vowpalwabbit.sklearn

This is an optional module which implements sklearn compatability.

Deprecated alias

Deprecated since version 9.0.0: The module name vowpalwabbit.sklearn_vw has been renamed to vowpalwabbit.sklearn. Please use the new module name instead.

Example usage

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from vowpalwabbit.sklearn import VWClassifier
    # generate some data
X, y = datasets.make_hastie_10_2(n_samples=10000, random_state=1)
X = X.astype(np.float32)
    # split train and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=256)
    # build model
model = VWClassifier()
model.fit(X_train, y_train)
    # predict model
y_pred = model.predict(X_test)
    # evaluate model
model.score(X_train, y_train)
model.score(X_test, y_test)

Module contents

Utilities to support integration of Vowpal Wabbit and scikit-learn

class vowpalwabbit.sklearn.LinearClassifierMixin

Bases: LogisticRegression

__init__()
set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LinearClassifierMixin

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → LinearClassifierMixin

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

class vowpalwabbit.sklearn.VW(convert_to_vw=True, convert_labels=True, ring_size=None, strict_parse=None, learning_rate=None, l=None, power_t=None, decay_learning_rate=None, initial_t=None, feature_mask=None, initial_regressor=None, i=None, initial_weight=None, random_weights=None, normal_weights=None, truncated_normal_weights=None, sparse_weights=None, input_feature_regularizer=None, quiet=True, random_seed=None, hash=None, hash_seed=None, ignore=None, ignore_linear=None, keep=None, redefine=None, bit_precision=None, b=None, noconstant=None, constant=None, C=None, ngram=None, skips=None, feature_limit=None, affix=None, spelling=None, dictionary=None, dictionary_path=None, interactions=None, permutations=None, leave_duplicate_interactions=None, quadratic=None, q=None, cubic=None, testonly=None, t=None, holdout_off=None, holdout_period=None, holdout_after=None, early_terminate=None, passes=1, initial_pass_length=None, examples=None, min_prediction=None, max_prediction=None, sort_features=None, loss_function=None, quantile_tau=None, l1=None, l2=None, no_bias_regularization=None, named_labels=None, final_regressor=None, f=None, readable_model=None, invert_hash=None, save_resume=None, preserve_performance_counters=None, output_feature_regularizer_binary=None, output_feature_regularizer_text=None, oaa=None, ect=None, csoaa=None, wap=None, probabilities=None, nn=None, inpass=None, multitask=None, dropout=None, meanfield=None, conjugate_gradient=None, bfgs=None, hessian_on=None, mem=None, termination=None, lda=None, lda_alpha=None, lda_rho=None, lda_D=None, lda_epsilon=None, minibatch=None, svrg=None, stage_size=None, ftrl=None, coin=None, pistol=None, ftrl_alpha=None, ftrl_beta=None, ksvm=None, kernel=None, bandwidth=None, degree=None, sgd=None, adaptive=None, invariant=None, normalized=None, link=None, stage_poly=None, sched_exponent=None, batch_sz=None, batch_sz_no_doubling=None, lrq=None, lrqdropout=None, lrqfa=None, data=None, d=None, cache=None, c=None, cache_file=None, json=None, kill_cache=None, k=None)

Bases: BaseEstimator

Vowpal Wabbit Scikit-learn Base Estimator wrapper

__init__(convert_to_vw=True, convert_labels=True, ring_size=None, strict_parse=None, learning_rate=None, l=None, power_t=None, decay_learning_rate=None, initial_t=None, feature_mask=None, initial_regressor=None, i=None, initial_weight=None, random_weights=None, normal_weights=None, truncated_normal_weights=None, sparse_weights=None, input_feature_regularizer=None, quiet=True, random_seed=None, hash=None, hash_seed=None, ignore=None, ignore_linear=None, keep=None, redefine=None, bit_precision=None, b=None, noconstant=None, constant=None, C=None, ngram=None, skips=None, feature_limit=None, affix=None, spelling=None, dictionary=None, dictionary_path=None, interactions=None, permutations=None, leave_duplicate_interactions=None, quadratic=None, q=None, cubic=None, testonly=None, t=None, holdout_off=None, holdout_period=None, holdout_after=None, early_terminate=None, passes=1, initial_pass_length=None, examples=None, min_prediction=None, max_prediction=None, sort_features=None, loss_function=None, quantile_tau=None, l1=None, l2=None, no_bias_regularization=None, named_labels=None, final_regressor=None, f=None, readable_model=None, invert_hash=None, save_resume=None, preserve_performance_counters=None, output_feature_regularizer_binary=None, output_feature_regularizer_text=None, oaa=None, ect=None, csoaa=None, wap=None, probabilities=None, nn=None, inpass=None, multitask=None, dropout=None, meanfield=None, conjugate_gradient=None, bfgs=None, hessian_on=None, mem=None, termination=None, lda=None, lda_alpha=None, lda_rho=None, lda_D=None, lda_epsilon=None, minibatch=None, svrg=None, stage_size=None, ftrl=None, coin=None, pistol=None, ftrl_alpha=None, ftrl_beta=None, ksvm=None, kernel=None, bandwidth=None, degree=None, sgd=None, adaptive=None, invariant=None, normalized=None, link=None, stage_poly=None, sched_exponent=None, batch_sz=None, batch_sz_no_doubling=None, lrq=None, lrqdropout=None, lrqfa=None, data=None, d=None, cache=None, c=None, cache_file=None, json=None, kill_cache=None, k=None)

VW model constructor, exposing all supported parameters to keep sklearn happy

Parameters:

  • convert_to_vw (bool) – flag to convert X input to vw format

  • convert_labels (bool) – Convert labels of the form [0,1] to [-1,1]

  • ring_size (int) – size of example ring

  • strict_parse (bool) – throw on malformed examples

  • learning_rate (float) – Set learning rate

  • l (float) – Set learning rate

  • power_t (float) – t power value

  • decay_learning_rate (float) – Set Decay factor for learning_rate between passes

  • initial_t (float) – initial t value

  • feature_mask (str) – Use existing regressor to determine which parameters may be updated. If no initial_regressor given, also used for initial weights.

  • initial_regressor (str) – Initial regressor(s)

  • i (str) – Initial regressor(s)

  • initial_weight (float) – Set all weights to an initial value of arg.

  • random_weights (bool) – make initial weights random

  • normal_weights (bool) – make initial weights normal

  • truncated_normal_weights (bool) – make initial weights truncated normal

  • sparse_weights (float) – Use a sparse datastructure for weights

  • input_feature_regularizer (str) – Per feature regularization input file

  • quiet (bool) – Don’t output disgnostics and progress updates

  • random_seed (integer) – seed random number generator

  • hash (str) – , all

  • hash_seed (int) – seed for hash function

  • ignore (str) – ignore namespaces beginning with character <arg>

  • ignore_linear (str) – ignore namespaces beginning with character <arg> for linear terms only

  • keep (str) – keep namespaces beginning with character <arg>

  • redefine (str) – Redefine namespaces beginning with characters of string S as namespace N. <arg> shall be in form ‘N:=S’ where := is operator. Empty N or S are treated as default namespace. Use ‘:’ as a wildcard in S.

  • bit_precision (integer) – number of bits in the feature table

  • b (integer) – number of bits in the feature table

  • noconstant (bool) – Don’t add a constant feature

  • constant (float) – Set initial value of constant

  • C (float) – Set initial value of constant

  • ngram (str) – Generate N grams. To generate N grams for a single namespace ‘foo’, arg should be fN.

  • skips (str) – Generate skips in N grams. This in conjunction with the ngram tag can be used to generate generalized n-skip-k-gram. To generate n-skips for a single namespace ‘foo’, arg should be fN.

  • feature_limit (str) – limit to N features. To apply to a single namespace ‘foo’, arg should be fN

  • affix (str) – generate prefixes/suffixes of features; argument ‘+2a,-3b,+1’ means generate 2-char prefixes for namespace a, 3-char suffixes for b and 1 char prefixes for default namespace

  • spelling (str) – compute spelling features for a give namespace (use ‘_’ for default namespace)

  • dictionary (str) – read a dictionary for additional features (arg either ‘x:file’ or just ‘file’)

  • dictionary_path (str) – look in this directory for dictionaries; defaults to current directory or env{PATH}

  • interactions (str) – Create feature interactions of any level between namespaces.

  • permutations (bool) – Use permutations instead of combinations for feature interactions of same namespace.

  • leave_duplicate_interactions (bool) – Don’t remove interactions with duplicate combinations of namespaces. For ex. this is a duplicate: ‘-q ab -q ba’ and a lot more in ‘-q ::’.

  • quadratic (str) – Create and use quadratic features, q:: corresponds to a wildcard for all printable characters

  • q (str) – Create and use quadratic features, q:: corresponds to a wildcard for all printable characters

  • cubic (str) – Create and use cubic features

  • testonly (bool) – Ignore label information and just test

  • t (bool) – Ignore label information and just test

  • holdout_off (bool) – no holdout data in multiple passes

  • holdout_period (int) – holdout period for test only

  • holdout_after (int) – holdout after n training examples

  • early_terminate (int) – Specify the number of passes tolerated when holdout loss doesn’t decrease before early termination

  • passes (int) – Number of Training Passes

  • initial_pass_length (int) – initial number of examples per pass

  • examples (int) – number of examples to parse

  • min_prediction (float) – Smallest prediction to output

  • max_prediction (float) – Largest prediction to output

  • sort_features (bool) – turn this on to disregard order in which features have been defined. This will lead to smaller cache sizes

  • loss_function (str) – default_value(“squared”), “Specify the loss function to be used, uses squared by default. Currently available ones are squared, classic, hinge, logistic and quantile.

  • quantile_tau (float) – Parameter tau associated with Quantile loss. Defaults to 0.5

  • l1 (float) – l_1 lambda (L1 regularization)

  • l2 (float) – l_2 lambda (L2 regularization)

  • no_bias_regularization (bool) – no bias in regularization

  • named_labels (str) – use names for labels (multiclass, etc.) rather than integers, argument specified all possible labels, comma-sep, eg “–named_labels Noun,Verb,Adj,Punc”

  • final_regressor (str) – Final regressor

  • f (str) – Final regressor

  • readable_model (str) – Output human-readable final regressor with numeric features

  • invert_hash (str) – Output human-readable final regressor with feature names. Computationally expensive.

  • save_resume (bool) – save extra state so learning can be resumed later with new data

  • preserve_performance_counters (bool) – reset performance counters when warmstarting

  • output_feature_regularizer_binary (str) – Per feature regularization output file

  • output_feature_regularizer_text (str) – Per feature regularization output file, in text

  • oaa (integer) – Use one-against-all multiclass learning with labels

  • oaa_subsample (int) – subsample this number of negative examples when learning

  • ect (integer) – Use error correcting tournament multiclass learning

  • csoaa (integer) – Use cost sensitive one-against-all multiclass learning

  • wap (integer) – Use weighted all pairs multiclass learning

  • probabilities (float) – predict probabilities of all classes

  • nn (integer) – Use a sigmoidal feed-forward neural network with N hidden units

  • inpass (bool) – Train or test sigmoidal feed-forward network with input pass-through

  • multitask (bool) – Share hidden layer across all reduced tasks

  • dropout (bool) – Train or test sigmoidal feed-forward network using dropout

  • meanfield (bool) – Train or test sigmoidal feed-forward network using mean field

  • conjugate_gradient (bool) – use conjugate gradient based optimization

  • bgfs (bool) – use bfgs updates

  • hessian_on (bool) – use second derivative in line search

  • mem (int) – memory in bfgs

  • termination (float) – termination threshold

  • lda (int) – Run lda with <int> topics

  • lda_alpha (float) – Prior on sparsity of per-document topic weights

  • lda_rho (float) – Prior on sparsity of topic distributions

  • lda_D (int) – Number of documents

  • lda_epsilon (float) – Loop convergence threshold

  • minibatch (int) – Minibatch size for LDA

  • svrg (bool) – Streaming Stochastic Variance Reduced Gradient

  • stage_size (int) – Number of passes per SVRG stage

  • ftrl (bool) – Run Follow the Proximal Regularized Leader

  • coin (bool) – Coin betting optimizer

  • pistol (bool) – PiSTOL - Parameter free STOchastic Learning

  • ftrl_alpha (float) – Alpha parameter for FTRL optimization

  • ftrl_beta (float) – Beta parameters for FTRL optimization

  • ksvm (bool) – kernel svm

  • kernel (str) – type of kernel (rbf or linear (default))

  • bandwidth (int) – bandwidth of rbf kernel

  • degree (int) – degree of poly kernel

  • sgd (bool) – use regular stochastic gradient descent update

  • adaptive (bool) – use adaptive, individual learning rates

  • adax (bool) – use adaptive learning rates with x^2 instead of g^2x^2

  • invariant (bool) – use save/importance aware updates

  • normalized (bool) – use per feature normalized updates

  • link (str) – Specify the link function - identity, logistic, glf1 or poisson

  • stage_poly (bool) – use stagewise polynomial feature learning

  • sched_exponent (int) – exponent controlling quantity of included features

  • batch_sz (int) – multiplier on batch size before including more features

  • batch_sz_no_doubling (bool) – batch_sz does not double

  • lrq (bool) – use low rank quadratic features

  • lrqdropout (bool) – use dropout training for low rank quadratic features

  • lrqfa (bool) – use low rank quadratic features with field aware weights

  • data (str) – path to data file for fitting external to sklearn

  • d (str) – path to data file for fitting external to sklearn

  • cache (str) – use a cache. default is <data>.cache

  • c (str) – use a cache. default is <data>.cache

  • cache_file (str) – path to cache file to use

  • json (bool) – enable JSON parsing

  • kill_cache (bool) – do not reuse existing cache file, create a new one always

  • k (bool) – do not reuse existing cache file, create a new one always

convert_labels: bool = True

Convert labels of the form [0,1] to [-1,1]

convert_to_vw: bool = True

flag to convert X input to vw format

fit(X=None, y=None, sample_weight=None)

Fit the model according to the given training data

Todo

For first pass create and store example objects. For N-1 passes use example objects directly (simulate cache file…but in memory for faster processing)

Parameters:

  • X – {array-like, sparse matrix}, shape (n_samples, n_features or 1 if not convert_to_vw) or Training vector, where n_samples in the number of samples and n_features is the number of features. if not using convert_to_vw, X is expected to be a list of vw formatted feature vector strings with labels

  • y – array-like, shape (n_samples,), optional if not convert_to_vw Target vector relative to X.

  • sample_weight – array-like, shape (n_samples,) sample weight vector relative to X.

Returns:

self

get_coefs()

Returns coefficient weights as ordered sparse matrix

Returns:

coefficient weights for model

Return type:

sparse matrix

get_intercept()

Returns intercept weight for model

Returns:

intercept value. 0 if no constant

Return type:

int

get_params(deep=True)

This returns the full set of vw and estimator parameters currently in use

get_vw()

Get the vw instance

Returns:

instance

Return type:

vowpalwabbit.Workspace

load(filename)

Load model from file

predict(X)

Predict with Vowpal Wabbit model

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features or 1)) – Training vector, where n_samples in the number of samples and n_features is the number of features. if not using convert_to_vw, X is expected to be a list of vw formatted feature vector strings with labels

Returns:

  1. Output vector relative to X.

Return type:

array-like, shape (n_samples, 1 or n_classes)

save(filename)

Save model to file

set_coefs(coefs)

Sets coefficients weights from ordered sparse matrix

Parameters:

coefs (sparse matrix) – coefficient weights for model

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → VW

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_params(**kwargs)

This destroys and recreates the Vowpal Wabbit model with updated parameters any parameters not provided will remain as they are currently

vw_: Workspace = None
class vowpalwabbit.sklearn.VWClassifier(loss_function='logistic', **kwargs)

Bases: VW, LinearClassifierMixin

Vowpal Wabbit Classifier model for binary classification Use VWMultiClassifier for multiclass classification Note - We are assuming the VW.predict returns logits, applying link=logistic will break this assumption

__init__(loss_function='logistic', **kwargs)

VW model constructor, exposing all supported parameters to keep sklearn happy

Parameters:

  • convert_to_vw (bool) – flag to convert X input to vw format

  • convert_labels (bool) – Convert labels of the form [0,1] to [-1,1]

  • ring_size (int) – size of example ring

  • strict_parse (bool) – throw on malformed examples

  • learning_rate (float) – Set learning rate

  • l (float) – Set learning rate

  • power_t (float) – t power value

  • decay_learning_rate (float) – Set Decay factor for learning_rate between passes

  • initial_t (float) – initial t value

  • feature_mask (str) – Use existing regressor to determine which parameters may be updated. If no initial_regressor given, also used for initial weights.

  • initial_regressor (str) – Initial regressor(s)

  • i (str) – Initial regressor(s)

  • initial_weight (float) – Set all weights to an initial value of arg.

  • random_weights (bool) – make initial weights random

  • normal_weights (bool) – make initial weights normal

  • truncated_normal_weights (bool) – make initial weights truncated normal

  • sparse_weights (float) – Use a sparse datastructure for weights

  • input_feature_regularizer (str) – Per feature regularization input file

  • quiet (bool) – Don’t output disgnostics and progress updates

  • random_seed (integer) – seed random number generator

  • hash (str) – , all

  • hash_seed (int) – seed for hash function

  • ignore (str) – ignore namespaces beginning with character <arg>

  • ignore_linear (str) – ignore namespaces beginning with character <arg> for linear terms only

  • keep (str) – keep namespaces beginning with character <arg>

  • redefine (str) – Redefine namespaces beginning with characters of string S as namespace N. <arg> shall be in form ‘N:=S’ where := is operator. Empty N or S are treated as default namespace. Use ‘:’ as a wildcard in S.

  • bit_precision (integer) – number of bits in the feature table

  • b (integer) – number of bits in the feature table

  • noconstant (bool) – Don’t add a constant feature

  • constant (float) – Set initial value of constant

  • C (float) – Set initial value of constant

  • ngram (str) – Generate N grams. To generate N grams for a single namespace ‘foo’, arg should be fN.

  • skips (str) – Generate skips in N grams. This in conjunction with the ngram tag can be used to generate generalized n-skip-k-gram. To generate n-skips for a single namespace ‘foo’, arg should be fN.

  • feature_limit (str) – limit to N features. To apply to a single namespace ‘foo’, arg should be fN

  • affix (str) – generate prefixes/suffixes of features; argument ‘+2a,-3b,+1’ means generate 2-char prefixes for namespace a, 3-char suffixes for b and 1 char prefixes for default namespace

  • spelling (str) – compute spelling features for a give namespace (use ‘_’ for default namespace)

  • dictionary (str) – read a dictionary for additional features (arg either ‘x:file’ or just ‘file’)

  • dictionary_path (str) – look in this directory for dictionaries; defaults to current directory or env{PATH}

  • interactions (str) – Create feature interactions of any level between namespaces.

  • permutations (bool) – Use permutations instead of combinations for feature interactions of same namespace.

  • leave_duplicate_interactions (bool) – Don’t remove interactions with duplicate combinations of namespaces. For ex. this is a duplicate: ‘-q ab -q ba’ and a lot more in ‘-q ::’.

  • quadratic (str) – Create and use quadratic features, q:: corresponds to a wildcard for all printable characters

  • q (str) – Create and use quadratic features, q:: corresponds to a wildcard for all printable characters

  • cubic (str) – Create and use cubic features

  • testonly (bool) – Ignore label information and just test

  • t (bool) – Ignore label information and just test

  • holdout_off (bool) – no holdout data in multiple passes

  • holdout_period (int) – holdout period for test only

  • holdout_after (int) – holdout after n training examples

  • early_terminate (int) – Specify the number of passes tolerated when holdout loss doesn’t decrease before early termination

  • passes (int) – Number of Training Passes

  • initial_pass_length (int) – initial number of examples per pass

  • examples (int) – number of examples to parse

  • min_prediction (float) – Smallest prediction to output

  • max_prediction (float) – Largest prediction to output

  • sort_features (bool) – turn this on to disregard order in which features have been defined. This will lead to smaller cache sizes

  • loss_function (str) – default_value(“squared”), “Specify the loss function to be used, uses squared by default. Currently available ones are squared, classic, hinge, logistic and quantile.

  • quantile_tau (float) – Parameter tau associated with Quantile loss. Defaults to 0.5

  • l1 (float) – l_1 lambda (L1 regularization)

  • l2 (float) – l_2 lambda (L2 regularization)

  • no_bias_regularization (bool) – no bias in regularization

  • named_labels (str) – use names for labels (multiclass, etc.) rather than integers, argument specified all possible labels, comma-sep, eg “–named_labels Noun,Verb,Adj,Punc”

  • final_regressor (str) – Final regressor

  • f (str) – Final regressor

  • readable_model (str) – Output human-readable final regressor with numeric features

  • invert_hash (str) – Output human-readable final regressor with feature names. Computationally expensive.

  • save_resume (bool) – save extra state so learning can be resumed later with new data

  • preserve_performance_counters (bool) – reset performance counters when warmstarting

  • output_feature_regularizer_binary (str) – Per feature regularization output file

  • output_feature_regularizer_text (str) – Per feature regularization output file, in text

  • oaa (integer) – Use one-against-all multiclass learning with labels

  • oaa_subsample (int) – subsample this number of negative examples when learning

  • ect (integer) – Use error correcting tournament multiclass learning

  • csoaa (integer) – Use cost sensitive one-against-all multiclass learning

  • wap (integer) – Use weighted all pairs multiclass learning

  • probabilities (float) – predict probabilities of all classes

  • nn (integer) – Use a sigmoidal feed-forward neural network with N hidden units

  • inpass (bool) – Train or test sigmoidal feed-forward network with input pass-through

  • multitask (bool) – Share hidden layer across all reduced tasks

  • dropout (bool) – Train or test sigmoidal feed-forward network using dropout

  • meanfield (bool) – Train or test sigmoidal feed-forward network using mean field

  • conjugate_gradient (bool) – use conjugate gradient based optimization

  • bgfs (bool) – use bfgs updates

  • hessian_on (bool) – use second derivative in line search

  • mem (int) – memory in bfgs

  • termination (float) – termination threshold

  • lda (int) – Run lda with <int> topics

  • lda_alpha (float) – Prior on sparsity of per-document topic weights

  • lda_rho (float) – Prior on sparsity of topic distributions

  • lda_D (int) – Number of documents

  • lda_epsilon (float) – Loop convergence threshold

  • minibatch (int) – Minibatch size for LDA

  • svrg (bool) – Streaming Stochastic Variance Reduced Gradient

  • stage_size (int) – Number of passes per SVRG stage

  • ftrl (bool) – Run Follow the Proximal Regularized Leader

  • coin (bool) – Coin betting optimizer

  • pistol (bool) – PiSTOL - Parameter free STOchastic Learning

  • ftrl_alpha (float) – Alpha parameter for FTRL optimization

  • ftrl_beta (float) – Beta parameters for FTRL optimization

  • ksvm (bool) – kernel svm

  • kernel (str) – type of kernel (rbf or linear (default))

  • bandwidth (int) – bandwidth of rbf kernel

  • degree (int) – degree of poly kernel

  • sgd (bool) – use regular stochastic gradient descent update

  • adaptive (bool) – use adaptive, individual learning rates

  • adax (bool) – use adaptive learning rates with x^2 instead of g^2x^2

  • invariant (bool) – use save/importance aware updates

  • normalized (bool) – use per feature normalized updates

  • link (str) – Specify the link function - identity, logistic, glf1 or poisson

  • stage_poly (bool) – use stagewise polynomial feature learning

  • sched_exponent (int) – exponent controlling quantity of included features

  • batch_sz (int) – multiplier on batch size before including more features

  • batch_sz_no_doubling (bool) – batch_sz does not double

  • lrq (bool) – use low rank quadratic features

  • lrqdropout (bool) – use dropout training for low rank quadratic features

  • lrqfa (bool) – use low rank quadratic features with field aware weights

  • data (str) – path to data file for fitting external to sklearn

  • d (str) – path to data file for fitting external to sklearn

  • cache (str) – use a cache. default is <data>.cache

  • c (str) – use a cache. default is <data>.cache

  • cache_file (str) – path to cache file to use

  • json (bool) – enable JSON parsing

  • kill_cache (bool) – do not reuse existing cache file, create a new one always

  • k (bool) – do not reuse existing cache file, create a new one always

classes_ = array([-1.,  1.])

Binary class labels

coef_ = None

Empty sparse matrix used the check if model has been fit

decision_function(X)

Predict confidence scores for samples. The confidence score for a sample is the signed distance of that sample to the hyperplane.

Parameters:

X – array_like or sparse matrix, shape (n_samples, n_features) Samples.

Returns:

array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)

Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

fit(X=None, y=None, sample_weight=None)

Fit the model according to the given training data.

Parameters:

  • X – {array-like, sparse matrix} of shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features.

  • y – array-like of shape (n_samples,) Target vector relative to X.

  • sample_weight – array-like of shape (n_samples,) default=None Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.

Returns:

self

predict(X)

Predict class labels for samples in X.

Parameters:

X – array_like or sparse matrix, shape (n_samples, n_features) Samples.

Returns:

  1. Predicted class label per sample.

Return type:

array, shape [n_samples]

predict_proba(X)

Predict probabilities for samples

Parameters:

X – {array-like, sparse matrix}, shape = (n_samples, n_features) Samples.

Returns:

  1. Returns the probability of the sample for each class in the model,

    where classes are ordered as they are in self.classes_.

Return type:

array-like of shape (n_samples, n_classes)

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → VWClassifier

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → VWClassifier

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

class vowpalwabbit.sklearn.VWMultiClassifier(probabilities=True, **kwargs)

Bases: VWClassifier

Vowpal Wabbit MultiClassifier model Note - We are assuming the VW.predict returns probabilities, setting probabilities=False will break this assumption

__init__(probabilities=True, **kwargs)

VW model constructor, exposing all supported parameters to keep sklearn happy

Parameters:

  • convert_to_vw (bool) – flag to convert X input to vw format

  • convert_labels (bool) – Convert labels of the form [0,1] to [-1,1]

  • ring_size (int) – size of example ring

  • strict_parse (bool) – throw on malformed examples

  • learning_rate (float) – Set learning rate

  • l (float) – Set learning rate

  • power_t (float) – t power value

  • decay_learning_rate (float) – Set Decay factor for learning_rate between passes

  • initial_t (float) – initial t value

  • feature_mask (str) – Use existing regressor to determine which parameters may be updated. If no initial_regressor given, also used for initial weights.

  • initial_regressor (str) – Initial regressor(s)

  • i (str) – Initial regressor(s)

  • initial_weight (float) – Set all weights to an initial value of arg.

  • random_weights (bool) – make initial weights random

  • normal_weights (bool) – make initial weights normal

  • truncated_normal_weights (bool) – make initial weights truncated normal

  • sparse_weights (float) – Use a sparse datastructure for weights

  • input_feature_regularizer (str) – Per feature regularization input file

  • quiet (bool) – Don’t output disgnostics and progress updates

  • random_seed (integer) – seed random number generator

  • hash (str) – , all

  • hash_seed (int) – seed for hash function

  • ignore (str) – ignore namespaces beginning with character <arg>

  • ignore_linear (str) – ignore namespaces beginning with character <arg> for linear terms only

  • keep (str) – keep namespaces beginning with character <arg>

  • redefine (str) – Redefine namespaces beginning with characters of string S as namespace N. <arg> shall be in form ‘N:=S’ where := is operator. Empty N or S are treated as default namespace. Use ‘:’ as a wildcard in S.

  • bit_precision (integer) – number of bits in the feature table

  • b (integer) – number of bits in the feature table

  • noconstant (bool) – Don’t add a constant feature

  • constant (float) – Set initial value of constant

  • C (float) – Set initial value of constant

  • ngram (str) – Generate N grams. To generate N grams for a single namespace ‘foo’, arg should be fN.

  • skips (str) – Generate skips in N grams. This in conjunction with the ngram tag can be used to generate generalized n-skip-k-gram. To generate n-skips for a single namespace ‘foo’, arg should be fN.

  • feature_limit (str) – limit to N features. To apply to a single namespace ‘foo’, arg should be fN

  • affix (str) – generate prefixes/suffixes of features; argument ‘+2a,-3b,+1’ means generate 2-char prefixes for namespace a, 3-char suffixes for b and 1 char prefixes for default namespace

  • spelling (str) – compute spelling features for a give namespace (use ‘_’ for default namespace)

  • dictionary (str) – read a dictionary for additional features (arg either ‘x:file’ or just ‘file’)

  • dictionary_path (str) – look in this directory for dictionaries; defaults to current directory or env{PATH}

  • interactions (str) – Create feature interactions of any level between namespaces.

  • permutations (bool) – Use permutations instead of combinations for feature interactions of same namespace.

  • leave_duplicate_interactions (bool) – Don’t remove interactions with duplicate combinations of namespaces. For ex. this is a duplicate: ‘-q ab -q ba’ and a lot more in ‘-q ::’.

  • quadratic (str) – Create and use quadratic features, q:: corresponds to a wildcard for all printable characters

  • q (str) – Create and use quadratic features, q:: corresponds to a wildcard for all printable characters

  • cubic (str) – Create and use cubic features

  • testonly (bool) – Ignore label information and just test

  • t (bool) – Ignore label information and just test

  • holdout_off (bool) – no holdout data in multiple passes

  • holdout_period (int) – holdout period for test only

  • holdout_after (int) – holdout after n training examples

  • early_terminate (int) – Specify the number of passes tolerated when holdout loss doesn’t decrease before early termination

  • passes (int) – Number of Training Passes

  • initial_pass_length (int) – initial number of examples per pass

  • examples (int) – number of examples to parse

  • min_prediction (float) – Smallest prediction to output

  • max_prediction (float) – Largest prediction to output

  • sort_features (bool) – turn this on to disregard order in which features have been defined. This will lead to smaller cache sizes

  • loss_function (str) – default_value(“squared”), “Specify the loss function to be used, uses squared by default. Currently available ones are squared, classic, hinge, logistic and quantile.

  • quantile_tau (float) – Parameter tau associated with Quantile loss. Defaults to 0.5

  • l1 (float) – l_1 lambda (L1 regularization)

  • l2 (float) – l_2 lambda (L2 regularization)

  • no_bias_regularization (bool) – no bias in regularization

  • named_labels (str) – use names for labels (multiclass, etc.) rather than integers, argument specified all possible labels, comma-sep, eg “–named_labels Noun,Verb,Adj,Punc”

  • final_regressor (str) – Final regressor

  • f (str) – Final regressor

  • readable_model (str) – Output human-readable final regressor with numeric features

  • invert_hash (str) – Output human-readable final regressor with feature names. Computationally expensive.

  • save_resume (bool) – save extra state so learning can be resumed later with new data

  • preserve_performance_counters (bool) – reset performance counters when warmstarting

  • output_feature_regularizer_binary (str) – Per feature regularization output file

  • output_feature_regularizer_text (str) – Per feature regularization output file, in text

  • oaa (integer) – Use one-against-all multiclass learning with labels

  • oaa_subsample (int) – subsample this number of negative examples when learning

  • ect (integer) – Use error correcting tournament multiclass learning

  • csoaa (integer) – Use cost sensitive one-against-all multiclass learning

  • wap (integer) – Use weighted all pairs multiclass learning

  • probabilities (float) – predict probabilities of all classes

  • nn (integer) – Use a sigmoidal feed-forward neural network with N hidden units

  • inpass (bool) – Train or test sigmoidal feed-forward network with input pass-through

  • multitask (bool) – Share hidden layer across all reduced tasks

  • dropout (bool) – Train or test sigmoidal feed-forward network using dropout

  • meanfield (bool) – Train or test sigmoidal feed-forward network using mean field

  • conjugate_gradient (bool) – use conjugate gradient based optimization

  • bgfs (bool) – use bfgs updates

  • hessian_on (bool) – use second derivative in line search

  • mem (int) – memory in bfgs

  • termination (float) – termination threshold

  • lda (int) – Run lda with <int> topics

  • lda_alpha (float) – Prior on sparsity of per-document topic weights

  • lda_rho (float) – Prior on sparsity of topic distributions

  • lda_D (int) – Number of documents

  • lda_epsilon (float) – Loop convergence threshold

  • minibatch (int) – Minibatch size for LDA

  • svrg (bool) – Streaming Stochastic Variance Reduced Gradient

  • stage_size (int) – Number of passes per SVRG stage

  • ftrl (bool) – Run Follow the Proximal Regularized Leader

  • coin (bool) – Coin betting optimizer

  • pistol (bool) – PiSTOL - Parameter free STOchastic Learning

  • ftrl_alpha (float) – Alpha parameter for FTRL optimization

  • ftrl_beta (float) – Beta parameters for FTRL optimization

  • ksvm (bool) – kernel svm

  • kernel (str) – type of kernel (rbf or linear (default))

  • bandwidth (int) – bandwidth of rbf kernel

  • degree (int) – degree of poly kernel

  • sgd (bool) – use regular stochastic gradient descent update

  • adaptive (bool) – use adaptive, individual learning rates

  • adax (bool) – use adaptive learning rates with x^2 instead of g^2x^2

  • invariant (bool) – use save/importance aware updates

  • normalized (bool) – use per feature normalized updates

  • link (str) – Specify the link function - identity, logistic, glf1 or poisson

  • stage_poly (bool) – use stagewise polynomial feature learning

  • sched_exponent (int) – exponent controlling quantity of included features

  • batch_sz (int) – multiplier on batch size before including more features

  • batch_sz_no_doubling (bool) – batch_sz does not double

  • lrq (bool) – use low rank quadratic features

  • lrqdropout (bool) – use dropout training for low rank quadratic features

  • lrqfa (bool) – use low rank quadratic features with field aware weights

  • data (str) – path to data file for fitting external to sklearn

  • d (str) – path to data file for fitting external to sklearn

  • cache (str) – use a cache. default is <data>.cache

  • c (str) – use a cache. default is <data>.cache

  • cache_file (str) – path to cache file to use

  • json (bool) – enable JSON parsing

  • kill_cache (bool) – do not reuse existing cache file, create a new one always

  • k (bool) – do not reuse existing cache file, create a new one always

classes_ = None

Class labels

decision_function(X)

Predict confidence scores for samples. The confidence score for a sample is the signed distance of that sample to the hyperplane.

Parameters:

X – array_like or sparse matrix, shape (n_samples, n_features) Samples.

Returns:

Confidence scores per (sample, class) combination.

Return type:

array, shape=(n_samples, n_classes)

estimator_ = None

“type of estimator to use [csoaa, ect, oaa, wap] and number of classes

fit(X=None, y=None, sample_weight=None)

Fit the model according to the given training data.

Parameters:

  • X – {array-like, sparse matrix} of shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features.

  • y – array-like of shape (n_samples,) Target vector relative to X.

  • sample_weight – array-like of shape (n_samples,) default=None Array of weights that are assigned to individual samples. If not provided, then each sample is given unit weight.

Returns:

self

predict_proba(X)

Predict probabilities for each class.

Parameters:

X – {array-like, sparse matrix}, shape = (n_samples, n_features) Samples.

Returns:

array, shape=(n_samples,) if n_classes == 2 else (n_samples, n_classes)

Confidence scores per (sample, class) combination. In the binary case, confidence score for self.classes_[1] where >0 means this class would be predicted.

Examples

>>> import numpy as np
>>> X = np.array([ [10, 10], [8, 10], [-5, 5.5], [-5.4, 5.5], [-20, -20],  [-15, -20] ])
>>> y = np.array([1, 1, 2, 2, 3, 3])
>>> from vowpalwabbit.sklearn import VWMultiClassifier
>>> model = VWMultiClassifier(oaa=3, loss_function='logistic')
>>> _ = model.fit(X, y)
>>> model.predict_proba(X)
array([[0.38926664, 0.30536669, 0.30536669],
       [0.40663728, 0.2966814 , 0.2966814 ],
       [0.52337217, 0.23831393, 0.23831393],
       [0.52698863, 0.23650573, 0.23650573],
       [0.6543135 , 0.17284323, 0.17284323],
       [0.61224902, 0.19387549, 0.19387549]])
set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → VWMultiClassifier

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → VWMultiClassifier

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

class vowpalwabbit.sklearn.VWRegressor(convert_labels=False, **kwargs)

Bases: VW, RegressorMixin

Vowpal Wabbit Regressor model

__init__(convert_labels=False, **kwargs)

VW model constructor, exposing all supported parameters to keep sklearn happy

Parameters:

  • convert_to_vw (bool) – flag to convert X input to vw format

  • convert_labels (bool) – Convert labels of the form [0,1] to [-1,1]

  • ring_size (int) – size of example ring

  • strict_parse (bool) – throw on malformed examples

  • learning_rate (float) – Set learning rate

  • l (float) – Set learning rate

  • power_t (float) – t power value

  • decay_learning_rate (float) – Set Decay factor for learning_rate between passes

  • initial_t (float) – initial t value

  • feature_mask (str) – Use existing regressor to determine which parameters may be updated. If no initial_regressor given, also used for initial weights.

  • initial_regressor (str) – Initial regressor(s)

  • i (str) – Initial regressor(s)

  • initial_weight (float) – Set all weights to an initial value of arg.

  • random_weights (bool) – make initial weights random

  • normal_weights (bool) – make initial weights normal

  • truncated_normal_weights (bool) – make initial weights truncated normal

  • sparse_weights (float) – Use a sparse datastructure for weights

  • input_feature_regularizer (str) – Per feature regularization input file

  • quiet (bool) – Don’t output disgnostics and progress updates

  • random_seed (integer) – seed random number generator

  • hash (str) – , all

  • hash_seed (int) – seed for hash function

  • ignore (str) – ignore namespaces beginning with character <arg>

  • ignore_linear (str) – ignore namespaces beginning with character <arg> for linear terms only

  • keep (str) – keep namespaces beginning with character <arg>

  • redefine (str) – Redefine namespaces beginning with characters of string S as namespace N. <arg> shall be in form ‘N:=S’ where := is operator. Empty N or S are treated as default namespace. Use ‘:’ as a wildcard in S.

  • bit_precision (integer) – number of bits in the feature table

  • b (integer) – number of bits in the feature table

  • noconstant (bool) – Don’t add a constant feature

  • constant (float) – Set initial value of constant

  • C (float) – Set initial value of constant

  • ngram (str) – Generate N grams. To generate N grams for a single namespace ‘foo’, arg should be fN.

  • skips (str) – Generate skips in N grams. This in conjunction with the ngram tag can be used to generate generalized n-skip-k-gram. To generate n-skips for a single namespace ‘foo’, arg should be fN.

  • feature_limit (str) – limit to N features. To apply to a single namespace ‘foo’, arg should be fN

  • affix (str) – generate prefixes/suffixes of features; argument ‘+2a,-3b,+1’ means generate 2-char prefixes for namespace a, 3-char suffixes for b and 1 char prefixes for default namespace

  • spelling (str) – compute spelling features for a give namespace (use ‘_’ for default namespace)

  • dictionary (str) – read a dictionary for additional features (arg either ‘x:file’ or just ‘file’)

  • dictionary_path (str) – look in this directory for dictionaries; defaults to current directory or env{PATH}

  • interactions (str) – Create feature interactions of any level between namespaces.

  • permutations (bool) – Use permutations instead of combinations for feature interactions of same namespace.

  • leave_duplicate_interactions (bool) – Don’t remove interactions with duplicate combinations of namespaces. For ex. this is a duplicate: ‘-q ab -q ba’ and a lot more in ‘-q ::’.

  • quadratic (str) – Create and use quadratic features, q:: corresponds to a wildcard for all printable characters

  • q (str) – Create and use quadratic features, q:: corresponds to a wildcard for all printable characters

  • cubic (str) – Create and use cubic features

  • testonly (bool) – Ignore label information and just test

  • t (bool) – Ignore label information and just test

  • holdout_off (bool) – no holdout data in multiple passes

  • holdout_period (int) – holdout period for test only

  • holdout_after (int) – holdout after n training examples

  • early_terminate (int) – Specify the number of passes tolerated when holdout loss doesn’t decrease before early termination

  • passes (int) – Number of Training Passes

  • initial_pass_length (int) – initial number of examples per pass

  • examples (int) – number of examples to parse

  • min_prediction (float) – Smallest prediction to output

  • max_prediction (float) – Largest prediction to output

  • sort_features (bool) – turn this on to disregard order in which features have been defined. This will lead to smaller cache sizes

  • loss_function (str) – default_value(“squared”), “Specify the loss function to be used, uses squared by default. Currently available ones are squared, classic, hinge, logistic and quantile.

  • quantile_tau (float) – Parameter tau associated with Quantile loss. Defaults to 0.5

  • l1 (float) – l_1 lambda (L1 regularization)

  • l2 (float) – l_2 lambda (L2 regularization)

  • no_bias_regularization (bool) – no bias in regularization

  • named_labels (str) – use names for labels (multiclass, etc.) rather than integers, argument specified all possible labels, comma-sep, eg “–named_labels Noun,Verb,Adj,Punc”

  • final_regressor (str) – Final regressor

  • f (str) – Final regressor

  • readable_model (str) – Output human-readable final regressor with numeric features

  • invert_hash (str) – Output human-readable final regressor with feature names. Computationally expensive.

  • save_resume (bool) – save extra state so learning can be resumed later with new data

  • preserve_performance_counters (bool) – reset performance counters when warmstarting

  • output_feature_regularizer_binary (str) – Per feature regularization output file

  • output_feature_regularizer_text (str) – Per feature regularization output file, in text

  • oaa (integer) – Use one-against-all multiclass learning with labels

  • oaa_subsample (int) – subsample this number of negative examples when learning

  • ect (integer) – Use error correcting tournament multiclass learning

  • csoaa (integer) – Use cost sensitive one-against-all multiclass learning

  • wap (integer) – Use weighted all pairs multiclass learning

  • probabilities (float) – predict probabilities of all classes

  • nn (integer) – Use a sigmoidal feed-forward neural network with N hidden units

  • inpass (bool) – Train or test sigmoidal feed-forward network with input pass-through

  • multitask (bool) – Share hidden layer across all reduced tasks

  • dropout (bool) – Train or test sigmoidal feed-forward network using dropout

  • meanfield (bool) – Train or test sigmoidal feed-forward network using mean field

  • conjugate_gradient (bool) – use conjugate gradient based optimization

  • bgfs (bool) – use bfgs updates

  • hessian_on (bool) – use second derivative in line search

  • mem (int) – memory in bfgs

  • termination (float) – termination threshold

  • lda (int) – Run lda with <int> topics

  • lda_alpha (float) – Prior on sparsity of per-document topic weights

  • lda_rho (float) – Prior on sparsity of topic distributions

  • lda_D (int) – Number of documents

  • lda_epsilon (float) – Loop convergence threshold

  • minibatch (int) – Minibatch size for LDA

  • svrg (bool) – Streaming Stochastic Variance Reduced Gradient

  • stage_size (int) – Number of passes per SVRG stage

  • ftrl (bool) – Run Follow the Proximal Regularized Leader

  • coin (bool) – Coin betting optimizer

  • pistol (bool) – PiSTOL - Parameter free STOchastic Learning

  • ftrl_alpha (float) – Alpha parameter for FTRL optimization

  • ftrl_beta (float) – Beta parameters for FTRL optimization

  • ksvm (bool) – kernel svm

  • kernel (str) – type of kernel (rbf or linear (default))

  • bandwidth (int) – bandwidth of rbf kernel

  • degree (int) – degree of poly kernel

  • sgd (bool) – use regular stochastic gradient descent update

  • adaptive (bool) – use adaptive, individual learning rates

  • adax (bool) – use adaptive learning rates with x^2 instead of g^2x^2

  • invariant (bool) – use save/importance aware updates

  • normalized (bool) – use per feature normalized updates

  • link (str) – Specify the link function - identity, logistic, glf1 or poisson

  • stage_poly (bool) – use stagewise polynomial feature learning

  • sched_exponent (int) – exponent controlling quantity of included features

  • batch_sz (int) – multiplier on batch size before including more features

  • batch_sz_no_doubling (bool) – batch_sz does not double

  • lrq (bool) – use low rank quadratic features

  • lrqdropout (bool) – use dropout training for low rank quadratic features

  • lrqfa (bool) – use low rank quadratic features with field aware weights

  • data (str) – path to data file for fitting external to sklearn

  • d (str) – path to data file for fitting external to sklearn

  • cache (str) – use a cache. default is <data>.cache

  • c (str) – use a cache. default is <data>.cache

  • cache_file (str) – path to cache file to use

  • json (bool) – enable JSON parsing

  • kill_cache (bool) – do not reuse existing cache file, create a new one always

  • k (bool) – do not reuse existing cache file, create a new one always

set_fit_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → VWRegressor

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in fit.

Returns:

self – The updated object.

Return type:

object

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → VWRegressor

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to score.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weight (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for sample_weight parameter in score.

Returns:

self – The updated object.

Return type:

object

vowpalwabbit.sklearn.tovw(x, y=None, sample_weight=None, convert_labels=False)

Convert array or sparse matrix to Vowpal Wabbit format

Parameters:

  • x – {array-like, sparse matrix}, shape (n_samples, n_features) Training vector, where n_samples is the number of samples and n_features is the number of features.

  • y – {array-like}, shape (n_samples,), optional Target vector relative to X.

  • sample_weight – {array-like}, shape (n_samples,), optional sample weight vector relative to X.

  • convert_labels – {bool} convert labels of the form [0,1] to [-1,1]

Returns:

{array-like}, shape (n_samples, 1)

Training vectors in VW string format

Examples

>>> import pandas as pd
>>> from sklearn.feature_extraction.text import HashingVectorizer
>>> from vowpalwabbit.sklearn import tovw
>>> X = pd.Series(['cat', 'dog', 'cat', 'cat'], name='catdog')
>>> y = pd.Series([-1, 1, -1, -1], name='label')
>>> hv = HashingVectorizer()
>>> hashed = hv.fit_transform(X)
>>> tovw(x=hashed, y=y)
['-1 1 | 300839:1', '1 1 | 980517:-1', '-1 1 | 300839:1', '-1 1 | 300839:1']