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combination.md datasets.md deep-learning.md ensemble.md index.md metrics.md model-selection.md over-sampling.md pipeline.md under-sampling.md utilities.md

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tessl/pypi-imbalanced-learn

Toolbox for imbalanced dataset in machine learning

Workspace: tessl
Visibility: Public
Created: 3 months ago
Last updated: 3 months ago
Describes: pkg:pypi/imbalanced-learn@0.14.x

To install, run

npx @tessl/cli install tessl/pypi-imbalanced-learn@0.14.0

Imbalanced-learn

A comprehensive Python toolbox for dealing with imbalanced datasets in machine learning. Provides over-sampling, under-sampling, and combination methods that integrate seamlessly with scikit-learn's API and pipeline system, enabling fair and robust machine learning models on class-imbalanced data.

Package Information

Package Name: imbalanced-learn
Language: Python
Installation: pip install imbalanced-learn

Core Imports

import imblearn

Common imports for sampling algorithms:

from imblearn.over_sampling import SMOTE, ADASYN, RandomOverSampler
from imblearn.under_sampling import RandomUnderSampler, TomekLinks, EditedNearestNeighbours
from imblearn.combine import SMOTEENN, SMOTETomek

Pipeline integration:

from imblearn.pipeline import Pipeline, make_pipeline

Basic Usage

from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
from imblearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
import numpy as np

# Create imbalanced dataset example
X = np.random.rand(1000, 4)
y = np.random.choice([0, 1], size=1000, p=[0.9, 0.1])  # 90% class 0, 10% class 1

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Apply SMOTE oversampling
smote = SMOTE(random_state=42)
X_resampled, y_resampled = smote.fit_resample(X_train, y_train)

# Or use in pipeline
pipeline = Pipeline([
    ('sampling', SMOTE(random_state=42)),
    ('classifier', RandomForestClassifier(random_state=42))
])
pipeline.fit(X_train, y_train)
predictions = pipeline.predict(X_test)

Architecture

Imbalanced-learn follows scikit-learn's design patterns and API conventions:

Base Classes: All samplers inherit from BaseSampler and implement fit_resample() method
Sampling Strategy: Consistent sampling_strategy parameter across all algorithms
Pipeline Integration: Native support for scikit-learn's Pipeline with custom Pipeline class
Validation: Comprehensive input validation following scikit-learn patterns
Randomization: Consistent random_state parameter for reproducibility

Capabilities

Over-sampling Methods

Methods to increase minority class samples by generating synthetic examples or duplicating existing ones. Includes SMOTE variants, ADASYN, and random over-sampling approaches.

class SMOTE:
    def __init__(self, sampling_strategy='auto', random_state=None, k_neighbors=5, n_jobs=None): ...
    def fit_resample(self, X, y): ...

class ADASYN:
    def __init__(self, sampling_strategy='auto', random_state=None, n_neighbors=5, n_jobs=None): ...
    def fit_resample(self, X, y): ...

class RandomOverSampler:
    def __init__(self, sampling_strategy='auto', random_state=None, shrinkage=None): ...
    def fit_resample(self, X, y): ...

Over-sampling Methods

Under-sampling Methods

Methods to reduce majority class samples by removing redundant or noisy examples. Includes random under-sampling, prototype selection, and cleaning techniques.

class RandomUnderSampler:
    def __init__(self, sampling_strategy='auto', random_state=None, replacement=False): ...
    def fit_resample(self, X, y): ...

class TomekLinks:
    def __init__(self, sampling_strategy='auto', n_jobs=None): ...
    def fit_resample(self, X, y): ...

class EditedNearestNeighbours:
    def __init__(self, sampling_strategy='auto', n_neighbors=3, kind_sel='all', n_jobs=None): ...
    def fit_resample(self, X, y): ...

Under-sampling Methods

Combination Methods

Methods that apply both over-sampling and under-sampling sequentially to balance datasets using complementary techniques.

class SMOTEENN:
    def __init__(self, sampling_strategy='auto', random_state=None, smote=None, enn=None, n_jobs=None): ...
    def fit_resample(self, X, y): ...

class SMOTETomek:
    def __init__(self, sampling_strategy='auto', random_state=None, smote=None, tomek=None, n_jobs=None): ...
    def fit_resample(self, X, y): ...

Combination Methods

Ensemble Methods

Ensemble classifiers that incorporate sampling techniques during training to handle class imbalance effectively.

class BalancedBaggingClassifier:
    def __init__(self, base_estimator=None, n_estimators=10, max_samples=1.0, max_features=1.0, 
                 bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False,
                 sampling_strategy='auto', replacement=False, n_jobs=None, random_state=None, verbose=0): ...
    def fit(self, X, y, sample_weight=None): ...
    def predict(self, X): ...

class BalancedRandomForestClassifier:
    def __init__(self, n_estimators=100, criterion='gini', max_depth=None, min_samples_split=2,
                 min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto',
                 max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False,
                 sampling_strategy='auto', replacement=False, n_jobs=None, random_state=None, 
                 verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None): ...
    def fit(self, X, y, sample_weight=None): ...
    def predict(self, X): ...

Ensemble Methods

Metrics for Imbalanced Datasets

Specialized metrics designed to evaluate model performance on imbalanced datasets, providing more meaningful insights than traditional accuracy-based measures.

def sensitivity_score(y_true, y_pred, labels=None, pos_label=1, average='binary', 
                     sample_weight=None, zero_division='warn'): ...

def specificity_score(y_true, y_pred, labels=None, pos_label=1, average='binary',
                     sample_weight=None, zero_division='warn'): ...

def geometric_mean_score(y_true, y_pred, labels=None, pos_label=1, average='binary',
                        sample_weight=None, correction=0.0, zero_division='warn'): ...

def classification_report_imbalanced(y_true, y_pred, labels=None, target_names=None,
                                   sample_weight=None, digits=2, output_dict=False,
                                   zero_division='warn'): ...

Metrics

Pipeline Integration

Extended pipeline functionality for combining sampling and classification steps, ensuring proper cross-validation and avoiding data leakage.

class Pipeline:
    def __init__(self, steps, memory=None, verbose=False): ...
    def fit(self, X, y=None, **fit_params): ...
    def predict(self, X, **predict_params): ...
    def fit_resample(self, X, y): ...

def make_pipeline(*steps, memory=None, verbose=False): ...

Pipeline

Model Selection

Cross-validation and model selection tools adapted for imbalanced datasets, including instance hardness-based splitting strategies.

class InstanceHardnessCV:
    def __init__(self, estimator, cv=5, n_jobs=None, verbose=0, pre_dispatch='2*n_jobs',
                 scoring=None, return_train_score=False): ...
    def fit(self, X, y, groups=None, **fit_params): ...
    def split(self, X, y, groups=None): ...

Model Selection

Deep Learning Integration

Utilities for handling imbalanced datasets in deep learning frameworks, including balanced batch generators for Keras and TensorFlow.

class BalancedBatchGenerator:
    def __init__(self, X, y, sampling_strategy='auto', random_state=None, **kwargs): ...
    def __call__(self): ...

def balanced_batch_generator(X, y, sampling_strategy='auto', batch_size=32, random_state=None): ...

Deep Learning

Utilities and Validation

Helper functions for validating sampling strategies, checking neighbor objects, and other utility operations.

def check_sampling_strategy(sampling_strategy, y, sampling_type, **kwargs): ...

def check_neighbors_object(nn_name, nn_object, additional_neighbor=0): ...

def check_target_type(y, indicate_one_vs_all=False): ...

class FunctionSampler:
    def __init__(self, func=None, accept_sparse=True, kw_args=None, validate=True): ...
    def fit(self, X, y): ...
    def fit_resample(self, X, y): ...

Utilities

Datasets

Functions for creating imbalanced datasets and fetching benchmark datasets for testing and evaluation.

def make_imbalance(X, y, sampling_strategy=None, random_state=None, verbose=False, **kwargs): ...

def fetch_datasets(data_home=None, filter_data=None, download_if_missing=True, 
                  return_X_y=False, as_frame=False): ...

Datasets

Base Classes and Types

Core base classes and type definitions used throughout the package:

class BaseSampler:
    def __init__(self): ...
    def fit(self, X, y): ...
    def fit_resample(self, X, y): ...
    def _validate_params(self): ...

class SamplerMixin:
    def fit_resample(self, X, y): ...

def is_sampler(estimator): ...

Version

Tile

Files

tessl/pypi-imbalanced-learn

To install, run

index.md.css-3qkkll{font-size:var(--chakra-font-sizes-sm);font-weight:var(--chakra-font-weights-normal);color:var(--chakra-colors-gray-300);}docs/

Imbalanced-learn

Package Information

Core Imports

Basic Usage

Architecture

Capabilities

Over-sampling Methods

Under-sampling Methods

Combination Methods

Ensemble Methods

Metrics for Imbalanced Datasets

Pipeline Integration

Model Selection

Deep Learning Integration

Utilities and Validation

Datasets

Base Classes and Types

index.mddocs/