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River

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online-ml/river

Introduction 🍼
Recipes 🌮
API reference 🍱
Examples 🌶️
FAQ
Releases
Benchmarks

River

online-ml/river

Introduction 🍼
Introduction 🍼
- Installation
- Basic concepts
- Getting started
  Getting started
- Why use River?
- Next steps
- Related projects
Recipes 🌮
Recipes 🌮
API reference 🍱
API reference 🍱
- Overview
- active
  active
  - EntropySampler
  - base
    base
    
    ActiveLearningClassifier
- anomaly
  anomaly
  - GaussianScorer
  - HalfSpaceTrees
  - OneClassSVM
  - QuantileFilter
  - ThresholdFilter
  - base
    base
    
    AnomalyDetector
    
    AnomalyFilter
    
    SupervisedAnomalyDetector
- bandit
  bandit
  - EpsilonGreedy
  - Exp3
  - ThompsonSampling
  - UCB
  - evaluate
  - base
    base
    
    Policy
  - envs
    envs
    
    CandyCaneContest
    
    KArmedTestbed
- base
  base
- cluster
  cluster
  - CluStream
  - DBSTREAM
  - DenStream
  - KMeans
  - STREAMKMeans
  - TextClust
- compat
  compat
- compose
  compose
- conf
  conf
  - Interval
  - RegressionJackknife
- covariance
  covariance
  - EmpiricalCovariance
  - EmpiricalPrecision
- datasets
  datasets
  - AirlinePassengers
  - Bananas
  - Bikes
  - ChickWeights
  - CreditCard
  - Elec2
  - HTTP
  - Higgs
  - ImageSegments
  - Insects
  - Keystroke
  - MaliciousURL
  - MovieLens100K
  - Music
  - Phishing
  - Restaurants
  - SMSSpam
  - SMTP
  - SolarFlare
  - TREC07
  - Taxis
  - TrumpApproval
  - WaterFlow
  - base
    base
    
    Dataset
    
    FileDataset
    
    RemoteDataset
    
    SyntheticDataset
  - synth
    synth
    
    Agrawal
    
    AnomalySine
    
    ConceptDriftStream
    
    Friedman
    
    FriedmanDrift
    
    Hyperplane
    
    LED
    
    LEDDrift
    
    Logical
    
    Mixed
    
    Mv
    
    Planes2D
    
    RandomRBF
    
    RandomRBFDrift
    
    RandomTree
    
    SEA
    
    STAGGER
    
    Sine
    
    Waveform
- drift
  drift
  - ADWIN
  - DriftRetrainingClassifier
  - DummyDriftDetector
  - KSWIN
  - PageHinkley
  - binary
    binary
    
    DDM
    
    EDDM
    
    HDDM_A
    
    HDDM_W
- dummy
  dummy
- ensemble
  ensemble
- evaluate
  evaluate
- facto
  facto
- feature_extraction
  feature_extraction
  - Agg
  - BagOfWords
  - PolynomialExtender
  - RBFSampler
  - TFIDF
  - TargetAgg
- feature_selection
  feature_selection
- forest
  forest
- imblearn
  imblearn
- linear_model
  linear_model
  - ALMAClassifier
  - BayesianLinearRegression
  - LinearRegression
  - LogisticRegression
  - PAClassifier
  - PARegressor
  - Perceptron
  - SoftmaxRegression
  - base
    base
    
    GLM
- metrics
  metrics
  - Accuracy
  - AdjustedMutualInfo
  - AdjustedRand
  - BalancedAccuracy
  - ClassificationReport
  - CohenKappa
  - Completeness
  - ConfusionMatrix
  - CrossEntropy
  - F1
  - FBeta
  - FowlkesMallows
  - GeometricMean
  - Homogeneity
  - Jaccard
  - LogLoss
  - MAE
  - MAPE
  - MCC
  - MSE
  - MacroF1
  - MacroFBeta
  - MacroJaccard
  - MacroPrecision
  - MacroRecall
  - MicroF1
  - MicroFBeta
  - MicroJaccard
  - MicroPrecision
  - MicroRecall
  - MultiFBeta
  - MutualInfo
  - NormalizedMutualInfo
  - Precision
  - R2
  - RMSE
  - RMSLE
  - ROCAUC
  - Rand
  - Recall
  - RollingROCAUC
  - SMAPE
  - Silhouette
  - VBeta
  - WeightedF1
  - WeightedFBeta
  - WeightedJaccard
  - WeightedPrecision
  - WeightedRecall
  - base
    base
    
    BinaryMetric
    
    ClassificationMetric
    
    Metric
    
    Metrics
    
    MultiClassMetric
    
    RegressionMetric
    
    WrapperMetric
  - multioutput
    multioutput
    
    ExactMatch
    
    MacroAverage
    
    MicroAverage
    
    MultiLabelConfusionMatrix
    
    PerOutput
    
    base
    base
    
    MultiOutputClassificationMetric
    
    MultiOutputRegressionMetric
- misc
  misc
  - SDFT
  - Skyline
- model_selection
  model_selection
- multiclass
  multiclass
- multioutput
  multioutput
- naive_bayes
  naive_bayes
- neighbors
  neighbors
- neural_net
  neural_net
  - MLPRegressor
  - activations
    activations
    
    Identity
    
    ReLU
    
    Sigmoid
- optim
  optim
  - AMSGrad
  - AdaBound
  - AdaDelta
  - AdaGrad
  - AdaMax
  - Adam
  - Averager
  - FTRLProximal
  - Momentum
  - Nadam
  - NesterovMomentum
  - RMSProp
  - SGD
  - base
    base
    
    Initializer
    
    Loss
    
    Optimizer
    
    Scheduler
  - initializers
    initializers
    
    Constant
    
    Normal
    
    Zeros
  - losses
    losses
    
    Absolute
    
    BinaryFocalLoss
    
    BinaryLoss
    
    Cauchy
    
    CrossEntropy
    
    EpsilonInsensitiveHinge
    
    Hinge
    
    Huber
    
    Log
    
    MultiClassLoss
    
    Poisson
    
    Quantile
    
    RegressionLoss
    
    Squared
  - schedulers
    schedulers
    
    Constant
    
    InverseScaling
    
    Optimal
- preprocessing
  preprocessing
- proba
  proba
  - Beta
  - Gaussian
  - Multinomial
  - base
    base
    
    BinaryDistribution
    
    ContinuousDistribution
    
    DiscreteDistribution
    
    Distribution
- reco
  reco
  - Baseline
  - BiasedMF
  - FunkMF
  - RandomNormal
  - base
    base
    
    Ranker
- rules
  rules
  - AMRules
- sketch
  sketch
  - Counter
  - HeavyHitters
  - Histogram
  - Set
- stats
  stats
  - AbsMax
  - AutoCorr
  - BayesianMean
  - Count
  - Cov
  - EWMean
  - EWVar
  - Entropy
  - IQR
  - Kurtosis
  - Link
  - MAD
  - Max
  - Mean
  - Min
  - Mode
  - NUnique
  - PeakToPeak
  - PearsonCorr
  - Quantile
  - RollingAbsMax
  - RollingIQR
  - RollingMax
  - RollingMin
  - RollingMode
  - RollingPeakToPeak
  - RollingQuantile
  - SEM
  - Shift
  - Skew
  - Sum
  - Var
  - base
    base
    
    Bivariate
    
    Univariate
- stream
  stream
- time_series
  time_series
  - ForecastingMetric
  - HoltWinters
  - HorizonAggMetric
  - HorizonMetric
  - SNARIMAX
  - evaluate
  - iter_evaluate
  - base
    base
    
    Forecaster
- tree
  tree
  - ExtremelyFastDecisionTreeClassifier
  - HoeffdingAdaptiveTreeClassifier
  - HoeffdingAdaptiveTreeRegressor
  - HoeffdingTreeClassifier
  - HoeffdingTreeRegressor
  - SGTClassifier
  - SGTRegressor
  - iSOUPTreeRegressor
  - base
    base
    
    Branch
    
    Leaf
  - splitter
    splitter
    
    DynamicQuantizer
    
    EBSTSplitter
    
    ExhaustiveSplitter
    
    GaussianSplitter
    
    HistogramSplitter
    
    QOSplitter
    
    Quantizer
    
    Splitter
    
    StaticQuantizer
    
    TEBSTSplitter
- utils
  utils
  - Rolling
  - SortedWindow
  - TimeRolling
  - VectorDict
  - dict2numpy
  - expand_param_grid
  - log_method_calls
  - numpy2dict
  - math
    math
    
    argmax
    
    chain_dot
    
    clamp
    
    dot
    
    dotvecmat
    
    log_sum_2_exp
    
    matmul2d
    
    minkowski_distance
    
    norm norm
    Table of contents
    
    Parameters
    
    outer
    
    prod
    
    sherman_morrison
    
    sigmoid
    
    sign
    
    softmax
    
    woodbury_matrix
  - norm
    norm
    
    normalize_values_in_dict
    
    scale_values_in_dict
  - pretty
    pretty
    
    humanize_bytes
    
    print_table
  - random
    random
    
    poisson
Examples 🌶️
Examples 🌶️
- From batch to online/stream
- Bike-sharing forecasting
- Building a simple nowcasting model
- Content personalization
- Debugging a pipeline
- Working with imbalanced data
- Handling uncertainty with quantile regression
- Sentence classification
- The art of using pipelines
- Matrix factorization for recommender systems
  Matrix factorization for recommender systems
  - Part 1
  - Part 2
  - Part 3
FAQ
FAQ
Releases
Releases
Benchmarks
Benchmarks
- Binary classification
  Binary classification
- Multiclass classification
  Multiclass classification
- Regression
  Regression

Table of contents

Parameters

norm¶

Compute the norm of a dictionaries values.

Parameters¶

x

Type → dict
order

Default → None

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