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class SimpleLogisticRegression extends ClassifierReal

The SimpleLogisticRegression class supports (binomial) logistic regression. In this case, 'x' is two-dimensional '[1, x_1]'. Fit the parameter vector 'b' in the logistic regression equation

logit (p_y) = b dot x + e = b_0 + b_1 * x_1 + e

where 'e' represents the residuals (the part not explained by the model) and 'y' is now binary.

See also

see.stanford.edu/materials/lsoeldsee263/05-ls.pdf

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  1. SimpleLogisticRegression
  2. ClassifierReal
  3. Classifier
  4. Model
  5. ConfusionFit
  6. Error
  7. QoF
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Instance Constructors

  1. new SimpleLogisticRegression(x: MatriD, y: VectoI, fn_: Strings = Array ("one", "x1"), cn_: Strings = null, hparam: HyperParameter = Classifier.hp)

    x

    the input/design matrix augmented with a first column of ones

    y

    the binary response vector, y_i in {0, 1}

    fn_

    the names for all features/variables

    cn_

    the names for both classes

    hparam

    the hyper-parameters

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. def accuracy: Double

    Compute the accuracy of the classification, i.e., the fraction of correct classifications.

    Compute the accuracy of the classification, i.e., the fraction of correct classifications. Note, the correct classifications 'tp_i' are in the main diagonal of the confusion matrix.

    Definition Classes
    ConfusionFit
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. def calcCorrelation: MatriD

    Calculate the correlation matrix for the feature vectors 'fea'.

    Calculate the correlation matrix for the feature vectors 'fea'. If the correlations are too high, the independence assumption may be dubious.

    Definition Classes
    ClassifierReal
  7. def calcCorrelation2(zrg: Range, xrg: Range): MatriD

    Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2).

    Calculate the correlation matrix for the feature vectors of Z (Level 3) and those of X (level 2). If the correlations are too high, the independence assumption may be dubious.

    zrg

    the range of Z-columns

    xrg

    the range of X-columns

    Definition Classes
    ClassifierReal
  8. def classify(z: VectoD): (Int, String, Double)

    Classify the value of 'y = f(z)' by evaluating the formula 'y = sigmoid (b dot z)'.

    Classify the value of 'y = f(z)' by evaluating the formula 'y = sigmoid (b dot z)'. Return the best class, its name and quality metric

    z

    the new vector to classify

    Definition Classes
    SimpleLogisticRegressionClassifier
  9. def classify(xx: MatriD = x): VectoI

    Classify all of the row vectors in matrix 'xx'.

    Classify all of the row vectors in matrix 'xx'.

    xx

    the row vectors to classify (defaults to x)

    Definition Classes
    ClassifierReal
  10. def classify(z: VectoI): (Int, String, Double)

    Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles.

    Given a new discrete (integer-valued) data vector 'z', determine which class it belongs to, by first converting it to a vector of doubles. Return the best class, its name and its relative probability

    z

    the vector to classify

    Definition Classes
    ClassifierRealClassifier
  11. def clearConfusion(): Unit

    Clear the total cummulative confusion matrix.

    Clear the total cummulative confusion matrix.

    Definition Classes
    ConfusionFit
  12. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native() @HotSpotIntrinsicCandidate()
  13. var cn: Strings
    Attributes
    protected
    Definition Classes
    ClassifierReal
  14. def confusion(yp: VectoI, yy: VectoI = y): MatriI

    Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning the confusion matrix 'cmat', which for 'k = 2' is

    Compare the actual class 'y' vector versus the predicted class 'yp' vector, returning the confusion matrix 'cmat', which for 'k = 2' is

    yp 0 1 ---------- y 0 | tn fp | 1 | fn tp | ----------

    Note: ScalaTion's confusion matrix is Actual × Predicted, but to swap the position of actual 'y' (rows) with predicted 'yp' (columns) simply use 'cmat.t', the transpose of 'cmat'.

    yp

    the precicted class values/labels

    yy

    the actual class values/labels for full (y) or test (y_e) dataset

    Definition Classes
    ConfusionFit
    See also

    www.dataschool.io/simple-guide-to-confusion-matrix-terminology

  15. def contrast(yp: VectoI, yy: VectoI = y): Unit

    Contract the actual class 'yy' vector versus the predicted class 'yp' vector.

    Contract the actual class 'yy' vector versus the predicted class 'yp' vector.

    yp

    the predicted class values/labels

    yy

    the actual class values/labels for full (y) or test (y_e) dataset

    Definition Classes
    ConfusionFit
  16. def crossValidate(nx: Int = 10, show: Boolean = false): Array[Statistic]

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy.

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method slices out instances/rows to form the test dataset.

    nx

    number of folds/crosses and cross-validations (defaults to 10x).

    show

    the show flag (show result from each iteration)

    Definition Classes
    ClassifierRealClassifier
  17. def crossValidateRand(nx: Int = 10, show: Boolean = false): Array[Statistic]

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy.

    Test the accuracy of the classified results by cross-validation, returning the Quality of Fit (QoF) measures such as accuracy. This method randomizes the instances/rows selected for the test dataset.

    nx

    number of folds/crosses and cross-validations (defaults to 10x).

    show

    the show flag (show result from each iteration)

    Definition Classes
    ClassifierRealClassifier
  18. def diagnose(e: VectoD, yy: VectoD, yp: VectoD, w: VectoD = null, ym: Double = noDouble): Unit

    Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses.

    Diagnose the health of the model by computing the Quality of Fit (QoF) measures, from the error/residual vector and the predicted & actual responses. For some models the instances may be weighted.

    e

    the m-dimensional error/residual vector (yy - yp)

    yy

    the actual response vector to use (test/full)

    yp

    the predicted response vector (test/full)

    w

    the weights on the instances (defaults to null)

    ym

    the mean of the actual response vector to use (test/full)

    Definition Classes
    ConfusionFitQoF
    See also

    Regression_WLS

  19. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  20. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  21. def eval(xx: MatriD, yy: VectoD = null): ClassifierReal

    Test the quality of the training with a test dataset and return the fraction of correct classifications.

    Test the quality of the training with a test dataset and return the fraction of correct classifications.

    xx

    the integer-valued test vectors stored as rows of a matrix

    yy

    the classification vector (impl. classes should ignore or default yy to y)

    Definition Classes
    ClassifierRealModel
  22. def f1_measure(p: Double, r: Double): Double

    Compute the F1-measure, i.e., the harmonic mean of the precision and recall.

    Compute the F1-measure, i.e., the harmonic mean of the precision and recall.

    p

    the precision

    r

    the recall

    Definition Classes
    ConfusionFit
  23. def f1v: VectoD

    Compute the micro-F1-measure vector, i.e., the harmonic mean of the precision and recall.

    Compute the micro-F1-measure vector, i.e., the harmonic mean of the precision and recall.

    Definition Classes
    ConfusionFit
  24. def f_(z: Double): String

    Format a double value.

    Format a double value.

    z

    the double value to format

    Definition Classes
    QoF
  25. def featureSelection(TOL: Double = 0.01): Unit

    Perform feature selection on the classifier.

    Perform feature selection on the classifier. Use backward elimination technique, that is, remove the least significant feature, in terms of cross- validation accuracy, in each round.

    TOL

    tolerance indicating negligible accuracy loss when removing features

    Definition Classes
    ClassifierReal
  26. def fit: VectoD

    Return the quality of fit.

    Return the quality of fit. Assumes both 'train' and 'confusion' methods have already been called.

    Definition Classes
    SimpleLogisticRegressionConfusionFitQoF
  27. def fitLabel: Seq[String]

    Return the labels for the fit.

    Return the labels for the fit. Override when necessary.

    Definition Classes
    SimpleLogisticRegressionConfusionFitQoF
  28. def fitLabel_v: Seq[String]

    Return the labels for the Quality of Fit (QoF) measures.

    Return the labels for the Quality of Fit (QoF) measures. Override to add additional QoF measures.

    Definition Classes
    ConfusionFit
  29. def fitMap: Map[String, String]

    Build a map of quality of fit measures (use of LinkedHashMap makes it ordered).

    Build a map of quality of fit measures (use of LinkedHashMap makes it ordered).

    Definition Classes
    QoF
  30. def fitMicroMap: Map[String, VectoD]

    Return the Quality of Fit (QoF) vector micor-measures, i.e., measures for each class.

    Return the Quality of Fit (QoF) vector micor-measures, i.e., measures for each class.

    Definition Classes
    ConfusionFit
  31. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  32. var fn: Strings
    Attributes
    protected
    Definition Classes
    ClassifierReal
  33. val fset: Array[Boolean]

    the set of features to turn on or off.

    the set of features to turn on or off. All features are on by default. Used for feature selection.

    Attributes
    protected
    Definition Classes
    ClassifierReal
  34. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  35. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  36. def help: String

    Return the help string that describes the Quality of Fit (QoF) measures provided by the ConfusionFit class.

    Return the help string that describes the Quality of Fit (QoF) measures provided by the ConfusionFit class. Override to correspond to 'fitLabel'.

    Definition Classes
    ConfusionFitQoF
  37. def hparameter: HyperParameter

    Return the model hyper-parameters (if none, return null).

    Return the model hyper-parameters (if none, return null). Hyper-parameters may be used to regularize parameters or tune the optimizer.

    Definition Classes
    ClassifierRealModel
  38. val index_p_rSq: Int
  39. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  40. def kappa: Double

    Compute Cohen's 'kappa' coefficient that measures agreement between actual 'y' and predicted 'yp' classifications.

    Compute Cohen's 'kappa' coefficient that measures agreement between actual 'y' and predicted 'yp' classifications.

    Definition Classes
    ConfusionFit
    See also

    en.wikipedia.org/wiki/Cohen%27s_kappa

  41. def ll(b: VectoD): Double

    For a given parameter vector 'b', compute '-2 * Log-Likelihood (-2l)'.

    For a given parameter vector 'b', compute '-2 * Log-Likelihood (-2l)'. '-2l' is the standard measure that follows a Chi-Square distribution.

    b

    the parameters to fit

    See also

    www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf

    www.statisticalhorizons.com/wp-content/uploads/Allison.StatComp.pdf

  42. def ll_null(b: VectoD): Double

    For a given parameter vector 'b = [b(0)]', compute '-2 * Log-Likelihood (-2l)'.

    For a given parameter vector 'b = [b(0)]', compute '-2 * Log-Likelihood (-2l)'. '-2l' is the standard measure that follows a Chi-Square distribution.

    b

    the parameters to fit

    See also

    www.stat.cmu.edu/~cshalizi/350/lectures/26/lecture-26.pdf

    www.statisticalhorizons.com/wp-content/uploads/Allison.StatComp.pdf

  43. val m: Int

    the number of data vectors in training-set (# rows)

    the number of data vectors in training-set (# rows)

    Attributes
    protected
    Definition Classes
    ClassifierReal
  44. val md: Double

    the training-set size as a Double

    the training-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierReal
  45. val modelConcept: URI

    An optional reference to an ontological concept

    An optional reference to an ontological concept

    Definition Classes
    Model
  46. def modelName: String

    An optional name for the model (or modeling technique)

    An optional name for the model (or modeling technique)

    Definition Classes
    Model
  47. val n: Int

    the number of features/variables (# columns)

    the number of features/variables (# columns)

    Attributes
    protected
    Definition Classes
    ClassifierReal
  48. val nd: Double

    the feature-set size as a Double

    the feature-set size as a Double

    Attributes
    protected
    Definition Classes
    ClassifierReal
  49. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  50. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  51. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native() @HotSpotIntrinsicCandidate()
  52. def p_r_s(): Unit

    Compute the micro-precision, micro-recall and micro-specificity vectors which have elements for each class i in {0, 1, ...

    Compute the micro-precision, micro-recall and micro-specificity vectors which have elements for each class i in {0, 1, ... k-1}. -------------------------------------------------------------------------- Precision is the fraction classified as true that are actually true. Recall (sensitivity) is the fraction of the actually true that are classified as true. Specificity is the fraction of the actually false that are classified as false. -------------------------------------------------------------------------- Note, for 'k = 2', ordinary precision 'p', recall 'r' and specificity 's' will correspond to the last elements in the 'pv', 'rv' and 'sv' micro vectors.

    Definition Classes
    ConfusionFit
  53. def parameter: VectoD

    Return the vector of coefficient/parameter values.

    Return the vector of coefficient/parameter values.

    Definition Classes
    SimpleLogisticRegressionModel
  54. def pseudo_rSq: Double

    Compute McFaffen's pseudo R-squared.

    Compute McFaffen's pseudo R-squared.

    Definition Classes
    SimpleLogisticRegressionConfusionFit
  55. def report: String

    Return a basic report on the trained model.

    Return a basic report on the trained model.

    Definition Classes
    ClassifierRealModel
  56. def reset(): Unit

    Reset or re-initialize the frequency tables and the probability tables.

    Reset or re-initialize the frequency tables and the probability tables.

    Definition Classes
    SimpleLogisticRegressionClassifier
  57. def setStream(str: Int = 0): Unit

    Set the random number 'stream' to 'str'.

    Set the random number 'stream' to 'str'. This is useful for testing purposes, since a fixed stream will follow the same sequence each time.

    str

    the new fixed random number stream

    Definition Classes
    Classifier
  58. def size: Int

    Return the number of data vectors in training/test-set (# rows).

    Return the number of data vectors in training/test-set (# rows).

    Definition Classes
    ClassifierRealClassifier
  59. val stream: Int

    the random number stream {0, 1, ..., 999} to be used

    the random number stream {0, 1, ..., 999} to be used

    Attributes
    protected
    Definition Classes
    Classifier
  60. def summary(b: VectoD = null, show: Boolean = false): String

    Produce a summary report with diagnostics and the overall quality of fit.

    Produce a summary report with diagnostics and the overall quality of fit.

    b

    the parameters of the model

    show

    flag indicating whether to print the summary

    Definition Classes
    ConfusionFit
  61. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  62. def test(xx: MatriD, yy: VectoI): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    xx

    the real-valued test vectors stored as rows of a matrix

    yy

    the test classification vector, where 'yy_i = class for row i of xx'

    Definition Classes
    ClassifierReal
  63. def test(itest: VectorI): Double

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    Test the quality of the training with a test-set and return the fraction of correct classifications.

    itest

    indices of the instances considered test data

    Definition Classes
    ClassifierRealClassifier
  64. def test(testStart: Int, testEnd: Int): Double

    Test the quality of the training with a test dataset and return the fraction of correct classifications.

    Test the quality of the training with a test dataset and return the fraction of correct classifications. Can be used when the dataset is randomized so that the testing/training part of a dataset corresponds to simple slices of vectors and matrices.

    testStart

    the beginning of test region (inclusive).

    testEnd

    the end of test region (exclusive).

    Definition Classes
    Classifier
  65. def tn_fp_fn_tp(con: MatriI = cmat): (Double, Double, Double, Double)

    Return the confusion matrix for 'k = 2' as a tuple (tn, fp, fn, tp).

    Return the confusion matrix for 'k = 2' as a tuple (tn, fp, fn, tp).

    con

    the confusion matrix (defaults to cmat)

    Definition Classes
    ConfusionFit
  66. def toString(): String
    Definition Classes
    AnyRef → Any
  67. def total_cmat(): MatriI

    Return a copy of the total cummulative confusion matrix 'tcmat' and clear 'tcmat'.

    Return a copy of the total cummulative confusion matrix 'tcmat' and clear 'tcmat'.

    Definition Classes
    ConfusionFit
  68. def train(itest: VectorI): SimpleLogisticRegression

    For the full model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood.

    For the full model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood. Do this by minimizing '-2l'. FIX: Use improved BFGS implementation or IRWLS

    itest

    the indices of test data

    Definition Classes
    SimpleLogisticRegressionClassifier
    See also

    stats.stackexchange.com/questions/81000/calculate-coefficients-in-a-logistic-regression-with-r

    en.wikipedia.org/wiki/Iteratively_reweighted_least_squares

  69. def train(xx: MatriD = null, yy: VectoD = null): Classifier

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the whole dataset is used for training.

    xx

    the data/input matrix (impl. classes should ignore or default xx to x)

    yy

    the response/classification vector (impl. classes should ignore or default yy to y)

    Definition Classes
    ClassifierModel
  70. def train(testStart: Int, testEnd: Int): Classifier

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications.

    Train the classifier by computing the probabilities from a training dataset of data vectors and their classifications. Must be implemented in any extending class. Can be used when the dataset is randomized so that the training part of a dataset corresponds to simple slices of vectors and matrices.

    testStart

    starting index of test region (inclusive) used in cross-validation

    testEnd

    ending index of test region (exclusive) used in cross-validation

    Definition Classes
    Classifier
  71. def train_null(): Unit

    For the null model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood.

    For the null model, train the classifier by fitting the parameter vector (b-vector) in the logistic regression equation using maximum likelihood. Do this by minimizing -2l.

  72. def vc_default: Array[Int]

    Return default values for binary input data (value count 'vc' set to 2).

    Return default values for binary input data (value count 'vc' set to 2). Also may be used for binning into two categories.

    Definition Classes
    ClassifierReal
  73. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  74. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  75. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])

Deprecated Value Members

  1. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
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    @throws(classOf[java.lang.Throwable]) @Deprecated
    Deprecated

Inherited from ClassifierReal

Inherited from Classifier

Inherited from Model

Inherited from ConfusionFit

Inherited from Error

Inherited from QoF

Inherited from AnyRef

Inherited from Any

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