Linear
Some words about linear functions.
Available optimizations
There are currently no optimizations available for linear functions. Check out Extending fastinference on how to add an optimization!
The Linear object
- class fastinference.models.Linear.Linear(classes, n_features, accuracy=None, name='Model')
A placeholder for all linear models. There is nothing fancy going on here. This class stores the coefficients of the linear function in
self.coeff
and the bias/intercept inself.intercept
.- __init__(classes, n_features, accuracy=None, name='Model')
Constructor of a linear model.
- Parameters
classes (int) – The class mappings. Each enty maps the given entry to the corresponding index so that the i-th output of the model belongs to class classes[i]. For example with classes = [1,0,2] the second output of the model maps to class 0, the first output to class 1 and the third output to class 2. n_features (list of int): The number of features this model was trained on.
model_accuracy (float, optional) – The accuracy of this tree on some test data. Can be used to verify the correctness of the implementation. Defaults to None.
name (str, optional) – The name of this model. Defaults to “Model”.
- classmethod from_dict(data)
Generates a new linear model from the given dictionary. It is assumed that a linear model has previously been stored with the
Linear.to_dict()
method.- Parameters
data (dict) – The dictionary from which this linear model should be generated.
- Returns
The newly generated linear model.
- Return type
- classmethod from_sklearn(sk_model, name='model', accuracy=None)
Generates a new linear model from sklearn.
- Parameters
sk_model (LinearModel) – A LinearModel trained in sklearn (e.g. SGDClassifier, RidgeClassifier, Perceptron, etc.).
name (str, optional) – The name of this model. Defaults to “Model”.
accuracy (float, optional) – The accuracy of this tree on some test data. Can be used to verify the correctness of the implementation. Defaults to None.
- Returns
The newly generated linear model.
- Return type
- predict_proba(X)
Applies this linear model to the given data and provides the predicted probabilities for each example in X.
- Parameters
X (numpy.array) – A (N,d) matrix where N is the number of data points and d is the feature dimension. If X has only one dimension then a single example is assumed and X is reshaped via
X = X.reshape(1,X.shape[0])
- Returns
A (N, c) prediction matrix where N is the number of data points and c is the number of classes
- Return type
numpy.array
- to_dict()
Stores this linear model as a dictionary which can be loaded with
Linear.from_dict()
.- Returns
The dictionary representation of this linear model.
- Return type
dict