5. Automated Machine Learning with Scikit-Learn#
In this tutorial, we will show how to automatically search among different machine learning algorithms from Scikit-Learn. Automated machine learning only requires the user to link the data with a predifined problem and run function that we provide.
Let us start by installing DeepHyper.
[1]:
!pip install deephyper["popt"]
!pip install ray
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5.1. Classification#
On this part of the tutorial we focus on the classification case.
Create run function to train and evaluate the model corresponding to the configuration generated by the search. This function has to return a scalar value (typically, validation accuracy), which will be maximized by the search algorithm. In the case of automated machine learning we use the run function provided at deephyper.sklearn.classifier.run_autosklearn1 and wrap it with our data such as:
[1]:
from deephyper.sklearn.classifier import run_autosklearn1
def load_data():
from sklearn.datasets import load_breast_cancer
X, y = load_breast_cancer(return_X_y=True)
return X, y
def run(config):
return run_autosklearn1(config, load_data)
We are ready to go! But, let us look at the problem provided by DeepHyper in deephyper.sklearn.classifier.problem_autosklearn1 to understand better what is happening under the hood.
[2]:
from deephyper.sklearn.classifier import problem_autosklearn1
problem_autosklearn1
[2]:
Configuration space object:
Hyperparameters:
C, Type: UniformFloat, Range: [1e-05, 10.0], Default: 0.01, on log-scale
alpha, Type: UniformFloat, Range: [1e-05, 10.0], Default: 0.01, on log-scale
classifier, Type: Categorical, Choices: {RandomForest, Logistic, AdaBoost, KNeighbors, MLP, SVC, XGBoost}, Default: RandomForest
gamma, Type: UniformFloat, Range: [1e-05, 10.0], Default: 0.01, on log-scale
kernel, Type: Categorical, Choices: {linear, poly, rbf, sigmoid}, Default: linear
max_depth, Type: UniformInteger, Range: [2, 100], Default: 14, on log-scale
n_estimators, Type: UniformInteger, Range: [1, 2000], Default: 45, on log-scale
n_neighbors, Type: UniformInteger, Range: [1, 100], Default: 50
Conditions:
(C | classifier == 'Logistic' || C | classifier == 'SVC')
(gamma | kernel == 'rbf' || gamma | kernel == 'poly' || gamma | kernel == 'sigmoid')
(n_estimators | classifier == 'RandomForest' || n_estimators | classifier == 'AdaBoost')
alpha | classifier == 'MLP'
kernel | classifier == 'SVC'
max_depth | classifier == 'RandomForest'
n_neighbors | classifier == 'KNeighbors'
Create an Evaluator object using the ray backend to distribute the evaluation of the run-function defined previously.
[3]:
from deephyper.evaluator import Evaluator
from deephyper.evaluator.callback import TqdmCallback
evaluator = Evaluator.create(run,
method="ray",
method_kwargs={
"address": None,
"num_cpus": 1,
"num_cpus_per_task": 1,
"callbacks": [TqdmCallback()]
})
print("Number of workers: ", evaluator.num_workers)
Number of workers: 1
Finally, you can define a Bayesian optimization search called CBO (for Centralized Bayesian Optimization) and link to it the defined problem_autosklearn1 and evaluator.
[4]:
from deephyper.search.hps import CBO
search = CBO(problem_autosklearn1, evaluator, log_dir="exp-automl-2")
[5]:
results = search.search(100)
(pid=35708) /Users/romainegele/miniforge3/envs/dh-arm/lib/python3.9/site-packages/xgboost/compat.py:31: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.
(pid=35708) from pandas import MultiIndex, Int64Index
(run pid=35708) /Users/romainegele/miniforge3/envs/dh-arm/lib/python3.9/site-packages/sklearn/neighbors/_classification.py:228: FutureWarning: Unlike other reduction functions (e.g. `skew`, `kurtosis`), the default behavior of `mode` typically preserves the axis it acts along. In SciPy 1.11.0, this behavior will change: the default value of `keepdims` will become False, the `axis` over which the statistic is taken will be eliminated, and the value None will no longer be accepted. Set `keepdims` to True or False to avoid this warning.
(run pid=35708) mode, _ = stats.mode(_y[neigh_ind, k], axis=1)
Once the search is over, a file named results.csv is saved in the current directory. The same dataframe is returned by the search.search(...) call. It contains the hyperparameters configurations evaluated during the search and their corresponding objective value (i.e, validation accuracy), timestamp_submit the time when the evaluator submitted the configuration to be evaluated and timestamp_gather the time when the evaluator received the configuration once evaluated (both are
relative times with respect to the creation of the Evaluator instance).
[6]:
results
[6]:
| p:classifier | p:C | p:alpha | p:kernel | p:max_depth | p:n_estimators | p:n_neighbors | p:gamma | objective | job_id | m:timestamp_submit | m:timestamp_gather | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Logistic | 0.000986 | NaN | NaN | NaN | NaN | NaN | NaN | 0.893617 | 0 | 1.847845 | 4.197699 |
| 1 | KNeighbors | NaN | NaN | NaN | NaN | NaN | 41.0 | NaN | 0.946809 | 1 | 4.348262 | 4.359891 |
| 2 | RandomForest | NaN | NaN | NaN | 48.0 | 51.0 | NaN | NaN | 0.957447 | 2 | 4.588290 | 4.632172 |
| 3 | Logistic | 0.000341 | NaN | NaN | NaN | NaN | NaN | NaN | 0.819149 | 3 | 4.764461 | 4.772226 |
| 4 | SVC | 0.000063 | NaN | linear | NaN | NaN | NaN | NaN | 0.643617 | 4 | 4.907278 | 4.917221 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 95 | MLP | NaN | 1.782067 | NaN | NaN | NaN | NaN | NaN | 0.989362 | 95 | 37.920646 | 38.037480 |
| 96 | MLP | NaN | 1.742599 | NaN | NaN | NaN | NaN | NaN | 0.989362 | 96 | 38.265214 | 38.381637 |
| 97 | MLP | NaN | 1.769931 | NaN | NaN | NaN | NaN | NaN | 0.989362 | 97 | 38.609994 | 38.726015 |
| 98 | MLP | NaN | 2.019310 | NaN | NaN | NaN | NaN | NaN | 0.989362 | 98 | 39.031003 | 39.148036 |
| 99 | MLP | NaN | 1.862691 | NaN | NaN | NaN | NaN | NaN | 0.989362 | 99 | 39.378585 | 39.494249 |
100 rows × 12 columns
Now that we have the full list of results we can display the top-3.
[7]:
results.nlargest(n=3, columns="objective")
[7]:
| p:classifier | p:C | p:alpha | p:kernel | p:max_depth | p:n_estimators | p:n_neighbors | p:gamma | objective | job_id | m:timestamp_submit | m:timestamp_gather | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 10 | MLP | NaN | 3.685755 | NaN | NaN | NaN | NaN | NaN | 0.989362 | 10 | 6.240106 | 6.363712 |
| 12 | MLP | NaN | 3.717318 | NaN | NaN | NaN | NaN | NaN | 0.989362 | 12 | 7.013312 | 7.129871 |
| 13 | MLP | NaN | 2.902145 | NaN | NaN | NaN | NaN | NaN | 0.989362 | 13 | 7.361486 | 7.477184 |
5.2. Regression#
On this part of the tutorial we focus on the regression case.
Create run function to train and evaluate the model corresponding to the configuration generated by the search. This function has to return a scalar value (typically, validation \(R^2\)), which will be maximized by the search algorithm. In the case of automated machine learning we use the run-function provided at deephyper.sklearn.regressor.run_autosklearn1 and wrap it with our data such as:
[8]:
from deephyper.sklearn.regressor import run_autosklearn1
def load_data():
from sklearn.datasets import fetch_california_housing
X, y = fetch_california_housing(return_X_y=True)
return X, y
def run(config):
return run_autosklearn1(config, load_data)
We are ready to go! But, let us look at the problem provided by DeepHyper to understand better what is happening under the hood.
[9]:
from deephyper.sklearn.regressor import problem_autosklearn1
problem_autosklearn1
[9]:
Configuration space object:
Hyperparameters:
C, Type: UniformFloat, Range: [1e-05, 10.0], Default: 0.01, on log-scale
alpha, Type: UniformFloat, Range: [1e-05, 10.0], Default: 0.01, on log-scale
gamma, Type: UniformFloat, Range: [1e-05, 10.0], Default: 0.01, on log-scale
kernel, Type: Categorical, Choices: {linear, poly, rbf, sigmoid}, Default: linear
max_depth, Type: UniformInteger, Range: [2, 100], Default: 14, on log-scale
n_estimators, Type: UniformInteger, Range: [1, 2000], Default: 45, on log-scale
n_neighbors, Type: UniformInteger, Range: [1, 100], Default: 50
regressor, Type: Categorical, Choices: {RandomForest, Linear, AdaBoost, KNeighbors, MLP, SVR, XGBoost}, Default: RandomForest
Conditions:
(gamma | kernel == 'rbf' || gamma | kernel == 'poly' || gamma | kernel == 'sigmoid')
(n_estimators | regressor == 'RandomForest' || n_estimators | regressor == 'AdaBoost')
C | regressor == 'SVR'
alpha | regressor == 'MLP'
kernel | regressor == 'SVR'
max_depth | regressor == 'RandomForest'
n_neighbors | regressor == 'KNeighbors'
Create an Evaluator object using the ray backend to distribute the evaluation of the run-function defined previously.
[10]:
from deephyper.evaluator import Evaluator
from deephyper.evaluator.callback import TqdmCallback
evaluator = Evaluator.create(run,
method="ray",
method_kwargs={
"address": None,
"num_cpus": 1,
"num_cpus_per_task": 1,
"callbacks": [TqdmCallback()]
})
print("Number of workers: ", evaluator.num_workers)
Number of workers: 1
Finally, you can define a Bayesian optimization search called CBO (for Centralized Bayesian Optimization) and link to it the defined Problem and evaluator.
[11]:
from deephyper.search.hps import CBO
search = CBO(problem_autosklearn1, evaluator)
[12]:
results = search.search(10)
Once the search is over, a file named results.csv is saved in the current directory. The same dataframe is returned by the search.search(...) call. It contains the hyperparameters configurations evaluated during the search and their corresponding objective value (i.e, validation \(R^2\)), timestamp_submit the time when the evaluator submitted the configuration to be evaluated and timestamp_gather the time when the evaluator received the configuration once evaluated (both
are relative times with respect to the creation of the Evaluator instance).
[13]:
results
[13]:
| p:regressor | p:C | p:alpha | p:kernel | p:max_depth | p:n_estimators | p:n_neighbors | p:gamma | objective | job_id | m:timestamp_submit | m:timestamp_gather | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Linear | NaN | NaN | NaN | NaN | NaN | NaN | NaN | 0.597049 | 0 | 0.135866 | 0.154057 |
| 1 | KNeighbors | NaN | NaN | NaN | NaN | NaN | 41.0 | NaN | 0.666496 | 1 | 0.363004 | 0.664028 |
| 2 | RandomForest | NaN | NaN | NaN | 48.0 | 51.0 | NaN | NaN | 0.802510 | 2 | 0.788540 | 3.497786 |
| 3 | RandomForest | NaN | NaN | NaN | 7.0 | 245.0 | NaN | NaN | 0.719056 | 3 | 3.625416 | 10.249726 |
| 4 | SVR | 0.000063 | NaN | linear | NaN | NaN | NaN | NaN | 0.322115 | 4 | 10.450442 | 13.565911 |
| 5 | SVR | 0.000016 | NaN | sigmoid | NaN | NaN | NaN | 0.004180 | -0.059354 | 5 | 13.690964 | 17.727409 |
| 6 | SVR | 0.422234 | NaN | sigmoid | NaN | NaN | NaN | 2.779419 | -321050.500503 | 6 | 17.852672 | 24.922506 |
| 7 | RandomForest | NaN | NaN | NaN | 91.0 | 15.0 | NaN | NaN | 0.796552 | 7 | 25.120666 | 25.918885 |
| 8 | MLP | NaN | 1.350762 | NaN | NaN | NaN | NaN | NaN | 0.708333 | 8 | 26.042982 | 28.260683 |
| 9 | MLP | NaN | 0.033863 | NaN | NaN | NaN | NaN | NaN | 0.771833 | 9 | 28.383097 | 31.670986 |
Now that we have the full list of results we can display the top-3.
[14]:
results.nlargest(n=3, columns="objective")
[14]:
| p:regressor | p:C | p:alpha | p:kernel | p:max_depth | p:n_estimators | p:n_neighbors | p:gamma | objective | job_id | m:timestamp_submit | m:timestamp_gather | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | RandomForest | NaN | NaN | NaN | 48.0 | 51.0 | NaN | NaN | 0.802510 | 2 | 0.788540 | 3.497786 |
| 7 | RandomForest | NaN | NaN | NaN | 91.0 | 15.0 | NaN | NaN | 0.796552 | 7 | 25.120666 | 25.918885 |
| 9 | MLP | NaN | 0.033863 | NaN | NaN | NaN | NaN | NaN | 0.771833 | 9 | 28.383097 | 31.670986 |