sklearndf.classification.SGDClassifierDF#
- class sklearndf.classification.SGDClassifierDF(loss='hinge', *, penalty='l2', alpha=0.0001, l1_ratio=0.15, fit_intercept=True, max_iter=1000, tol=0.001, shuffle=True, verbose=0, epsilon=0.1, n_jobs=None, random_state=None, learning_rate='optimal', eta0=0.0, power_t=0.5, early_stopping=False, validation_fraction=0.1, n_iter_no_change=5, class_weight=None, warm_start=False, average=False)[source]#
Linear classifiers (SVM, logistic regression, etc.) with SGD training.
Note
This class is a wrapper around class
sklearn.linear_model.SGDClassifier
. It provides enhanced support forpandas
data frames, and otherwise delegates all attribute access and method calls to an associatedSGDClassifier
instance.- Bases
- Metaclasses
Method summary
Make an unfitted clone of this estimator.
Compute the decision function for the given inputs.
Fit this estimator using the given inputs.
Make a new wrapped DF estimator, delegating to a given native estimator that has already been fitted.
Get the parameters for this estimator.
Perform incremental fit on a batch of samples.
Predict outputs for the given inputs.
Predict class log-probabilities for the given inputs.
Predict class probabilities for the given inputs.
Score this learner using the given inputs and outputs.
Set the parameters of this estimator.
Render this object as an expression.
Attribute summary
COL_FEATURE
Name assigned to an
Index
or aSeries
with the names of the features used to fit aEstimatorDF
.COL_PREDICTION
Name of
Series
objects containing the predictions of single-output learners.Get the classes predicted by this classifier as a numpy array of class labels for single-output problems, or a list of such arrays for multi-output problems
The pandas column index with the names of the features used to fit this estimator.
True
if this object is fitted,False
otherwise.The number of features used to fit this estimator.
The number of outputs used to fit this estimator.
The native estimator that this wrapper delegates to.
The name(s) of the output(s) this supervised learner was fitted to.
Definitions
- clone()#
Make an unfitted clone of this estimator.
- Return type
- Returns
the unfitted clone
- decision_function(X, **predict_params)#
Compute the decision function for the given inputs.
The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.
- Parameters
- Return type
- Returns
a data frame with observations as rows and classes as columns, and values as the raw values predicted per observation and class; for multi-output classifiers, a list of one observation/class data frames per output
- fit(X, y=None, **fit_params)#
Fit this estimator using the given inputs.
- Parameters
- Return type
- Returns
self
- classmethod from_fitted(estimator, features_in, n_outputs)#
Make a new wrapped DF estimator, delegating to a given native estimator that has already been fitted.
- Parameters
estimator (
SGDClassifier
) – the fitted native estimator to use as the delegatefeatures_in (
Index
) – the column names of X used for fitting the estimatorn_outputs (
int
) – the number of outputs in y used for fitting the estimator
- Return type
- Returns
the wrapped data frame estimator
- get_params(deep=True)#
Get the parameters for this estimator.
- partial_fit(X, y, classes=None, sample_weight=None)#
Perform incremental fit on a batch of samples.
This method is meant to be called multiple times for subsets of training data which, e.g., couldn’t fit in the required memory in full. It can be also used for online learning.
- Parameters
X (
Union
[Series
,DataFrame
]) – data frame with observations as rows and features as columnsy (
Union
[Series
,DataFrame
]) – a series or data frame with one or more outputs per observationclasses (
Optional
[Sequence
[Any
]]) – all classes present across all calls topartial_fit
; only required for the first call of this methodsample_weight (
Optional
[Series
]) – optional weights applied to individual samples
- Return type
- Returns
self
- predict(X, **predict_params)#
Predict outputs for the given inputs.
The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.
- Parameters
- Return type
- Returns
predictions per observation as a series, or as a data frame in case of multiple outputs
- predict_log_proba(X, **predict_params)#
Predict class log-probabilities for the given inputs.
The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.
- Parameters
- Return type
- Returns
a data frame with observations as rows and classes as columns, and values as log-probabilities per observation and class; for multi-output classifiers, a list of one observation/class data frames per output
- predict_proba(X, **predict_params)#
Predict class probabilities for the given inputs.
The inputs must have the same features as the inputs used to fit this learner. The features can be provided in any order since they are identified by their column names.
- Parameters
- Return type
- Returns
a data frame with observations as rows and classes as columns, and values as probabilities per observation and class; for multi-output classifiers, a list of one observation/class data frames per output
- score(X, y, sample_weight=None)#
Score this learner using the given inputs and outputs.
- Parameters
X (
Union
[Series
,DataFrame
]) – data frame with observations as rows and features as columnsy (
Series
) – a series or data frame with the true outputs per observationsample_weight (
Optional
[Series
]) – optional series of scalar weights, for calculating the resulting score as the weighted mean of the scores for the individual predictions
- Return type
- Returns
the score
- set_params(**params)#
Set the parameters of this estimator.
Valid parameter keys can be obtained by calling
get_params()
.- Parameters
params (
Any
) – the estimator parameters to set- Return type
- Returns
self
- to_expression()#
Render this object as an expression.
- Return type
- Returns
the expression representing this object
- property classes_: Union[numpy.ndarray[Any, numpy.dtype[Any]], List[numpy.ndarray[Any, numpy.dtype[Any]]]]#
Get the classes predicted by this classifier as a numpy array of class labels for single-output problems, or a list of such arrays for multi-output problems
- property feature_names_in_: pandas.Index#
The pandas column index with the names of the features used to fit this estimator.
- Raises
AttributeError – this estimator is not fitted
- Return type
- property n_features_in_: int#
The number of features used to fit this estimator.
- Raises
AttributeError – this estimator is not fitted
- Return type
- property n_outputs_: int#
The number of outputs used to fit this estimator.
- Raises
AttributeError – this estimator is not fitted
- Return type
- property native_estimator: sklearndf.wrapper.T_NativeEstimator#
The native estimator that this wrapper delegates to.
- Return type
TypeVar
(T_NativeEstimator
, bound=BaseEstimator
)
- property output_names_: List[str]#
The name(s) of the output(s) this supervised learner was fitted to.
- Raises
sklearn.exceptions.NotFittedError – this estimator is not fitted
- Return type