = encode_categorical_feature(exang, "exang", train_ds, False)
    ca_encoded = encode_categorical_feature(ca, "ca", train_ds, False)

    # String categorical features
    thal_encoded = encode_categorical_feature(thal, "thal", train_ds, True)

    # Numerical features
    age_encoded = encode_numerical_feature(age, "age", train_ds)
    trestbps_encoded = encode_numerical_feature(trestbps, "trestbps", train_ds)
    chol_encoded = encode_numerical_feature(chol, "chol", train_ds)
    thalach_encoded = encode_numerical_feature(thalach, "thalach", train_ds)
    oldpeak_encoded = encode_numerical_feature(oldpeak, "oldpeak", train_ds)
    slope_encoded = encode_numerical_feature(slope, "slope", train_ds)

    all_features = tf.keras.layers.concatenate(
        [
            sex_encoded,
            cp_encoded,
            fbs_encoded,
            restecg_encoded,
            exang_encoded,
            slope_encoded,
            ca_encoded,
            thal_encoded,
            age_encoded,
            trestbps_encoded,
            chol_encoded,
            thalach_encoded,
            oldpeak_encoded,
        ]
    )
    x = tf.keras.layers.Dense(config["units"], activation=config["activation"])(
        all_features
    )
    x = tf.keras.layers.Dropout(config["dropout_rate"])(x)
    output = tf.keras.layers.Dense(1, activation="sigmoid")(x)
    model = tf.keras.Model(all_inputs, output)

    optimizer = tf.keras.optimizers.Adam(learning_rate=config["learning_rate"])
    model.compile(optimizer, "binary_crossentropy", metrics=["accuracy"])

    history = model.fit(
        train_ds, epochs=config["num_epochs"], validation_data=val_ds, verbose=0
    )

    return history.history["val_accuracy"][-1]

Note

The objective maximised by DeepHyper is the scalar value returned by the run-function.

In this tutorial it corresponds to the validation accuracy of the last epoch of training which we retrieve in the History object returned by the model.fit(...) call.

...
history = model.fit(
    train_ds, epochs=config["num_epochs"], validation_data=val_ds, verbose=0
)
return history.history["val_accuracy"][-1]
...

Using an objective like max(history.history['val_accuracy']) can have undesired side effects.

For example, it is possible that the training curves will overshoot a local maximum, resulting in a model without the capacity to flexibly adapt to new data in the future.

2.5. Define the Hyperparameter optimization problem#

Hyperparameter ranges are defined using the following syntax:

Discrete integer ranges are generated from a tuple (lower: int, upper: int)
Continuous prarameters are generated from a tuple (lower: float, upper: float)
Categorical or nonordinal hyperparameter ranges can be given as a list of possible values [val1, val2, ...]

[7]:

from deephyper.problem import HpProblem


# Creation of an hyperparameter problem
problem = HpProblem()

# Discrete hyperparameter (sampled with uniform prior)
problem.add_hyperparameter((8, 128), "units", default_value=32)
problem.add_hyperparameter((10, 100), "num_epochs", default_value=50)


# Categorical hyperparameter (sampled with uniform prior)
ACTIVATIONS = [
    "elu", "gelu", "hard_sigmoid", "linear", "relu", "selu",
    "sigmoid", "softplus", "softsign", "swish", "tanh",
]
problem.add_hyperparameter(ACTIVATIONS, "activation", default_value="relu")


# Real hyperparameter (sampled with uniform prior)
problem.add_hyperparameter((0.0, 0.6), "dropout_rate", default_value=0.5)


# Discrete and Real hyperparameters (sampled with log-uniform)
problem.add_hyperparameter((8, 256, "log-uniform"), "batch_size", default_value=32)
problem.add_hyperparameter((1e-5, 1e-2, "log-uniform"), "learning_rate", default_value=1e-3)

problem

[7]:

Configuration space object:
  Hyperparameters:
    activation, Type: Categorical, Choices: {elu, gelu, hard_sigmoid, linear, relu, selu, sigmoid, softplus, softsign, swish, tanh}, Default: relu
    batch_size, Type: UniformInteger, Range: [8, 256], Default: 32, on log-scale
    dropout_rate, Type: UniformFloat, Range: [0.0, 0.6], Default: 0.5
    learning_rate, Type: UniformFloat, Range: [1e-05, 0.01], Default: 0.001, on log-scale
    num_epochs, Type: UniformInteger, Range: [10, 100], Default: 50
    units, Type: UniformInteger, Range: [8, 128], Default: 32

2.6. Evaluate a default configuration#

We evaluate the performance of the default set of hyperparameters provided in the Keras tutorial.

[8]:

import ray


# We launch the Ray run-time depending of the detected local ressources
# and execute the `run` function with the default configuration
# WARNING: in the case of GPUs it is important to follow this scheme
# to avoid multiple processes (Ray workers vs current process) to lock
# the same GPU.
if is_gpu_available:
    if not(ray.is_initialized()):
        ray.init(num_cpus=n_gpus, num_gpus=n_gpus, log_to_driver=False)

    run_default = ray.remote(num_cpus=1, num_gpus=1)(run)
    objective_default = ray.get(run_default.remote(problem.default_configuration))
else:
    if not(ray.is_initialized()):
        ray.init(num_cpus=1, log_to_driver=False)
    run_default = run
    objective_default = run_default(problem.default_configuration)

print(f"Accuracy Default Configuration:  {objective_default:.3f}")

Accuracy Default Configuration:  0.820

2.7. Define the evaluator object#

The Evaluator object allows to change the parallelization backend used by DeepHyper.
It is a standalone object which schedules the execution of remote tasks. All evaluators needs a run_function to be instantiated.
Then a keyword method defines the backend (e.g., "ray") and the method_kwargs corresponds to keyword arguments of this chosen method.

evaluator = Evaluator.create(run_function, method, method_kwargs)

Once created the evaluator.num_workers gives access to the number of available parallel workers.

Finally, to submit and collect tasks to the evaluator one just needs to use the following interface:

configs = [{"units": 8, ...}, ...]
evaluator.submit(configs)
...
# To collect the first finished task (asynchronous)
tasks_done = evaluator.get("BATCH", size=1)

# To collect all of the pending tasks (synchronous)
tasks_done = evaluator.get("ALL")

Warning

Each Evaluator saves its own state, therefore it is crucial to create a new evaluator when launching a fresh search.

[9]:

from deephyper.evaluator import Evaluator
from deephyper.evaluator.callback import TqdmCallback


def get_evaluator(run_function):
    # Default arguments for Ray: 1 worker and 1 worker per evaluation
    method_kwargs = {
        "num_cpus": 1,
        "num_cpus_per_task": 1,
        "callbacks": [TqdmCallback()]
    }

    # If GPU devices are detected then it will create 'n_gpus' workers
    # and use 1 worker for each evaluation
    if is_gpu_available:
        method_kwargs["num_cpus"] = n_gpus
        method_kwargs["num_gpus"] = n_gpus
        method_kwargs["num_cpus_per_task"] = 1
        method_kwargs["num_gpus_per_task"] = 1

    evaluator = Evaluator.create(
        run_function,
        method="ray",
        method_kwargs=method_kwargs
    )
    print(f"Created new evaluator with {evaluator.num_workers} worker{'s' if evaluator.num_workers > 1 else ''} and config: {method_kwargs}", )

    return evaluator

evaluator_1 = get_evaluator(run)

Created new evaluator with 1 worker and config: {'num_cpus': 1, 'num_cpus_per_task': 1, 'callbacks': [<deephyper.evaluator.callback.TqdmCallback object at 0x2a06bb670>]}

/Users/romainegele/Documents/Argonne/deephyper/deephyper/evaluator/_evaluator.py:99: UserWarning: Applying nest-asyncio patch for IPython Shell!
  warnings.warn(

2.8. Define and run the centralized Bayesian optimization search (CBO)#

A primary pillar of hyperparameter search in DeepHyper is given by a centralized Bayesian optimization search (henceforth CBO). CBO may be described in the following algorithm:

Following the parallelized evaluation of these configurations, a low-fidelity and high efficiency model (henceforth “the surrogate”) is devised to reproduce the relationship between the input variables involved in the model (i.e., the choice of hyperparameters) and the outputs (which are generally a measure of validation data accuracy).

After obtaining this surrogate of the validation accuracy, we may utilize ideas from classical methods in Bayesian optimization literature for adaptively sample the search space of hyperparameters.

First, the surrogate is used to obtain an estimate for the mean value of the validation accuracy at a certain sampling location \(x\) in addition to an estimated variance. The latter requirement restricts us to the use of high efficiency data-driven modeling strategies that have inbuilt variance estimates (such as a Gaussian process or Random Forest regressor).

Regions where the mean is high represent opportunities for exploitation and regions where the variance is high represent opportunities for exploration. An optimistic acquisition function called UCB can be constructed using these two quantities:

\[L_{\text{UCB}}(x) = \mu(x) + \kappa \cdot \sigma(x)\]

The unevaluated hyperparameter configurations that maximize the acquisition function are chosen for the next batch of evaluations.

Note that the choice of the variance weighting parameter \(\kappa\) controls the degree of exploration in the hyperparameter search with zero indicating purely exploitation (unseen configurations where the predicted accuracy is highest will be sampled).

The top s configurations are selected for the new batch. The following schematic demonstrates this process:

The process of obtaining s configurations relies on the “constant-liar” strategy where a sampled configuration is mapped to a dummy output given by a bulk metric of all the evaluated configurations thus far (such as the maximum, mean or median validation accuracy).

Prior to sampling the next configuration by acquisition function maximization, the surrogate is retrained with the dummy output as a data point. As the true validation accuracy becomes available for one of the sampled configurations, the dummy output is replaced and the surrogate is updated.

This allows for scalable asynchronous (or batch synchronous) sampling of new hyperparameter configurations.

2.8.1. Choice of surrogate model#

Users should note that our choice of the surrogate is given by the Random Forest regressor due to its ability to handle non-ordinal data (hyperparameter configurations may not be purely continuous or even numerical). Evidence for how they outperform other methods (such as Gaussian processes) is also available in [1]

2.8.1.1. Setup CBO#

We create the CBO using the problem and evaluator defined above.

[10]:

from deephyper.search.hps import CBO
# Uncomment the following line to show the arguments of CBO.
# help(CBO)

[11]:

# Instanciate the search with the problem and the evaluator that we created before

search = CBO(problem, evaluator_1, initial_points=[problem.default_configuration])

Note

All DeepHyper’s search algorithm have two stopping criteria:

    <li> <code>`max_evals (int)`</code>: Defines the maximum number of evaluations that we want to perform. Default to <code>-1</code> for an infinite number.</li>
    <li> <code>`timeout (int)`</code>: Defines a time budget (in seconds) before stopping the search. Default to <code>None</code> for an infinite time budget.</li>
</ul>

[12]:

results = search.search(max_evals=10)

100%|██████████| 10/10 [00:16<00:00,  2.16s/it, objective=0.836]

Warning

The search call does not output any information about the current status of the search. However, a results.csv file is created in the local directly and can be visualized to see finished tasks.

The returned results is a Pandas Dataframe where columns are hyperparameters and information stored by the evaluator:

job_id is a unique identifier corresponding to the order of creation of tasks
objective is the value returned by the run-function
timestamp_submit is the time (in seconds) when the task was created by the evaluator since the creation of the evaluator.
timestamp_gather is the time (in seconds) when the task was received after finishing by the evaluator since the creation of the evaluator.

[13]:

results

[13]:

	activation	batch_size	dropout_rate	learning_rate	num_epochs	units	job_id	objective	timestamp_submit	timestamp_gather
0	relu	32	0.500000	0.001000	50	32	1	0.803279	5.007318	7.747537
1	relu	35	0.033381	0.000265	33	103	2	0.836066	7.893480	9.366301
2	sigmoid	10	0.304321	0.001563	18	116	3	0.819672	9.396822	10.795727
3	tanh	22	0.400652	0.000750	45	120	4	0.786885	10.826229	12.536323
4	relu	13	0.154455	0.003917	55	108	5	0.770492	12.567024	14.700497
5	gelu	167	0.095698	0.000015	70	108	6	0.622951	14.730562	16.405771
6	relu	29	0.474157	0.000866	13	48	7	0.819672	16.436501	17.624854
7	gelu	33	0.513944	0.000103	67	12	8	0.737705	17.655086	19.627776
8	selu	26	0.209993	0.001832	34	45	9	0.786885	19.728662	21.173264
9	elu	58	0.543356	0.000043	94	81	10	0.803279	21.203402	24.535908

The search can be continued without any issue.

[14]:

results = search.search(max_evals=5)

results

100%|██████████| 10/10 [00:18<00:00,  1.82s/it, objective=0.836]
100%|██████████| 5/5 [00:05<00:00,  1.33s/it, objective=0.836]

[14]:

	activation	batch_size	dropout_rate	learning_rate	num_epochs	units	job_id	objective	timestamp_submit	timestamp_gather
0	relu	32	0.500000	0.001000	50	32	1	0.803279	5.007318	7.747537
1	relu	35	0.033381	0.000265	33	103	2	0.836066	7.893480	9.366301
2	sigmoid	10	0.304321	0.001563	18	116	3	0.819672	9.396822	10.795727
3	tanh	22	0.400652	0.000750	45	120	4	0.786885	10.826229	12.536323
4	relu	13	0.154455	0.003917	55	108	5	0.770492	12.567024	14.700497
5	gelu	167	0.095698	0.000015	70	108	6	0.622951	14.730562	16.405771
6	relu	29	0.474157	0.000866	13	48	7	0.819672	16.436501	17.624854
7	gelu	33	0.513944	0.000103	67	12	8	0.737705	17.655086	19.627776
8	selu	26	0.209993	0.001832	34	45	9	0.786885	19.728662	21.173264
9	elu	58	0.543356	0.000043	94	81	10	0.803279	21.203402	24.535908
10	tanh	181	0.585590	0.001198	97	64	11	0.803279	26.024792	27.976763
11	relu	256	0.021215	0.000020	22	123	12	0.180328	28.262971	29.387546
12	relu	256	0.019209	0.000098	12	89	13	0.557377	29.675868	30.765097
13	relu	256	0.580891	0.000016	23	122	14	0.737705	31.123886	32.239641
14	relu	256	0.054057	0.000020	40	128	15	0.245902	32.529887	33.878031

Now that the search is over, let us print the best configuration found during this run.

[15]:

i_max = results.objective.argmax()
best_config = results.iloc[i_max][:-3].to_dict()


print(f"The default configuration has an accuracy of {objective_default:.3f}. \n"
      f"The best configuration found by DeepHyper has an accuracy {results['objective'].iloc[i_max]:.3f}, \n"
      f"discovered after {results['timestamp_gather'].iloc[i_max]:.2f} secondes of search.\n")

best_config

The default configuration has an accuracy of 0.820.
The best configuration found by DeepHyper has an accuracy 0.836,
discovered after 9.37 secondes of search.

[15]:

{'activation': 'relu',
 'batch_size': 35,
 'dropout_rate': 0.0333810292433754,
 'learning_rate': 0.0002654663875892,
 'num_epochs': 33,
 'units': 103,
 'job_id': 2}

2.9. Restart from a checkpoint#

It can often be useful to continue the search from previous results. For example, if the allocation requested was not enough or if an unexpected crash happened. The AMBS searhc provides the fit_surrogate(dataframe_of_results) method for this use case.

To simulate this we create a second evaluator evaluator_2 and start a fresh AMBS search with strong explotation kappa=0.001.

[16]:

# Create a new evaluator
evaluator_2 = get_evaluator(run)

# Create a new AMBS search with strong explotation (i.e., small kappa)
search_from_checkpoint = CBO(problem, evaluator_2, kappa=0.001)

# Initialize surrogate model of Bayesian optization (in AMBS)
# With results of previous search
search_from_checkpoint.fit_surrogate(results)

Created new evaluator with 1 worker and config: {'num_cpus': 1, 'num_cpus_per_task': 1, 'callbacks': [<deephyper.evaluator.callback.TqdmCallback object at 0x3427e3e50>]}

/Users/romainegele/Documents/Argonne/deephyper/deephyper/evaluator/_evaluator.py:99: UserWarning: Applying nest-asyncio patch for IPython Shell!
  warnings.warn(

[17]:

results_from_checkpoint = search_from_checkpoint.search(max_evals=10)

[18]:

results_from_checkpoint

[18]:

	activation	batch_size	dropout_rate	learning_rate	num_epochs	units	job_id	objective	timestamp_submit	timestamp_gather
0	sigmoid	256	0.427160	0.000767	30	70	1	0.704918	1.533085	2.817155
1	relu	256	0.518127	0.002222	32	77	2	0.819672	3.201892	4.484528
2	relu	256	0.569377	0.002478	39	79	3	0.836066	4.789920	6.043683
3	relu	256	0.588966	0.006999	31	75	4	0.819672	6.352603	7.632020
4	relu	256	0.578453	0.000659	54	77	5	0.786885	8.010408	9.583746
5	relu	256	0.572587	0.003358	35	87	6	0.868852	9.889757	11.116933
6	relu	256	0.582392	0.002076	36	86	7	0.852459	11.428699	12.755969
7	relu	256	0.584875	0.003595	43	85	8	0.803279	13.066058	14.417546
8	relu	256	0.536006	0.008031	34	74	9	0.803279	14.798247	16.009628
9	relu	256	0.571806	0.001217	35	76	10	0.803279	16.325069	17.680495

[19]:

i_max = results_from_checkpoint.objective.argmax()
best_config = results_from_checkpoint.iloc[i_max][:-3].to_dict()

print(f"The default configuration has an accuracy of {objective_default:.3f}. "
      f"The best configuration found by DeepHyper has an accuracy {results_from_checkpoint['objective'].iloc[i_max]:.3f}, "
      f"finished after {results_from_checkpoint['timestamp_gather'].iloc[i_max]:.2f} secondes of search.")

best_config

The default configuration has an accuracy of 0.820. The best configuration found by DeepHyper has an accuracy 0.869, finished after 11.12 secondes of search.

[19]:

{'activation': 'relu',
 'batch_size': 256,
 'dropout_rate': 0.5725867102466046,
 'learning_rate': 0.0033577267689664,
 'num_epochs': 35,
 'units': 87,
 'job_id': 6}

2.10. Add conditional hyperparameters#

Now we want to add the possibility to search for a second fully-connected layer. We simply add two new lines:

if config.get("dense_2", False):
    x = tf.keras.layers.Dense(config["dense_2:units"], activation=config["dense_2:activation"])(x)

[20]:

def run_with_condition(config: dict):
    tf.autograph.set_verbosity(0)

    train_dataframe, val_dataframe = load_data()

    train_ds = dataframe_to_dataset(train_dataframe)
    val_ds = dataframe_to_dataset(val_dataframe)

    train_ds = train_ds.batch(config["batch_size"])
    val_ds = val_ds.batch(config["batch_size"])

    # Categorical features encoded as integers
    sex = tf.keras.Input(shape=(1,), name="sex", dtype="int64")
    cp = tf.keras.Input(shape=(1,), name="cp", dtype="int64")
    fbs = tf.keras.Input(shape=(1,), name="fbs", dtype="int64")
    restecg = tf.keras.Input(shape=(1,), name="restecg", dtype="int64")
    exang = tf.keras.Input(shape=(1,), name="exang", dtype="int64")
    ca = tf.keras.Input(shape=(1,), name="ca", dtype="int64")

    # Categorical feature encoded as string
    thal = tf.keras.Input(shape=(1,), name="thal", dtype="string")

    # Numerical features
    age = tf.keras.Input(shape=(1,), name="age")
    trestbps = tf.keras.Input(shape=(1,), name="trestbps")
    chol = tf.keras.Input(shape=(1,), name="chol")
    thalach = tf.keras.Input(shape=(1,), name="thalach")
    oldpeak = tf.keras.Input(shape=(1,), name="oldpeak")
    slope = tf.keras.Input(shape=(1,), name="slope")

    all_inputs = [
        sex,
        cp,
        fbs,
        restecg,
        exang,
        ca,
        thal,
        age,
        trestbps,
        chol,
        thalach,
        oldpeak,
        slope,
    ]

    # Integer categorical features
    sex_encoded = encode_categorical_feature(sex, "sex", train_ds, False)
    cp_encoded = encode_categorical_feature(cp, "cp", train_ds, False)
    fbs_encoded = encode_categorical_feature(fbs, "fbs", train_ds, False)
    restecg_encoded = encode_categorical_feature(restecg, "restecg", train_ds, False)
    exang_encoded = encode_categorical_feature(exang, "exang", train_ds, False)
    ca_encoded = encode_categorical_feature(ca, "ca", train_ds, False)

    # String categorical features
    thal_encoded = encode_categorical_feature(thal, "thal", train_ds, True)

    # Numerical features
    age_encoded = encode_numerical_feature(age, "age", train_ds)
    trestbps_encoded = encode_numerical_feature(trestbps, "trestbps", train_ds)
    chol_encoded = encode_numerical_feature(chol, "chol", train_ds)
    thalach_encoded = encode_numerical_feature(thalach, "thalach", train_ds)
    oldpeak_encoded = encode_numerical_feature(oldpeak, "oldpeak", train_ds)
    slope_encoded = encode_numerical_feature(slope, "slope", train_ds)

    all_features = tf.keras.layers.concatenate(
        [
            sex_encoded,
            cp_encoded,
            fbs_encoded,
            restecg_encoded,
            exang_encoded,
            slope_encoded,
            ca_encoded,
            thal_encoded,
            age_encoded,
            trestbps_encoded,
            chol_encoded,
            thalach_encoded,
            oldpeak_encoded,
        ]
    )
    x = tf.keras.layers.Dense(config["units"], activation=config["activation"])(
        all_features
    )

    ### START - NEW LINES
    if config.get("dense_2", False):
        x = tf.keras.layers.Dense(config["dense_2:units"], activation=config["dense_2:activation"])(x)
    ### END - NEW LINES

    x = tf.keras.layers.Dropout(config["dropout_rate"])(x)
    output = tf.keras.layers.Dense(1, activation="sigmoid")(x)
    model = tf.keras.Model(all_inputs, output)

    optimizer = tf.keras.optimizers.Adam(learning_rate=config["learning_rate"])
    model.compile(optimizer, "binary_crossentropy", metrics=["accuracy"])

    history = model.fit(
        train_ds, epochs=config["num_epochs"], validation_data=val_ds, verbose=0
    )

    return history.history["val_accuracy"][-1]

To define conditionnal hyperparameters we use ConfigSpace. We define dense_2:units and dense_2:activation as active hyperparameters only when dense_2 == True. The cs.EqualsCondition help us do that. Then we call

problem_with_condition.add_condition(condition)

to register each new condition to the HpProblem.

[21]:

from deephyper.problem import EqualsCondition

# Define the hyperparameter problem
problem_with_condition = HpProblem()


# Define the same hyperparameters as before
problem_with_condition.add_hyperparameter((8, 128), "units")
problem_with_condition.add_hyperparameter(ACTIVATIONS, "activation")
problem_with_condition.add_hyperparameter((0.0, 0.6), "dropout_rate")
problem_with_condition.add_hyperparameter((10, 100), "num_epochs")
problem_with_condition.add_hyperparameter((8, 256, "log-uniform"), "batch_size")
problem_with_condition.add_hyperparameter((1e-5, 1e-2, "log-uniform"), "learning_rate")


# Add a new hyperparameter "dense_2 (bool)" to decide if a second fully-connected layer should be created
hp_dense_2 = problem_with_condition.add_hyperparameter([True, False], "dense_2")
hp_dense_2_units = problem_with_condition.add_hyperparameter((8, 128), "dense_2:units")
hp_dense_2_activation = problem_with_condition.add_hyperparameter(ACTIVATIONS, "dense_2:activation")

problem_with_condition.add_condition(EqualsCondition(hp_dense_2_units, hp_dense_2, True))
problem_with_condition.add_condition(EqualsCondition(hp_dense_2_activation, hp_dense_2, True))


problem_with_condition

[21]:

Configuration space object:
  Hyperparameters:
    activation, Type: Categorical, Choices: {elu, gelu, hard_sigmoid, linear, relu, selu, sigmoid, softplus, softsign, swish, tanh}, Default: elu
    batch_size, Type: UniformInteger, Range: [8, 256], Default: 45, on log-scale
    dense_2, Type: Categorical, Choices: {True, False}, Default: True
    dense_2:activation, Type: Categorical, Choices: {elu, gelu, hard_sigmoid, linear, relu, selu, sigmoid, softplus, softsign, swish, tanh}, Default: elu
    dense_2:units, Type: UniformInteger, Range: [8, 128], Default: 68
    dropout_rate, Type: UniformFloat, Range: [0.0, 0.6], Default: 0.3
    learning_rate, Type: UniformFloat, Range: [1e-05, 0.01], Default: 0.0003162278, on log-scale
    num_epochs, Type: UniformInteger, Range: [10, 100], Default: 55
    units, Type: UniformInteger, Range: [8, 128], Default: 68
  Conditions:
    dense_2:activation | dense_2 == True
    dense_2:units | dense_2 == True

We create a new evaluator evaluator_3 and start a fresh AMBS search with this new problem problem_with_condition.

[22]:

evaluator_3 = get_evaluator(run_with_condition)

search_with_condition = CBO(problem_with_condition, evaluator_3)

/Users/romainegele/Documents/Argonne/deephyper/deephyper/evaluator/_evaluator.py:99: UserWarning: Applying nest-asyncio patch for IPython Shell!
  warnings.warn(

Created new evaluator with 1 worker and config: {'num_cpus': 1, 'num_cpus_per_task': 1, 'callbacks': [<deephyper.evaluator.callback.TqdmCallback object at 0x341d4e970>]}

[23]:

results_with_condition = search_with_condition.search(max_evals=10)

[24]:

results_with_condition

[24]:

	activation	batch_size	dense_2	dropout_rate	learning_rate	num_epochs	units	dense_2:activation	dense_2:units	job_id	objective	timestamp_submit	timestamp_gather
0	gelu	24	False	0.522131	0.001057	44	8	NaN	NaN	1	0.836066	3.763403	5.403222
1	softsign	47	True	0.377181	0.000019	47	127	elu	75.0	2	0.786885	5.769829	7.444927
2	hard_sigmoid	25	False	0.333931	0.000461	12	33	NaN	NaN	3	0.786885	7.735531	8.905337
3	softsign	8	True	0.203617	0.000153	77	24	softplus	20.0	4	0.836066	9.270190	12.828704
4	tanh	232	False	0.544339	0.000236	17	15	NaN	NaN	5	0.245902	13.121197	14.197206
5	linear	31	True	0.431446	0.002055	27	114	tanh	69.0	6	0.803279	14.490091	15.919840
6	swish	185	True	0.143728	0.000066	36	126	relu	88.0	7	0.819672	16.282540	17.669768
7	selu	85	True	0.513058	0.000975	20	24	elu	77.0	8	0.836066	17.957845	19.113598
8	softplus	256	False	0.541869	0.000032	44	123	NaN	NaN	9	0.213115	19.404429	20.872728
9	hard_sigmoid	57	True	0.221024	0.000014	77	101	selu	56.0	10	0.770492	21.233461	23.194847

Finally, let us print out the best configuration found from this conditionned search space.

[25]:

i_max = results_with_condition.objective.argmax()
best_config = results_with_condition.iloc[i_max][:-3].to_dict()

print(f"The default configuration has an accuracy of {objective_default:.3f}. "
      f"The best configuration found by DeepHyper has an accuracy {results_with_condition['objective'].iloc[i_max]:.3f}, "
      f"finished after {results_with_condition['timestamp_gather'].iloc[i_max]:.2f} seconds of search.")

best_config

The default configuration has an accuracy of 0.820. The best configuration found by DeepHyper has an accuracy 0.836, finished after 5.40 seconds of search.

[25]:

{'activation': 'gelu',
 'batch_size': 24,
 'dense_2': False,
 'dropout_rate': 0.5221310827728567,
 'learning_rate': 0.0010573740607372,
 'num_epochs': 44,
 'units': 8,
 'dense_2:activation': nan,
 'dense_2:units': nan,
 'job_id': 1}

Hyperparameter search for classification with Tabular data (Keras)

Contents

2. Hyperparameter search for classification with Tabular data (Keras)#

2.1. Imports#

2.2. The dataset (from Keras.io)#

2.3. Preprocessing & encoding of features#

2.4. Define the run-function#

2.5. Define the Hyperparameter optimization problem#

2.6. Evaluate a default configuration#

2.7. Define the evaluator object#

2.8. Define and run the centralized Bayesian optimization search (CBO)#

2.8.1. Choice of surrogate model#

2.8.1.1. Setup CBO#

2.9. Restart from a checkpoint#

2.10. Add conditional hyperparameters#