module ml.roc#

Inheritance diagram of mlstatpy.ml.roc

Short summary#

module mlstatpy.ml.roc

About ROC.

source on GitHub

Classes#

class

truncated documentation

ROC

Helper to draw a ROC curve.

Properties#

property

truncated documentation

Data

Returns the underlying dataframe.

Methods#

method

truncated documentation

__init__

Initialisation with a dataframe and two or three columns:

__len__

usual

__repr__

Shows first elements, precision rate.

__str__

Shows first elements, precision rate.

auc

Computes the area under the curve (:epkg:`AUC`).

auc_interval

Determines a confidence interval for the :epkg:`AUC` with bootstrap.

compute_roc_curve

Computes a ROC curve with nb points avec nb, if nb == -1, there are as many as points as the data contains, …

confusion

Computes the confusion matrix for a specific score or all if score is None.

plot

Plots a ROC curve.

precision

Computes the precision.

random_cloud

Resamples among the data.

roc_intersect

The ROC curve is defined by a set of points. This function interpolates those points to determine …

roc_intersect_interval

Computes a confidence interval for the value returned by roc_intersect().

Documentation#

About ROC.

source on GitHub

class mlstatpy.ml.roc.ROC(y_true=None, y_score=None, sample_weight=None, df=None)#

Bases : object

Helper to draw a ROC curve.

source on GitHub

Initialisation with a dataframe and two or three columns:

  • column 1: score (y_score)

  • column 2: expected answer (boolean) (y_true)

  • column 3: weight (optional) (sample_weight)

Paramètres:

  • y_true – if df is None, y_true, y_score, sample_weight must be filled, y_true is whether or None the answer is true. y_true means the prediction is right.

  • y_score – score prediction

  • sample_weight – weights

  • df – dataframe or array or list, it must contains 2 or 3 columns always in the same order

source on GitHub

class CurveType(value)#

Bases : Enum

Curve types:

  • PROBSCORE: 1 - False Positive / True Positive

  • ERRPREC: error / recall

  • RECPREC: precision / recall

  • ROC: False Positive / True Positive

  • SKROC: False Positive / True Positive (scikit-learn)

source on GitHub

property Data#

Returns the underlying dataframe.

source on GitHub

__init__(y_true=None, y_score=None, sample_weight=None, df=None)#

Initialisation with a dataframe and two or three columns:

  • column 1: score (y_score)

  • column 2: expected answer (boolean) (y_true)

  • column 3: weight (optional) (sample_weight)

Paramètres:

  • y_true – if df is None, y_true, y_score, sample_weight must be filled, y_true is whether or None the answer is true. y_true means the prediction is right.

  • y_score – score prediction

  • sample_weight – weights

  • df – dataframe or array or list, it must contains 2 or 3 columns always in the same order

source on GitHub

__len__()#

usual

source on GitHub

__repr__()#

Shows first elements, precision rate.

source on GitHub

__str__()#

Shows first elements, precision rate.

source on GitHub

auc(cloud=None)#

Computes the area under the curve (:epkg:`AUC`).

Paramètres:

cloud – data or None to use self.data, the function assumes the data is sorted.

Renvoie:

AUC

The first column is the label, the second one is the score, the third one is the weight.

source on GitHub

auc_interval(bootstrap=10, alpha=0.95)#

Determines a confidence interval for the :epkg:`AUC` with bootstrap.

Paramètres:

  • bootstrap – number of random estimation

  • alpha – define the confidence interval

Renvoie:

dictionary of values

source on GitHub

compute_roc_curve(nb=100, curve=CurveType.ROC, bootstrap=False)#

Computes a ROC curve with nb points avec nb, if nb == -1, there are as many as points as the data contains, if bootstrap == True, it draws random number to create confidence interval based on bootstrap method.

Paramètres:

  • nb – number of points for the curve

  • curve – see CurveType

  • boostrap – builds the curve after resampling

Renvoie:

DataFrame (metrics and threshold)

If curve is SKROC, the parameter nb is not taken into account. It should be set to 0.

source on GitHub

confusion(score=None, nb=10, curve=CurveType.ROC, bootstrap=False)#

Computes the confusion matrix for a specific score or all if score is None.

Paramètres:

  • score – score or None.

  • nb – number of scores (if score is None)

  • curve – see CurveType

  • boostrap – builds the curve after resampling

Renvoie:

One row if score is precised, many roww is score is None

source on GitHub

plot(nb=100, curve=CurveType.ROC, bootstrap=0, ax=None, thresholds=False, **kwargs)#

Plots a ROC curve.

Paramètres:

  • nb – number of points

  • curve – see CurveType

  • boostrap – number of curves for the boostrap (0 for None)

  • ax – axis

  • thresholds – use thresholds for the X axis

  • kwargs – sent to pandas.plot

Renvoie:

ax

source on GitHub

precision()#

Computes the precision.

source on GitHub

random_cloud()#

Resamples among the data.

Renvoie:

DataFrame

source on GitHub

roc_intersect(roc, x)#

The ROC curve is defined by a set of points. This function interpolates those points to determine y for any x.

Paramètres:

  • roc – ROC curve

  • x – x

Renvoie:

y

source on GitHub

roc_intersect_interval(x, nb, curve=CurveType.ROC, bootstrap=10, alpha=0.05)#

Computes a confidence interval for the value returned by roc_intersect.

Paramètres:

  • roc – ROC curve

  • x – x

  • curve – see CurveType

Renvoie:

dictionary

source on GitHub