# GradientFeatureSelector¶

The algorithm in GradientFeatureSelector comes from Feature Gradients: Scalable Feature Selection via Discrete Relaxation.

GradientFeatureSelector, a gradient-based search algorithm for feature selection.

1) This approach extends a recent result on the estimation of
learnability in the sublinear data regime by showing that the calculation can be performed iteratively (i.e., in mini-batches) and in **linear time and space** with respect to both the number of features D and the sample size N.

This, along with a discrete-to-continuous relaxation of the search domain, allows for an

**efficient, gradient-based**search algorithm among feature subsets for very**large datasets**.Crucially, this algorithm is capable of finding

**higher-order correlations**between features and targets for both the N > D and N < D regimes, as opposed to approaches that do not consider such interactions and/or only consider one regime.

## Usage¶

```
from nni.algorithms.feature_engineering.gradient_selector import FeatureGradientSelector
# 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(n_features=10)
# fit data
fgs.fit(X_train, y_train)
# get improtant features
# will return the index with important feature here.
print(fgs.get_selected_features())
...
```

And you could reference the examples in `/examples/feature_engineering/gradient_feature_selector/`

, too.

**Parameters of class FeatureGradientSelector constructor**

**order**(int, optional, default = 4) - What order of interactions to include. Higher orders may be more accurate but increase the run time. 12 is the maximum allowed order.**penalty**(int, optional, default = 1) - Constant that multiplies the regularization term.**n_features**(int, optional, default = None) - If None, will automatically choose number of features based on search. Otherwise, the number of top features to select.**max_features**(int, optional, default = None) - If not None, will use the 'elbow method' to determine the number of features with max_features as the upper limit.**learning_rate**(float, optional, default = 1e-1) - learning rate**init**(*zero, on, off, onhigh, offhigh, or sklearn, optional, default = zero*) - How to initialize the vector of scores. 'zero' is the default.**n_epochs**(int, optional, default = 1) - number of epochs to run**shuffle**(bool, optional, default = True) - Shuffle "rows" prior to an epoch.**batch_size**(int, optional, default = 1000) - Nnumber of "rows" to process at a time.**target_batch_size**(int, optional, default = 1000) - Number of "rows" to accumulate gradients over. Useful when many rows will not fit into memory but are needed for accurate estimation.**classification**(bool, optional, default = True) - If True, problem is classification, else regression.**ordinal**(bool, optional, default = True) - If True, problem is ordinal classification. Requires classification to be True.**balanced**(bool, optional, default = True) - If true, each class is weighted equally in optimization, otherwise weighted is done via support of each class. Requires classification to be True.**prerocess**(str, optional, default = 'zscore') - 'zscore' which refers to centering and normalizing data to unit variance or 'center' which only centers the data to 0 mean.**soft_grouping**(bool, optional, default = True) - If True, groups represent features that come from the same source. Used to encourage sparsity of groups and features within groups.**verbose**(int, optional, default = 0) - Controls the verbosity when fitting. Set to 0 for no printing 1 or higher for printing every verbose number of gradient steps.**device**(str, optional, default = 'cpu') - 'cpu' to run on CPU and 'cuda' to run on GPU. Runs much faster on GPU

**Requirement of fit FuncArgs**

**X**(array-like, require) - The training input samples which shape = [n_samples, n_features]. np.ndarry recommended.**y**(array-like, require) - The target values (class labels in classification, real numbers in regression) which shape = [n_samples]. np.ndarry recommended.**groups**(array-like, optional, default = None) - Groups of columns that must be selected as a unit. e.g. [0, 0, 1, 2] specifies the first two columns are part of a group. Which shape is [n_features].

**Requirement of get_selected_features FuncArgs**

For now, the

`get_selected_features`

function has no parameters.