FastProp

class getml.feature_learning.FastProp(aggregation: typing.List[str] = <factory>, delta_t: float = 0.0, loss_function: typing.Optional[typing.Literal['CrossEntropyLoss', 'SquareLoss']] = None, max_lag: int = 0, min_df: int = 30, n_most_frequent: int = 0, num_features: int = 200, num_threads: int = 0, sampling_factor: float = 1.0, silent: bool = True, vocab_size: int = 500)[source]

Generates simple features based on propositionalization.

FastProp generates simple and easily interpretable features for relational data and time series. It is based on a propositionalization approach and has been optimized for speed and memory efficiency. FastProp generates a large number of features and selects the most relevant ones based on the pair-wise correlation with the target(s).

It is recommended to combine FastProp with the Mapping and Seasonal preprocessors, which can drastically improve predictive accuracy.

Args:
aggregation (List[aggregations], optional):

Mathematical operations used by the automated feature learning algorithm to create new features.

Must be from aggregations.

delta_t (float, optional):

Frequency with which lag variables will be explored in a time series setting. When set to 0.0, there will be no lag variables. Please note that you must also pass a value to max_lag.

For more information please refer to Time series. Range: [0, \(\infty\)]

loss_function (loss_functions, optional):

Objective function used by the feature learning algorithm to optimize your features. For regression problems use SquareLoss and for classification problems use CrossEntropyLoss.

max_lag (int, optional):

Maximum number of steps taken into the past to form lag variables. The step size is determined by delta_t. Please note that you must also pass a value to delta_t.

For more information please refer to Time series. Range: [0, \(\infty\)]

min_df (int, optional):

Only relevant for columns with role text. The minimum number of fields (i.e. rows) in text column a given word is required to appear in to be included in the bag of words. Range: [1, \(\infty\)]

num_features (int, optional):

Number of features generated by the feature learning algorithm. Range: [1, \(\infty\)]

n_most_frequent (int, optional):

FastProp can find the N most frequent categories in a categorical column and derive features from them. The parameter determines how many categories should be used. Range: [0, \(\infty\)]

num_threads (int, optional):

Number of threads used by the feature learning algorithm. If set to zero or a negative value, the number of threads will be determined automatically by the getML engine. Range: [\(0\), \(\infty\)]

sampling_factor (float, optional):

FastProp uses a bootstrapping procedure (sampling with replacement) to train each of the features. The sampling factor is proportional to the share of the samples randomly drawn from the population table every time Multirel generates a new feature. A lower sampling factor (but still greater than 0.0), will lead to less danger of overfitting, less complex statements and faster training. When set to 1.0, roughly 2,000 samples are drawn from the population table. If the population table contains less than 2,000 samples, it will use standard bagging. When set to 0.0, there will be no sampling at all. Range: [0, \(\infty\)]

silent (bool, optional):

Controls the logging during training.

vocab_size (int, optional):

Determines the maximum number of words that are extracted in total from getml.data.roles.text columns. This can be interpreted as the maximum size of the bag of words. Range: [0, \(\infty\)]

Methods

validate([params])

Checks both the types and the values of all instance variables and raises an exception if something is off.

Attributes

agg_sets

aggregation

delta_t

loss_function

max_lag

min_df

n_most_frequent

num_features

num_threads

sampling_factor

silent

type

vocab_size