TheilSenRegressor
¶
-
class
ibex.sklearn.linear_model.
TheilSenRegressor
(fit_intercept=True, copy_X=True, max_subpopulation=10000.0, n_subsamples=None, max_iter=300, tol=0.001, random_state=None, n_jobs=1, verbose=False)¶ Bases:
sklearn.linear_model.theil_sen.TheilSenRegressor
,ibex._base.FrameMixin
Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Note
The documentation following is of the original class wrapped by this class. This class wraps the attribute
coef_
.Example:
>>> import pandas as pd >>> import numpy as np >>> from ibex.sklearn import datasets >>> from ibex.sklearn.linear_model import LinearRegression as PdLinearRegression
>>> iris = datasets.load_iris() >>> features = iris['feature_names'] >>> iris = pd.DataFrame( ... np.c_[iris['data'], iris['target']], ... columns=features+['class'])
>>> iris[features] sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) 0 5.1 3.5 1.4 0.2 1 4.9 3.0 1.4 0.2 2 4.7 3.2 1.3 0.2 3 4.6 3.1 1.5 0.2 4 5.0 3.6 1.4 0.2 ...
>>> from ibex.sklearn import linear_model as pd_linear_model >>> >>> prd = pd_linear_model.TheilSenRegressor().fit(iris[features], iris['class']) >>> >>> prd.coef_ sepal length (cm) ... sepal width (cm) ... petal length (cm) ... petal width (cm) ... dtype: float64
Note
The documentation following is of the original class wrapped by this class. This class wraps the attribute
intercept_
.Example:
>>> import pandas as pd >>> import numpy as np >>> from ibex.sklearn import datasets >>> from ibex.sklearn.linear_model import LinearRegression as PdLinearRegression
>>> iris = datasets.load_iris() >>> features = iris['feature_names'] >>> iris = pd.DataFrame( ... np.c_[iris['data'], iris['target']], ... columns=features+['class'])
>>> iris[features] sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) 0 5.1 3.5 1.4 0.2 1 4.9 3.0 1.4 0.2 2 4.7 3.2 1.3 0.2 3 4.6 3.1 1.5 0.2 4 5.0 3.6 1.4 0.2 ...
>>> >>> from ibex.sklearn import linear_model as pd_linear_model >>> >>> prd = pd_linear_model.TheilSenRegressor().fit(iris[features], iris['class']) >>> >>> #scalar intercept >>> type(prd.intercept_) <class 'numpy.float64'>
Theil-Sen Estimator: robust multivariate regression model.
The algorithm calculates least square solutions on subsets with size n_subsamples of the samples in X. Any value of n_subsamples between the number of features and samples leads to an estimator with a compromise between robustness and efficiency. Since the number of least square solutions is “n_samples choose n_subsamples”, it can be extremely large and can therefore be limited with max_subpopulation. If this limit is reached, the subsets are chosen randomly. In a final step, the spatial median (or L1 median) is calculated of all least square solutions.
Read more in the User Guide.
- fit_intercept : boolean, optional, default True
- Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations.
- copy_X : boolean, optional, default True
- If True, X will be copied; else, it may be overwritten.
- max_subpopulation : int, optional, default 1e4
- Instead of computing with a set of cardinality ‘n choose k’, where n is the number of samples and k is the number of subsamples (at least number of features), consider only a stochastic subpopulation of a given maximal size if ‘n choose k’ is larger than max_subpopulation. For other than small problem sizes this parameter will determine memory usage and runtime if n_subsamples is not changed.
- n_subsamples : int, optional, default None
- Number of samples to calculate the parameters. This is at least the number of features (plus 1 if fit_intercept=True) and the number of samples as a maximum. A lower number leads to a higher breakdown point and a low efficiency while a high number leads to a low breakdown point and a high efficiency. If None, take the minimum number of subsamples leading to maximal robustness. If n_subsamples is set to n_samples, Theil-Sen is identical to least squares.
- max_iter : int, optional, default 300
- Maximum number of iterations for the calculation of spatial median.
- tol : float, optional, default 1.e-3
- Tolerance when calculating spatial median.
- random_state : int, RandomState instance or None, optional, default None
- A random number generator instance to define the state of the random permutations generator. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.
- n_jobs : integer, optional, default 1
- Number of CPUs to use during the cross validation. If
-1
, use all the CPUs. - verbose : boolean, optional, default False
- Verbose mode when fitting the model.
- coef_ : array, shape = (n_features)
- Coefficients of the regression model (median of distribution).
- intercept_ : float
- Estimated intercept of regression model.
- breakdown_ : float
- Approximated breakdown point.
- n_iter_ : int
- Number of iterations needed for the spatial median.
- n_subpopulation_ : int
- Number of combinations taken into account from ‘n choose k’, where n is the number of samples and k is the number of subsamples.
- Theil-Sen Estimators in a Multiple Linear Regression Model, 2009 Xin Dang, Hanxiang Peng, Xueqin Wang and Heping Zhang http://home.olemiss.edu/~xdang/papers/MTSE.pdf
-
fit
(X, y)[source]¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Fit linear model.
- X : numpy array of shape [n_samples, n_features]
- Training data
- y : numpy array of shape [n_samples]
- Target values
self : returns an instance of self.
- A parameter
-
predict
(X)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Predict using the linear model
- X : {array-like, sparse matrix}, shape = (n_samples, n_features)
- Samples.
- C : array, shape = (n_samples,)
- Returns predicted values.
- A parameter
-
score
(X, y, sample_weight=None)¶ Note
The documentation following is of the class wrapped by this class. There are some changes, in particular:
- A parameter
X
denotes apandas.DataFrame
. - A parameter
y
denotes apandas.Series
.
Returns the coefficient of determination R^2 of the prediction.
The coefficient R^2 is defined as (1 - u/v), where u is the residual sum of squares ((y_true - y_pred) ** 2).sum() and v is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a R^2 score of 0.0.
- X : array-like, shape = (n_samples, n_features)
- Test samples.
- y : array-like, shape = (n_samples) or (n_samples, n_outputs)
- True values for X.
- sample_weight : array-like, shape = [n_samples], optional
- Sample weights.
- score : float
- R^2 of self.predict(X) wrt. y.
- A parameter
- A parameter