LinearRegression
¶
-
class
ibex.sklearn.linear_model.
LinearRegression
(fit_intercept=True, normalize=False, copy_X=True, n_jobs=1)¶ Bases:
sklearn.linear_model.base.LinearRegression
,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.LinearRegression().fit(iris[features], iris['class']) >>> >>> prd.coef_ sepal length (cm) ... sepal width (cm) ... petal length (cm) ... petal width (cm) ... dtype: float64
Example:
>>> from ibex.sklearn import linear_model as pd_linear_model >>> prd = pd_linear_model.LinearRegression().fit(iris[features], iris[['class', 'class']]) >>> >>> prd.coef_ sepal length (cm) sepal width (cm) petal length (cm) petal width (cm) 0... 0.414988 1.461297 -2.262141 -1.029095 1... 0.416640 -1.600833 0.577658 -1.385538 2... -1.707525 -1.534268 2.470972 2.555382
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.LinearRegression().fit(iris[features], iris[['class', 'class']]) >>> >>> prd.intercept_ sepal length (cm) ... sepal width (cm) ... petal length (cm) ... petal width (cm) ... dtype: float64
Ordinary least squares Linear Regression.
Parameters: - 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 (e.g. data is expected to be already centered).
- normalize (boolean, optional, default False) – This parameter is ignored when
fit_intercept
is set to False. If True, the regressors X will be normalized before regression by subtracting the mean and dividing by the l2-norm. If you wish to standardize, please usesklearn.preprocessing.StandardScaler
before callingfit
on an estimator withnormalize=False
. - copy_X (boolean, optional, default True) – If True, X will be copied; else, it may be overwritten.
- n_jobs (int, optional, default 1) – The number of jobs to use for the computation. If -1 all CPUs are used. This will only provide speedup for n_targets > 1 and sufficient large problems.
-
coef_
¶ array, shape (n_features, ) or (n_targets, n_features) – Estimated coefficients for the linear regression problem. If multiple targets are passed during the fit (y 2D), this is a 2D array of shape (n_targets, n_features), while if only one target is passed, this is a 1D array of length n_features.
-
intercept_
¶ array – Independent term in the linear model.
Notes
From the implementation point of view, this is just plain Ordinary Least Squares (scipy.linalg.lstsq) wrapped as a predictor object.
-
fit
(X, y, sample_weight=None)[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 or sparse matrix of shape [n_samples,n_features]
- Training data
- y : numpy array of shape [n_samples, n_targets]
- Target values. Will be cast to X’s dtype if necessary
- sample_weight : numpy array of shape [n_samples]
Individual weights for each sample
New in version 0.17: parameter sample_weight support to LinearRegression.
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