LinearDiscriminantAnalysis
¶
-
class
ibex.sklearn.discriminant_analysis.
LinearDiscriminantAnalysis
(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001)¶ Bases:
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
,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 numpy as np >>> from sklearn import datasets >>> import pandas as pd >>> >>> iris = datasets.load_iris() >>> features, targets, iris = iris['feature_names'], iris['target_names'], pd.DataFrame( ... np.c_[iris['data'], iris['target']], ... columns=iris['feature_names']+['class']) >>> iris['class'] = iris['class'].map(pd.Series(targets)) >>> >>> iris.head() 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 class 0 setosa 1 setosa 2 setosa 3 setosa 4 setosa
>>> >>> from ibex.sklearn import discriminant_analysis as pd_discriminant_analysis >>> >>> clf = pd_discriminant_analysis.LinearDiscriminantAnalysis().fit(iris[features], iris['class']) >>> >>> clf.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 numpy as np >>> from sklearn import datasets >>> import pandas as pd >>> >>> iris = datasets.load_iris() >>> features, targets, iris = iris['feature_names'], iris['target_names'], pd.DataFrame( ... np.c_[iris['data'], iris['target']], ... columns=iris['feature_names']+['class']) >>> iris['class'] = iris['class'].map(pd.Series(targets)) >>> >>> iris.head() 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 class 0 setosa 1 setosa 2 setosa 3 setosa 4 setosa
>>> from ibex.sklearn import discriminant_analysis as pd_discriminant_analysis >>> >>> clf = pd_discriminant_analysis.LinearDiscriminantAnalysis().fit(iris[features], iris['class']) >>> >>> clf.intercept_ sepal length (cm) ... sepal width (cm) ... petal length (cm) ... petal width (cm) ... dtype: float64
Linear Discriminant Analysis
A classifier with a linear decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule.
The model fits a Gaussian density to each class, assuming that all classes share the same covariance matrix.
The fitted model can also be used to reduce the dimensionality of the input by projecting it to the most discriminative directions.
New in version 0.17: LinearDiscriminantAnalysis.
Read more in the User Guide.
- solver : string, optional
- Solver to use, possible values:
- ‘svd’: Singular value decomposition (default). Does not compute the covariance matrix, therefore this solver is recommended for data with a large number of features.
- ‘lsqr’: Least squares solution, can be combined with shrinkage.
- ‘eigen’: Eigenvalue decomposition, can be combined with shrinkage.
- shrinkage : string or float, optional
- Shrinkage parameter, possible values:
- None: no shrinkage (default).
- ‘auto’: automatic shrinkage using the Ledoit-Wolf lemma.
- float between 0 and 1: fixed shrinkage parameter.
Note that shrinkage works only with ‘lsqr’ and ‘eigen’ solvers.
- priors : array, optional, shape (n_classes,)
- Class priors.
- n_components : int, optional
- Number of components (< n_classes - 1) for dimensionality reduction.
- store_covariance : bool, optional
Additionally compute class covariance matrix (default False), used only in ‘svd’ solver.
New in version 0.17.
- tol : float, optional, (default 1.0e-4)
Threshold used for rank estimation in SVD solver.
New in version 0.17.
- coef_ : array, shape (n_features,) or (n_classes, n_features)
- Weight vector(s).
- intercept_ : array, shape (n_features,)
- Intercept term.
- covariance_ : array-like, shape (n_features, n_features)
- Covariance matrix (shared by all classes).
- explained_variance_ratio_ : array, shape (n_components,)
- Percentage of variance explained by each of the selected components.
If
n_components
is not set then all components are stored and the sum of explained variances is equal to 1.0. Only available when eigen or svd solver is used. - means_ : array-like, shape (n_classes, n_features)
- Class means.
- priors_ : array-like, shape (n_classes,)
- Class priors (sum to 1).
- scalings_ : array-like, shape (rank, n_classes - 1)
- Scaling of the features in the space spanned by the class centroids.
- xbar_ : array-like, shape (n_features,)
- Overall mean.
- classes_ : array-like, shape (n_classes,)
- Unique class labels.
- sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis: Quadratic
- Discriminant Analysis
The default solver is ‘svd’. It can perform both classification and transform, and it does not rely on the calculation of the covariance matrix. This can be an advantage in situations where the number of features is large. However, the ‘svd’ solver cannot be used with shrinkage.
The ‘lsqr’ solver is an efficient algorithm that only works for classification. It supports shrinkage.
The ‘eigen’ solver is based on the optimization of the between class scatter to within class scatter ratio. It can be used for both classification and transform, and it supports shrinkage. However, the ‘eigen’ solver needs to compute the covariance matrix, so it might not be suitable for situations with a high number of features.
>>> import numpy as np >>> from sklearn.discriminant_analysis import LinearDiscriminantAnalysis >>> X = np.array([[-1, -1], [-2, -1], [-3, -2], [1, 1], [2, 1], [3, 2]]) >>> y = np.array([1, 1, 1, 2, 2, 2]) >>> clf = LinearDiscriminantAnalysis() >>> clf.fit(X, y) LinearDiscriminantAnalysis(n_components=None, priors=None, shrinkage=None, solver='svd', store_covariance=False, tol=0.0001) >>> print(clf.predict([[-0.8, -1]])) [1]
-
decision_function
(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 confidence scores for samples.
The confidence score for a sample is the signed distance of that sample to the hyperplane.
- X : {array-like, sparse matrix}, shape = (n_samples, n_features)
- Samples.
- 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.
- A parameter
-
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 LinearDiscriminantAnalysis model according to the given
training data and parameters.
Changed in version 0.19: store_covariance has been moved to main constructor.
Changed in version 0.19: tol has been moved to main constructor.
- X : array-like, shape (n_samples, n_features)
- Training data.
- y : array, shape (n_samples,)
- Target values.
- A parameter
-
fit_transform
(X, y=None, **fit_params)¶ 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 to data, then transform it.
Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.
- X : numpy array of shape [n_samples, n_features]
- Training set.
- y : numpy array of shape [n_samples]
- Target values.
- X_new : numpy array of shape [n_samples, n_features_new]
- Transformed array.
- 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 class labels for samples in X.
- X : {array-like, sparse matrix}, shape = [n_samples, n_features]
- Samples.
- C : array, shape = [n_samples]
- Predicted class label per sample.
- A parameter
-
predict_log_proba
(X)[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
.
Estimate log probability.
- X : array-like, shape (n_samples, n_features)
- Input data.
- C : array, shape (n_samples, n_classes)
- Estimated log probabilities.
- A parameter
-
predict_proba
(X)[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
.
Estimate probability.
- X : array-like, shape (n_samples, n_features)
- Input data.
- C : array, shape (n_samples, n_classes)
- Estimated probabilities.
- 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 mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
- X : array-like, shape = (n_samples, n_features)
- Test samples.
- y : array-like, shape = (n_samples) or (n_samples, n_outputs)
- True labels for X.
- sample_weight : array-like, shape = [n_samples], optional
- Sample weights.
- score : float
- Mean accuracy of self.predict(X) wrt. y.
- A parameter
-
transform
(X)[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
.
Project data to maximize class separation.
- X : array-like, shape (n_samples, n_features)
- Input data.
- X_new : array, shape (n_samples, n_components)
- Transformed data.
- A parameter
- A parameter