weka.plot package¶
Submodules¶
weka.plot.classifiers module¶
- weka.plot.classifiers.generate_thresholdcurve_data(evaluation, class_index)¶
Generates the threshold curve data from the evaluation object’s predictions.
Parameters: - evaluation (Evaluation) – the evaluation to obtain the predictions from
- class_index (int) – the 0-based index of the class-label to create the plot for
Returns: the generated threshold curve data
Return type: Instances
- weka.plot.classifiers.get_auc(data)¶
Calculates the area under the ROC curve (AUC).
Parameters: data (Instances) – the threshold curve data Returns: the area Return type: float
- weka.plot.classifiers.get_prc(data)¶
Calculates the area under the precision recall curve (PRC).
Parameters: data (Instances) – the threshold curve data Returns: the area Return type: float
- weka.plot.classifiers.get_thresholdcurve_data(data, xname, yname)¶
Retrieves x and y columns from of the data generated by the weka.classifiers.evaluation.ThresholdCurve class.
Parameters: - data (Instances) – the threshold curve data
- xname (str) – the name of the X column
- yname (str) – the name of the Y column
Returns: tuple of x and y arrays
Return type: tuple
- weka.plot.classifiers.plot_classifier_errors(predictions, absolute=True, max_relative_size=50, absolute_size=50, title=None, outfile=None, wait=True)¶
Plots the classifers for the given list of predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
Parameters: - predictions (list) – the predictions to plot
- absolute (bool) – whether to use absolute errors as size or relative ones
- max_relative_size (int) – the maximum size in point in case of relative mode
- absolute_size (int) – the size in point in case of absolute mode
- title (str) – an optional title
- outfile (str) – the output file, ignored if None
- wait (bool) – whether to wait for the user to close the plot
- weka.plot.classifiers.plot_learning_curve(classifiers, train, test=None, increments=100, metric='percent_correct', title='Learning curve', label_template='[#] @ $', key_loc='lower right', outfile=None, wait=True)¶
Plots a learning curve.
Parameters: - classifiers (list of Classifier) – list of Classifier template objects
- train (Instances) – dataset to use for the building the classifier, used for evaluating it test set None
- test (list or Instances) – optional dataset (or list of datasets) to use for the testing the built classifiers
- increments (float) – the increments (>= 1: # of instances, <1: percentage of dataset)
- metric (str) – the name of the numeric metric to plot (Evaluation.<metric>)
- title (str) – the title for the plot
- label_template (str) – the template for the label in the plot (#: 1-based index of classifier, @: full classname, !: simple classname, $: options, *: 1-based index of test set)
- key_loc (str) – the location string for the key
- outfile (str) – the output file, ignored if None
- wait (bool) – whether to wait for the user to close the plot
- weka.plot.classifiers.plot_prc(evaluation, class_index=None, title=None, key_loc='lower center', outfile=None, wait=True)¶
Plots the PRC (precision recall) curve for the given predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
Parameters: - evaluation (Evaluation) – the evaluation to obtain the predictions from
- class_index (list) – the list of 0-based indices of the class-labels to create the plot for
- title (str) – an optional title
- key_loc (str) – the location string for the key
- outfile (str) – the output file, ignored if None
- wait (bool) – whether to wait for the user to close the plot
- weka.plot.classifiers.plot_roc(evaluation, class_index=None, title=None, key_loc='lower right', outfile=None, wait=True)¶
Plots the ROC (receiver operator characteristics) curve for the given predictions.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
Parameters: - evaluation (Evaluation) – the evaluation to obtain the predictions from
- class_index (list) – the list of 0-based indices of the class-labels to create the plot for
- title (str) – an optional title
- key_loc (str) – the position string for the key
- outfile (str) – the output file, ignored if None
- wait (bool) – whether to wait for the user to close the plot
weka.plot.clusterers module¶
- weka.plot.clusterers.plot_cluster_assignments(evl, data, atts=None, inst_no=False, size=10, title=None, outfile=None, wait=True)¶
Plots the cluster assignments against the specified attributes.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
Parameters: - evl (ClusterEvaluation) – the cluster evaluation to obtain the cluster assignments from
- data (Instances) – the dataset the clusterer was evaluated against
- atts (list) – the list of attribute indices to plot, None for all
- inst_no (bool) – whether to include a fake attribute with the instance number
- size (int) – the size of the circles in point
- title (str) – an optional title
- outfile (str) – the (optional) file to save the generated plot to. The extension determines the file format.
- wait (bool) – whether to wait for the user to close the plot
weka.plot.dataset module¶
- weka.plot.dataset.line_plot(data, atts=None, percent=100.0, seed=1, title=None, outfile=None, wait=True)¶
Uses the internal format to plot the dataset, one line per instance.
Parameters: - data (Instances) – the dataset
- atts (list) – the list of 0-based attribute indices of attributes to plot
- percent (float) – the percentage of the dataset to use for plotting
- seed (int) – the seed value to use for subsampling
- title (str) – an optional title
- outfile (str) – the (optional) file to save the generated plot to. The extension determines the file format.
- wait (bool) – whether to wait for the user to close the plot
- weka.plot.dataset.matrix_plot(data, percent=100.0, seed=1, size=10, title=None, outfile=None, wait=True)¶
Plots all attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
Parameters: - data (Instances) – the dataset
- percent (float) – the percentage of the dataset to use for plotting
- seed (int) – the seed value to use for subsampling
- size (int) – the size of the circles in point
- title (str) – an optional title
- outfile (str) – the (optional) file to save the generated plot to. The extension determines the file format.
- wait (bool) – whether to wait for the user to close the plot
- weka.plot.dataset.scatter_plot(data, index_x, index_y, percent=100.0, seed=1, size=50, title=None, outfile=None, wait=True)¶
Plots two attributes against each other.
TODO: click events http://matplotlib.org/examples/event_handling/data_browser.html
Parameters: - data (Instances) – the dataset
- index_x (int) – the 0-based index of the attribute on the x axis
- index_y (int) – the 0-based index of the attribute on the y axis
- percent (float) – the percentage of the dataset to use for plotting
- seed (int) – the seed value to use for subsampling
- size (int) – the size of the circles in point
- title (str) – an optional title
- outfile (str) – the (optional) file to save the generated plot to. The extension determines the file format.
- wait (bool) – whether to wait for the user to close the plot
weka.plot.experiments module¶
- weka.plot.experiments.plot_experiment(mat, title='Experiment', axes_swapped=False, measure='Statistic', show_stdev=False, key_loc='lower right', outfile=None, wait=True)¶
Plots the results from an experiment.
Parameters: - mat (ResultMatrix) – the result matrix to plot
- title (str) – the title for the experiment
- axes_swapped (bool) – whether the axes whether swapped (“sets x cls” or “cls x sets”)
- measure (str) – the measure that is being displayed
- show_stdev (bool) – whether to show the standard deviation as error bar
- key_loc (str) – the location string for the key
- outfile (str) – the output file, ignored if None
- wait (bool) – whether to wait for the user to close the plot
