We are glad to announce the alpha release for Feature Engineering toolkit on top of NNI, it's still in the experiment phase which might evolve based on user feedback. We'd like to invite you to use, feedback and even contribute.
For now, we support the following feature selector:
These selectors are suitable for tabular data(which means it doesn't include image, speech and text data).
In addition, those selector only for feature selection. If you want to:
- generate high-order combined features on nni while doing feature selection;
- leverage your distributed resources; you could try this example.
from nni.feature_engineering.gradient_selector import FeatureGradientSelector
# from nni.feature_engineering.gbdt_selector import GBDTSelector
# load data
...
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
# initlize a selector
fgs = FeatureGradientSelector(...)
# fit data
fgs.fit(X_train, y_train)
# get improtant features
# will return the index with important feature here.
print(fgs.get_selected_features(...))
...When using the built-in Selector, you first need to import a feature selector, and initialize it. You could call the function fit in the selector to pass the data to the selector. After that, you could use get_seleteced_features to get important features. The function parameters in different selectors might be different, so you need to check the docs before using it.
NNI provides state-of-the-art feature selector algorithm in the builtin-selector. NNI also supports to build a feature selector by yourself.
If you want to implement a customized feature selector, you need to:
- Inherit the base FeatureSelector class
- Implement fit and get_selected_features function
- Integrate with sklearn (Optional)
Here is an example:
1. Inherit the base Featureselector Class
from nni.feature_engineering.feature_selector import FeatureSelector
class CustomizedSelector(FeatureSelector):
def __init__(self, ...):
...2. Implement fit and get_selected_features Function
from nni.tuner import Tuner
from nni.feature_engineering.feature_selector import FeatureSelector
class CustomizedSelector(FeatureSelector):
def __init__(self, ...):
...
def fit(self, X, y, **kwargs):
"""
Fit the training data to FeatureSelector
Parameters
------------
X : array-like numpy matrix
The training input samples, which shape is [n_samples, n_features].
y: array-like numpy matrix
The target values (class labels in classification, real numbers in regression). Which shape is [n_samples].
"""
self.X = X
self.y = y
...
def get_selected_features(self):
"""
Get important feature
Returns
-------
list :
Return the index of the important feature.
"""
...
return self.selected_features_
...3. Integrate with Sklearn
sklearn.pipeline.Pipeline can connect models in series, such as feature selector, normalization, and classification/regression to form a typical machine learning problem workflow.
The following step could help us to better integrate with sklearn, which means we could treat the customized feature selector as a mudule of the pipeline.
- Inherit the calss sklearn.base.BaseEstimator
- Implement get_params and set_params function in BaseEstimator
- Inherit the class sklearn.feature_selection.base.SelectorMixin
- Implement get_support, transform and inverse_transform Function in SelectorMixin
Here is an example:
1. Inherit the BaseEstimator Class and its Function
from sklearn.base import BaseEstimator
from nni.feature_engineering.feature_selector import FeatureSelector
class CustomizedSelector(FeatureSelector, BaseEstimator):
def __init__(self, ...):
...
def get_params(self, ...):
"""
Get parameters for this estimator.
"""
params = self.__dict__
params = {key: val for (key, val) in params.items()
if not key.endswith('_')}
return params
def set_params(self, **params):
"""
Set the parameters of this estimator.
"""
for param in params:
if hasattr(self, param):
setattr(self, param, params[param])
return self2. Inherit the SelectorMixin Class and its Function
from sklearn.base import BaseEstimator
from sklearn.feature_selection.base import SelectorMixin
from nni.feature_engineering.feature_selector import FeatureSelector
class CustomizedSelector(FeatureSelector, BaseEstimator, SelectorMixin):
def __init__(self, ...):
...
def get_params(self, ...):
"""
Get parameters for this estimator.
"""
params = self.__dict__
params = {key: val for (key, val) in params.items()
if not key.endswith('_')}
return params
def set_params(self, **params):
"""
Set the parameters of this estimator.
"""
for param in params:
if hasattr(self, param):
setattr(self, param, params[param])
return self
def get_support(self, indices=False):
"""
Get a mask, or integer index, of the features selected.
Parameters
----------
indices : bool
Default False. If True, the return value will be an array of integers, rather than a boolean mask.
Returns
-------
list :
returns support: An index that selects the retained features from a feature vector.
If indices are False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention.
If indices are True, this is an integer array of shape [# output features] whose values
are indices into the input feature vector.
"""
...
return mask
def transform(self, X):
"""Reduce X to the selected features.
Parameters
----------
X : array
which shape is [n_samples, n_features]
Returns
-------
X_r : array
which shape is [n_samples, n_selected_features]
The input samples with only the selected features.
"""
...
return X_r
def inverse_transform(self, X):
"""
Reverse the transformation operation
Parameters
----------
X : array
shape is [n_samples, n_selected_features]
Returns
-------
X_r : array
shape is [n_samples, n_original_features]
"""
...
return X_rAfter integrating with Sklearn, we could use the feature selector as follows:
from sklearn.linear_model import LogisticRegression
# load data
...
X_train, y_train = ...
# build a ppipeline
pipeline = make_pipeline(XXXSelector(...), LogisticRegression())
pipeline = make_pipeline(SelectFromModel(ExtraTreesClassifier(n_estimators=50)), LogisticRegression())
pipeline.fit(X_train, y_train)
# score
print("Pipeline Score: ", pipeline.score(X_train, y_train))Baseline means without any feature selection, we directly pass the data to LogisticRegression. For this benchmark, we only use 10% data from the train as test data. For the GradientFeatureSelector, we only take the top20 features. The metric is the mean accuracy on the given test data and labels.
| Dataset | All Features + LR (acc, time, memory) | GradientFeatureSelector + LR (acc, time, memory) | TreeBasedClassifier + LR (acc, time, memory) | #Train | #Feature |
|---|---|---|---|---|---|
| colon-cancer | 0.7547, 890ms, 348MiB | 0.7368, 363ms, 286MiB | 0.7223, 171ms, 1171 MiB | 62 | 2,000 |
| gisette | 0.9725, 215ms, 584MiB | 0.89416, 446ms, 397MiB | 0.9792, 911ms, 234MiB | 6,000 | 5,000 |
| avazu | 0.8834, N/A, N/A | N/A, N/A, N/A | N/A, N/A, N/A | 40,428,967 | 1,000,000 |
| rcv1 | 0.9644, 557ms, 241MiB | 0.7333, 401ms, 281MiB | 0.9615, 752ms, 284MiB | 20,242 | 47,236 |
| news20.binary | 0.9208, 707ms, 361MiB | 0.6870, 565ms, 371MiB | 0.9070, 904ms, 364MiB | 19,996 | 1,355,191 |
| real-sim | 0.9681, 433ms, 274MiB | 0.7969, 251ms, 274MiB | 0.9591, 643ms, 367MiB | 72,309 | 20,958 |
The dataset of benchmark could be download in here
The code could be refenrence /examples/feature_engineering/gradient_feature_selector/benchmark_test.py.
- To report a bug for this feature in GitHub;
- To file a feature or improvement request for this feature in GitHub;
- To know more about Neural Architecture Search with NNI;
- To know more about Model Compression with NNI;
- To know more about Hyperparameter Tuning with NNI;