Documentation
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Overview ¶
Package scalers provides data normalization and standardization transformers.
Index ¶
- type MaxAbsScaler
- func (m *MaxAbsScaler) Fit(df *dataframe.DataFrame, _ ...string) error
- func (m *MaxAbsScaler) FitTransform(df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)
- func (m *MaxAbsScaler) GetMaxAbss() map[string]float64
- func (m *MaxAbsScaler) IsFitted() bool
- func (m *MaxAbsScaler) Transform(df *dataframe.DataFrame) (*dataframe.DataFrame, error)
- type MinMaxScaler
- func (m *MinMaxScaler) Fit(df *dataframe.DataFrame, _ ...string) error
- func (m *MinMaxScaler) FitTransform(df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)
- func (m *MinMaxScaler) GetMaxs() map[string]float64
- func (m *MinMaxScaler) GetMins() map[string]float64
- func (m *MinMaxScaler) IsFitted() bool
- func (m *MinMaxScaler) Transform(df *dataframe.DataFrame) (*dataframe.DataFrame, error)
- type RobustScaler
- func (r *RobustScaler) Fit(df *dataframe.DataFrame, _ ...string) error
- func (r *RobustScaler) FitTransform(df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)
- func (r *RobustScaler) GetIQRs() map[string]float64
- func (r *RobustScaler) GetMedians() map[string]float64
- func (r *RobustScaler) IsFitted() bool
- func (r *RobustScaler) Transform(df *dataframe.DataFrame) (*dataframe.DataFrame, error)
- type StandardScaler
- func (s *StandardScaler) Fit(df *dataframe.DataFrame, _ ...string) error
- func (s *StandardScaler) FitTransform(df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)
- func (s *StandardScaler) GetMeans() map[string]float64
- func (s *StandardScaler) GetStds() map[string]float64
- func (s *StandardScaler) IsFitted() bool
- func (s *StandardScaler) Transform(df *dataframe.DataFrame) (*dataframe.DataFrame, error)
Constants ¶
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Variables ¶
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Functions ¶
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Types ¶
type MaxAbsScaler ¶
type MaxAbsScaler struct {
// Columns to scale. If nil, all numeric columns are scaled.
Columns []string
// contains filtered or unexported fields
}
MaxAbsScaler scales each feature by its maximum absolute value. This scaler is particularly suited for sparse data as it preserves zero values. The transformation is: X_scaled = X / max(abs(X))
func NewMaxAbsScaler ¶
func NewMaxAbsScaler(columns []string) *MaxAbsScaler
NewMaxAbsScaler creates a new MaxAbsScaler.
func (*MaxAbsScaler) Fit ¶
func (m *MaxAbsScaler) Fit(df *dataframe.DataFrame, _ ...string) error
Fit computes the maximum absolute value for each column.
func (*MaxAbsScaler) FitTransform ¶
func (m *MaxAbsScaler) FitTransform(df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)
FitTransform fits the scaler and transforms the data in one step.
func (*MaxAbsScaler) GetMaxAbss ¶
func (m *MaxAbsScaler) GetMaxAbss() map[string]float64
GetMaxAbss returns the computed maximum absolute values.
func (*MaxAbsScaler) IsFitted ¶
func (m *MaxAbsScaler) IsFitted() bool
IsFitted returns true if the scaler has been fitted.
type MinMaxScaler ¶
type MinMaxScaler struct {
// Columns to scale. If nil, all numeric columns are scaled.
Columns []string
// FeatureRange defines the desired range of transformed data.
// Default: [0, 1]
FeatureMin float64
FeatureMax float64
// contains filtered or unexported fields
}
MinMaxScaler scales features to a given range (default: [0, 1]). The transformation is given by: X_scaled = (X - X_min) / (X_max - X_min) * (max - min) + min
func NewMinMaxScaler ¶
func NewMinMaxScaler(columns []string) *MinMaxScaler
NewMinMaxScaler creates a new MinMaxScaler with default range [0, 1].
func (*MinMaxScaler) Fit ¶
func (m *MinMaxScaler) Fit(df *dataframe.DataFrame, _ ...string) error
Fit computes the min and max for each column.
func (*MinMaxScaler) FitTransform ¶
func (m *MinMaxScaler) FitTransform(df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)
FitTransform fits the scaler and transforms the data in one step.
func (*MinMaxScaler) GetMaxs ¶
func (m *MinMaxScaler) GetMaxs() map[string]float64
GetMaxs returns the computed maximums.
func (*MinMaxScaler) GetMins ¶
func (m *MinMaxScaler) GetMins() map[string]float64
GetMins returns the computed minimums.
func (*MinMaxScaler) IsFitted ¶
func (m *MinMaxScaler) IsFitted() bool
IsFitted returns true if the scaler has been fitted.
type RobustScaler ¶
type RobustScaler struct {
// Columns to scale. If nil, all numeric columns are scaled.
Columns []string
// WithCentering centers the data before scaling. Default: true
WithCentering bool
// WithScaling scales the data to IQR. Default: true
WithScaling bool
// QuantileRange for IQR calculation. Default: [25.0, 75.0]
QuantileRange [2]float64
// contains filtered or unexported fields
}
RobustScaler scales features using statistics that are robust to outliers. It removes the median and scales the data according to the Interquartile Range (IQR). The transformation is: X_scaled = (X - median) / IQR
func NewRobustScaler ¶
func NewRobustScaler(columns []string) *RobustScaler
NewRobustScaler creates a new RobustScaler with default settings.
func (*RobustScaler) Fit ¶
func (r *RobustScaler) Fit(df *dataframe.DataFrame, _ ...string) error
Fit computes the median and IQR for each column.
func (*RobustScaler) FitTransform ¶
func (r *RobustScaler) FitTransform(df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)
FitTransform fits the scaler and transforms the data in one step.
func (*RobustScaler) GetIQRs ¶
func (r *RobustScaler) GetIQRs() map[string]float64
GetIQRs returns the computed IQRs.
func (*RobustScaler) GetMedians ¶
func (r *RobustScaler) GetMedians() map[string]float64
GetMedians returns the computed medians.
func (*RobustScaler) IsFitted ¶
func (r *RobustScaler) IsFitted() bool
IsFitted returns true if the scaler has been fitted.
type StandardScaler ¶
type StandardScaler struct {
// Columns to scale. If nil, all numeric columns are scaled.
Columns []string
// WithMean centers the data before scaling. Default: true
WithMean bool
// WithStd scales the data to unit variance. Default: true
WithStd bool
// contains filtered or unexported fields
}
StandardScaler standardizes features by removing the mean and scaling to unit variance. The standard score of a sample x is calculated as: z = (x - μ) / σ where μ is the mean and σ is the standard deviation.
func NewStandardScaler ¶
func NewStandardScaler(columns []string) *StandardScaler
NewStandardScaler creates a new StandardScaler with default settings.
func (*StandardScaler) Fit ¶
func (s *StandardScaler) Fit(df *dataframe.DataFrame, _ ...string) error
Fit computes the mean and standard deviation for each column.
func (*StandardScaler) FitTransform ¶
func (s *StandardScaler) FitTransform(df *dataframe.DataFrame, target ...string) (*dataframe.DataFrame, error)
FitTransform fits the scaler and transforms the data in one step.
func (*StandardScaler) GetMeans ¶
func (s *StandardScaler) GetMeans() map[string]float64
GetMeans returns the computed means.
func (*StandardScaler) GetStds ¶
func (s *StandardScaler) GetStds() map[string]float64
GetStds returns the computed standard deviations.
func (*StandardScaler) IsFitted ¶
func (s *StandardScaler) IsFitted() bool
IsFitted returns true if the scaler has been fitted.