wvu is a simple set of utilities for doing and teaching data science and machine learning methods. They are not replacements for the standard methods in sklearn.

importnumpy.randomimportpandasimportwvu.utilwvu.__version__

'0.3.6'

Illustration of cross-method plan.

wvu.util.mk_cross_plan(10, 2)

[{'train': [2, 3, 7, 8, 9], 'test': [0, 1, 4, 5, 6]},
{'train': [0, 1, 4, 5, 6], 'test': [2, 3, 7, 8, 9]}]

Plotting example

help(wvu.util.plot_roc)

Help on function plot_roc in module wvu.util:
plot_roc(prediction, istrue, title='Receiver operating characteristic plot', *, truth_target=True, ideal_line_color=None, extra_points=None, show=True)
Plot a ROC curve of numeric prediction against boolean istrue.
:param prediction: column of numeric predictions
:param istrue: column of items to predict
:param title: plot title
:param truth_target: value to consider target or true.
:param ideal_line_color: if not None, color of ideal line
:param extra_points: data frame of additional point to annotate graph, columns fpr, tpr, label
:param show: logical, if True call matplotlib.pyplot.show()
:return: calculated area under the curve, plot produced by call.
Example:
import pandas
import wvpy.util
d = pandas.DataFrame({
'x': [1, 2, 3, 4, 5],
'y': [False, False, True, True, False]
})
wvpy.util.plot_roc(
prediction=d['x'],
istrue=d['y'],
ideal_line_color='lightgrey'
)
wvpy.util.plot_roc(
prediction=d['x'],
istrue=d['y'],
ideal_line_color='lightgrey',
extra_points=pandas.DataFrame({
'tpr': [0, 1],
'fpr': [0, 1],
'label': ['AAA', 'BBB']
})
)

d=pandas.concat([
pandas.DataFrame({
'x': numpy.random.normal(size=1000),
'y': numpy.random.choice([True, False], p=(0.02, 0.98), size=1000, replace=True)}),
pandas.DataFrame({
'x': numpy.random.normal(size=200) +5,
'y': numpy.random.choice([True, False], size=200, replace=True)}),
])

wvu.util.plot_roc(
prediction=d.x,
istrue=d.y,
ideal_line_color="DarkGrey",
title='Example ROC plot')

<Figure size 432x288 with 0 Axes>

0.861085556577737

help(wvu.util.threshold_plot)

Help on function threshold_plot in module wvu.util:
threshold_plot(d: pandas.core.frame.DataFrame, pred_var: str, truth_var: str, truth_target: bool = True, threshold_range: Iterable[float] = (-inf, inf), plotvars: Iterable[str] = ('precision', 'recall'), title: str = 'Measures as a function of threshold', *, show: bool = True) -> None
Produce multiple facet plot relating the performance of using a threshold greater than or equal to
different values at predicting a truth target.
:param d: pandas.DataFrame to plot
:param pred_var: name of column of numeric predictions
:param truth_var: name of column with reference truth
:param truth_target: value considered true
:param threshold_range: x-axis range to plot
:param plotvars: list of metrics to plot, must come from ['threshold', 'count', 'fraction',
'true_positive_rate', 'false_positive_rate', 'true_negative_rate', 'false_negative_rate',
'precision', 'recall', 'sensitivity', 'specificity', 'accuracy']
:param title: title for plot
:param show: logical, if True call matplotlib.pyplot.show()
:return: None, plot produced as a side effect
Example:
import pandas
import wvpy.util
d = pandas.DataFrame({
'x': [1, 2, 3, 4, 5],
'y': [False, False, True, True, False]
})
wvpy.util.threshold_plot(
d,
pred_var='x',
truth_var='y',
plotvars=("sensitivity", "specificity"),
)

wvu.util.threshold_plot(
d,
pred_var='x',
truth_var='y',
plotvars=("sensitivity", "specificity"),
title="example plot"
)

wvu.util.threshold_plot(
d,
pred_var='x',
truth_var='y',
plotvars=("precision", "recall"),
title="example plot"
)

help(wvu.util.gain_curve_plot)

Help on function gain_curve_plot in module wvu.util:
gain_curve_plot(prediction, outcome, title='Gain curve plot', *, show=True)
plot cumulative outcome as a function of prediction order (descending)
:param prediction: vector of numeric predictions
:param outcome: vector of actual values
:param title: plot title
:param show: logical, if True call matplotlib.pyplot.show()
:return: None
Example:
d = pandas.DataFrame({
'x': [.1, .2, .3, .4, .5],
'y': [0, 0, 1, 1, 0]
})
wvpy.util.gain_curve_plot(
prediction=d['x'],
outcome=d['y'],
)

wvu.util.gain_curve_plot(
prediction=d['x'],
outcome=d['y'],
title="gain curve plot"
)

wvu.util.lift_curve_plot(
prediction=d['x'],
outcome=d['y'],
title="lift curve plot"
)