deepmol.scalers package

Submodules

deepmol.scalers.base_scaler module

class BaseScaler[source]

Bases: ABC

Abstract class for all scalers. It is used to define the interface for all scalers.

fit(dataset: Dataset, columns: Optional[list] = None) → None[source]

Fits the dataset.

dataset: Dataset: The dataset to be fitted.
columns: list: The columns to be fitted.

fit_transform(dataset: Dataset, columns: Optional[list] = None) → None[source]

Fits and transforms the dataset.

dataset: Dataset: The dataset to be fitted and transformed.
columns: list: The columns to be fitted and transformed.

abstract load(file_path: str) → object[source]

Loads the scaler object from a file.

file_path: str: The path to the file where the scaler object is saved.

Returns: The scaler object.
Return type: object

partial_fit(dataset: Dataset) → None[source]

Partially fits the dataset.

dataset: Dataset: The dataset to be partially fitted.

save(file_path: str) → None[source]

Saves the scaler object to a file.

file_path: str: The path to the file where the scaler object will be saved.

abstract property scaler_object: object

Returns the scaler object.

Returns: The scaler object.
Return type: object

transform(dataset: Dataset, columns: Optional[list] = None) → None[source]

Transforms the dataset.

dataset: Dataset: The dataset to be transformed.
columns: list: The columns to be transformed.

deepmol.scalers.sklearn_scalers module

class Binarizer(threshold: float = 0.0, copy: bool = True)[source]

Bases: BaseScaler

Binarize data (set feature values to 0 or 1) according to a threshold.

load(file_path: str)[source]

Loads the scaler object from a file.

Parameters: file_path (str) – The path to the file where the scaler object is saved.

partial_fit(dataset: Dataset) → None[source]

Fits the scaler object to the dataset.

Parameters: dataset (Dataset) – The dataset to fit the scaler object.

property scaler_object

Returns the scaler object.

Returns: The scaler object.
Return type: object

class KernelCenterer[source]

Bases: BaseScaler

Center a kernel matrix.

load(file_path: str)[source]

Loads the scaler object from a file.

Parameters: file_path (str) – The path to the file where the scaler object is saved.

partial_fit(dataset: Dataset) → None[source]

Fits the scaler object to the dataset.

Parameters: dataset (Dataset) – The dataset to fit the scaler object.

property scaler_object

Returns the scaler object.

Returns: The scaler object.
Return type: object

class MaxAbsScaler(copy: bool = True)[source]

Bases: BaseScaler

Scale each feature by its maximum absolute value.

load(file_path: str)[source]

Loads the scaler object from a file.

Parameters: file_path (str) – The path to the file where the scaler object is saved.

partial_fit(dataset: Dataset) → None[source]

Fits the scaler object to the dataset.

Parameters: dataset (Dataset) – The dataset to fit the scaler object.

property scaler_object

Returns the scaler object.

Returns: The scaler object.
Return type: object

class MinMaxScaler(feature_range: Tuple[int, int] = (0, 1), copy: bool = True, clip: bool = False)[source]

Bases: BaseScaler

Transform features by scaling each feature to a given range.

load(file_path: str)[source]

Loads the scaler object from a file.

Parameters: file_path (str) – The path to the file where the scaler object is saved.

partial_fit(dataset: Dataset) → None[source]

Fits the scaler object to the dataset.

Parameters: dataset (Dataset) – The dataset to fit the scaler object.

property scaler_object

Returns the scaler object.

Returns: The scaler object.
Return type: object

class Normalizer(norm: str = 'l2', copy: bool = True)[source]

Bases: BaseScaler

Normalize samples individually to unit norm.

load(file_path: str)[source]

Loads the scaler object from a file.

Parameters: file_path (str) – The path to the file where the scaler object is saved.

partial_fit(dataset: Dataset) → None[source]

Fits the scaler object to the dataset.

Parameters: dataset (Dataset) – The dataset to fit the scaler object.

property scaler_object

Returns the scaler object.

Returns: The scaler object.
Return type: object

class PolynomialFeatures(degree: int = 2, interaction_only: bool = False, include_bias: bool = True, order: str = 'C')[source]

Bases: BaseScaler

Generate polynomial and interaction features.

load(file_path: str)[source]

Loads the scaler object from a file.

Parameters: file_path (str) – The path to the file where the scaler object is saved.

partial_fit(dataset: Dataset) → None[source]

Fits the scaler object to the dataset.

Parameters: dataset (Dataset) – The dataset to fit the scaler object.

property scaler_object

Returns the scaler object.

Returns: The scaler object.
Return type: object

class PowerTransformer(method: str = 'yeo-johnson', standardize: bool = True, copy: bool = True)[source]

Bases: BaseScaler

Apply a power transform featurewise to make data more Gaussian-like.

load(file_path: str)[source]

Loads the scaler object from a file.

Parameters: file_path (str) – The path to the file where the scaler object is saved.

partial_fit(dataset: Dataset) → None[source]

Fits the scaler object to the dataset.

Parameters: dataset (Dataset) – The dataset to fit the scaler object.

property scaler_object

Returns the scaler object.

Returns: The scaler object.
Return type: object

class QuantileTransformer(n_quantiles: int = 1000, output_distribution: str = 'uniform', ignore_implicit_zeros: bool = False, subsample: int = 100000, random_state: Optional[int] = None, copy: bool = True)[source]

Bases: BaseScaler

Transform features using quantiles information.

load(file_path: str)[source]

Loads the scaler object from a file.

Parameters: file_path (str) – The path to the file where the scaler object is saved.

partial_fit(dataset: Dataset) → None[source]

Fits the scaler object to the dataset.

Parameters: dataset (Dataset) – The dataset to fit the scaler object.

property scaler_object

Returns the scaler object.

Returns: The scaler object.
Return type: object

class RobustScaler(with_centering: bool = True, with_scaling: bool = True, quantile_range: Tuple[float, float] = (25.0, 75.0), copy: bool = True, unit_variance: bool = False)[source]

Bases: BaseScaler

Scale features using statistics that are robust to outliers.

load(file_path: str)[source]

Loads the scaler object from a file.

Parameters: file_path (str) – The path to the file where the scaler object is saved.

partial_fit(dataset: Dataset) → None[source]

Fits the scaler object to the dataset.

Parameters: dataset (Dataset) – The dataset to fit the scaler object.

property scaler_object

Returns the scaler object.

Returns: The scaler object.
Return type: object

class StandardScaler(copy: bool = True, with_mean: bool = True, with_std: bool = True)[source]

Bases: BaseScaler

Standardize features by removing the mean and scaling to unit variance.

load(file_path: str)[source]

Loads the scaler object from a file.

Parameters: file_path (str) – The path to the file where the scaler object is saved.

partial_fit(dataset: Dataset) → None[source]

Fits the scaler object to the dataset.

Parameters: dataset (Dataset) – The dataset to fit the scaler object.

property scaler_object

Returns the scaler object.

Returns: The scaler object.
Return type: object

deepmol.scalers package

Submodules

deepmol.scalers.base_scaler module

deepmol.scalers.sklearn_scalers module

Module contents