Bases: weka.core.classes.OptionHandler
Wrapper class for associators.
Builds the associator with the data.
Parameters: | data (Instances) – the data to train the associator with |
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Returns the capabilities of the associator.
Returns: | the capabilities |
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Return type: | Capabilities |
Creates a copy of the clusterer.
Parameters: | associator (Associator) – the associator to copy |
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Returns: | the copy of the associator |
Return type: | Associator |
Runs a associator from the command-line. Calls JVM start/stop automatically. Use -h to see all options.
Bases: weka.core.classes.OptionHandler
Wrapper class for attribute selection evaluation algorithm.
Builds the evaluator with the data.
Parameters: | data (Instances) – the data to use |
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Returns the capabilities of the classifier.
Returns: | the capabilities |
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Return type: | Capabilities |
Post-processes the evaluator with the selected attribute indices.
Parameters: | indices (ndarray) – the attribute indices list to use |
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Returns: | the processed indices |
Return type: | ndarray |
Bases: weka.core.classes.OptionHandler
Wrapper class for attribute selection search algorithm.
Performs the search and returns the indices of the selected attributes.
Parameters: |
|
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Returns: | the selected attributes (0-based indices) |
Return type: | ndarray |
Bases: weka.core.classes.JavaObject
Performs attribute selection using search and evaluation algorithms.
Performs attribute selection using the given attribute evaluator and options.
Parameters: |
|
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Returns: | the results string |
Return type: | str |
Sets whether to perform cross-validation.
Parameters: | crossvalidation (bool) – whether to perform cross-validation |
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Generates a results string from the last cross-validation attribute selection.
Returns: | the results string |
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Return type: | str |
Sets the evaluator to use.
Parameters: | evaluator (ASEvaluation) – the evaluator to use. |
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Sets the number of folds to use for cross-validation.
Parameters: | folds (int) – the number of folds |
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Returns the number of attributes that were selected.
Returns: | the number of attributes |
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Return type: | int |
Returns the matrix of ranked attributes from the last run.
Returns: | the Numpy matrix |
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Return type: | ndarray |
Sets whether to perform a ranking, if possible.
Parameters: | ranking (bool) – whether to perform a ranking |
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Reduces the dimensionality of the provided Instance or Instances object.
Parameters: | data (Instances) – the data to process |
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Returns: | the reduced dataset |
Return type: | Instances |
Generates a results string from the last attribute selection.
Returns: | the results string |
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Return type: | str |
Sets the search algorithm to use.
Parameters: | search (ASSearch) – the search algorithm |
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Sets the seed for cross-validation.
Parameters: | seed (int) – the seed value |
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Performs attribute selection on the given dataset.
Parameters: | instances (Instances) – the data to process |
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Performs attribute selection on the given cross-validation split.
Parameters: | instances (Instances) – the data to process |
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Returns the selected attributes from the last run.
Returns: | the Numpy array of 0-based indices |
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Return type: | ndarray |
Runs attribute selection from the command-line. Calls JVM start/stop automatically. Use -h to see all options.
Bases: weka.core.classes.OptionHandler
Wrapper class for classifiers.
Returns the batch size, in case this classifier is a batch predictor.
Returns: | the batch size, None if not a batch predictor |
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Return type: | str |
Builds the classifier with the data.
Parameters: | data (Instances) – the data to train the classifier with |
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Returns the capabilities of the classifier.
Returns: | the capabilities |
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Return type: | Capabilities |
Peforms a prediction.
Parameters: | inst (Instance) – the Instance to get a prediction for |
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Returns: | the classification (either regression value or 0-based label index) |
Return type: | float |
Peforms a prediction, returning the class distribution.
Parameters: | inst (Instance) – the Instance to get the class distribution for |
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Returns: | the class distribution array |
Return type: | ndarray |
Peforms predictions, returning the class distributions.
Parameters: | data (Instances) – the Instances to get the class distributions for |
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Returns: | the class distribution matrix, None if not a batch predictor |
Return type: | ndarray |
Returns the graph if classifier implements weka.core.Drawable, otherwise None.
Returns: | the generated graph string |
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Return type: | str |
Returns the graph type if classifier implements weka.core.Drawable, otherwise -1.
Returns: | the type |
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Return type: | int |
Returns whether the classifier implements a more efficient batch prediction.
Returns: | True if a more efficient batch prediction is implemented, always False if not batch predictor |
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Return type: | bool |
Creates a copy of the classifier.
Parameters: | classifier (Classifier) – the classifier to copy |
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Returns: | the copy of the classifier |
Return type: | Classifier |
Updates the classifier with the instance.
Parameters: | inst (Instance) – the Instance to update the classifier with |
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Bases: weka.core.classes.JavaObject
Class for storing and manipulating a misclassification cost matrix. The element at position i,j in the matrix is the penalty for classifying an instance of class j as class i. Cost values can be fixed or computed on a per-instance basis (cost sensitive evaluation only) from the value of an attribute or an expression involving attribute(s).
Applies the cost matrix to the data.
Parameters: |
|
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Calculates the expected misclassification cost for each possible class value, given class probability estimates.
Parameters: | class_probs (ndarray) – the class probabilities |
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Returns: | the calculated costs |
Return type: | ndarray |
Returns the JB_Object at the specified location.
Parameters: |
|
---|---|
Returns: | the object in that cell |
Return type: | JB_Object |
Returns the value at the specified location.
Parameters: |
|
---|---|
Returns: | the value in that cell |
Return type: | float |
Gets the maximum cost for a particular class value.
Parameters: |
|
---|---|
Returns: | the cost |
Return type: | float |
Initializes the matrix.
Normalizes the matrix.
Returns the number of columns.
Returns: | the number of columns |
---|---|
Return type: | int |
Returns the number of rows.
Returns: | the number of rows |
---|---|
Return type: | int |
Parses the costmatrix definition in matlab format and returns a matrix.
Parameters: | matlab (str) – the matlab matrix string, eg [1 2; 3 4]. |
---|---|
Returns: | the generated matrix |
Return type: | CostMatrix |
Sets the JB_Object at the specified location. Automatically unwraps JavaObject.
Parameters: |
|
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Sets the float value at the specified location.
Parameters: |
|
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Returns the number of rows/columns.
Returns: | the number of rows/columns |
---|---|
Return type: | int |
Returns the matrix in Matlab format.
Returns: | the matrix as Matlab formatted string |
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Return type: | str |
Bases: weka.core.classes.JavaObject
Evaluation class for classifiers.
Returns the area under precision recall curve.
Parameters: | class_index (int) – the 0-based index of the class label |
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Returns: | the area |
Return type: | float |
Returns the area under receiver operators characteristics curve.
Parameters: | class_index (int) – the 0-based index of the class label |
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Returns: | the area |
Return type: | float |
Returns the average cost.
Returns: | the cost |
---|---|
Return type: | float |
Generates the class details.
Parameters: | title (str) – optional title |
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Returns: | the details |
Return type: | str |
Returns the class priors.
Returns: | the priors |
---|---|
Return type: | ndarray |
Returns the confusion matrix.
Returns: | the matrix |
---|---|
Return type: | ndarray |
Returns the correct count (nominal classes).
Returns: | the count |
---|---|
Return type: | float |
Returns the correlation coefficient (numeric classes).
Returns: | the coefficient |
---|---|
Return type: | float |
Returns the coverage of the test cases by the predicted regions at the confidence level specified when evaluation was performed.
Returns: | the coverage |
---|---|
Return type: | float |
Crossvalidates the model using the specified data, number of folds and random number generator wrapper.
Parameters: |
|
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Returns whether to discard predictions (saves memory).
Returns: | True if to discard |
---|---|
Return type: | bool |
Returns the error rate (numeric classes).
Returns: | the rate |
---|---|
Return type: | float |
Evaluates the classifier with the given options.
Parameters: |
|
---|---|
Returns: | the evaluation string |
Return type: | str |
Splits the data into train and test, builds the classifier with the training data and evaluates it against the test set.
Parameters: |
|
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Returns the f measure.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the measure |
Return type: | float |
Returns the false negative rate.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the rate |
Return type: | float |
Returns the false positive rate.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the rate |
Return type: | float |
Returns the header format.
Returns: | the header format |
---|---|
Return type: | Instances |
Returns the incorrect count (nominal classes).
Returns: | the count |
---|---|
Return type: | float |
Returns kappa.
Returns: | kappa |
---|---|
Return type: | float |
Returns KB information.
Returns: | the information |
---|---|
Return type: | float |
Returns KB mean information.
Returns: | the information |
---|---|
Return type: | float |
Returns KB relative information.
Returns: | the information |
---|---|
Return type: | float |
Generates the confusion matrix.
Parameters: | title (str) – optional title |
---|---|
Returns: | the matrix |
Return type: | str |
Returns the Matthews correlation coefficient (nominal classes).
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the coefficient |
Return type: | float |
Returns the mean absolute error.
Returns: | the error |
---|---|
Return type: | float |
Returns the mean prior absolute error.
Returns: | the error |
---|---|
Return type: | float |
Returns the number of false negatives.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the count |
Return type: | float |
Returns the number of false positives.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the count |
Return type: | float |
Returns the number of instances that had a known class value.
Returns: | the number of instances |
---|---|
Return type: | float |
Returns the number of true negatives.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the count |
Return type: | float |
Returns the number of true positives.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the count |
Return type: | float |
Returns the percent correct (nominal classes).
Returns: | the percentage |
---|---|
Return type: | float |
Returns the percent incorrect (nominal classes).
Returns: | the percentage |
---|---|
Return type: | float |
Returns the percent unclassified.
Returns: | the percentage |
---|---|
Return type: | float |
Returns the precision.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the precision |
Return type: | float |
Returns the predictions.
Returns: | the predictions. None if not available |
---|---|
Return type: | list |
Returns the recall.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the recall |
Return type: | float |
Returns the relative absolute error.
Returns: | the error |
---|---|
Return type: | float |
Returns the root mean prior squared error.
Returns: | the error |
---|---|
Return type: | float |
Returns the root mean squared error.
Returns: | the error |
---|---|
Return type: | float |
Returns the root relative squared error.
Returns: | the error |
---|---|
Return type: | float |
Returns the total SF, which is the null model entropy minus the scheme entropy.
Returns: | the gain |
---|---|
Return type: | float |
Returns the SF per instance, which is the null model entropy minus the scheme entropy, per instance.
Returns: | the gain |
---|---|
Return type: | float |
Returns the entropy per instance for the null model.
Returns: | the entropy |
---|---|
Return type: | float |
Returns the entropy per instance for the scheme.
Returns: | the entropy |
---|---|
Return type: | float |
Returns the average size of the predicted regions, relative to the range of the target in the training data, at the confidence level specified when evaluation was performed.
:return:the size of the regions :rtype: float
Generates a summary.
Parameters: |
|
---|---|
Returns: | the summary |
Return type: | str |
Evaluates the built model using the specified test data and returns the classifications.
Parameters: |
|
---|---|
Returns: | the classifications |
Return type: | ndarray |
Evaluates the built model using the specified test instance and returns the classification.
Parameters: |
|
---|---|
Returns: | the classification |
Return type: | float |
Returns the total cost.
Returns: | the cost |
---|---|
Return type: | float |
Returns the true negative rate.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the rate |
Return type: | float |
Returns the true positive rate.
Parameters: | class_index (int) – the 0-based index of the class label |
---|---|
Returns: | the rate |
Return type: | float |
Returns the unclassified count.
Returns: | the count |
---|---|
Return type: | float |
Returns the unweighted macro-averaged F-measure.
Returns: | the measure |
---|---|
Return type: | float |
Returns the unweighted micro-averaged F-measure.
Returns: | the measure |
---|---|
Return type: | float |
Returns the weighted area under precision recall curve.
Returns: | the weighted area |
---|---|
Return type: | float |
Returns the weighted area under receiver operator characteristic curve.
Returns: | the weighted area |
---|---|
Return type: | float |
Returns the weighted f measure.
Returns: | the measure |
---|---|
Return type: | float |
Returns the weighted false negative rate.
Returns: | the rate |
---|---|
Return type: | float |
Returns the weighted false positive rate.
Returns: | the rate |
---|---|
Return type: | float |
Returns the weighted Matthews correlation (nominal classes).
Returns: | the correlation |
---|---|
Return type: | float |
Returns the weighted precision.
Returns: | the precision |
---|---|
Return type: | float |
Returns the weighted recall.
Returns: | the recall |
---|---|
Return type: | float |
Returns the weighted true negative rate.
Returns: | the rate |
---|---|
Return type: | float |
Returns the weighted true positive rate.
Returns: | the rate |
---|---|
Return type: | float |
Bases: weka.classifiers.SingleClassifierEnhancer
Wrapper class for the filtered classifier.
Returns the filter.
Returns: | the filter in use |
---|---|
Return type: | Filter |
Bases: weka.classifiers.SingleClassifierEnhancer
Wrapper class for the GridSearch meta-classifier.
Returns the best classifier setup found during the th search.
Returns: | the best classifier setup |
---|---|
Return type: | Classifier |
Returns the currently set statistic used for evaluation.
Returns: | the statistic |
---|---|
Return type: | SelectedTag |
Returns a dictionary with all the current values for the X of the grid. Keys for the dictionary: property, min, max, step, base, expression Types: property=str, min=float, max=float, step=float, base=float, expression=str
Returns: | the dictionary with the parameters |
---|---|
Return type: | dict |
Returns a dictionary with all the current values for the Y of the grid. Keys for the dictionary: property, min, max, step, base, expression Types: property=str, min=float, max=float, step=float, base=float, expression=str
Returns: | the dictionary with the parameters |
---|---|
Return type: | dict |
Bases: weka.core.classes.OptionHandler
Wrapper class for kernels.
Builds the classifier with the data.
Parameters: | data (Instances) – the data to train the classifier with |
---|
Returns the capabilities of the classifier.
Returns: | the capabilities |
---|---|
Return type: | Capabilities |
Returns whether checks are turned off.
Returns: | True if checks turned off |
---|---|
Return type: | bool |
Frees the memory used by the kernel.
Computes the result of the kernel function for two instances. If id1 == -1, eval use inst1 instead of an instance in the dataset.
Parameters: |
|
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Creates a copy of the kernel.
Parameters: | kernel (Kernel) – the kernel to copy |
---|---|
Returns: | the copy of the kernel |
Return type: | Kernel |
Bases: weka.classifiers.Classifier
Wrapper class for classifiers that have a kernel property, like SMO.
Returns the current kernel.
Returns: | the kernel or None if none found |
---|---|
Return type: | Kernel |
Bases: weka.classifiers.SingleClassifierEnhancer
Wrapper class for the MultiSearch meta-classifier. NB: ‘multi-search-weka-package’ must be installed (https://github.com/fracpete/multisearch-weka-package), version 2016.1.15 or later.
Returns the best classifier setup found during the th search.
Returns: | the best classifier setup |
---|---|
Return type: | Classifier |
Returns the currently set statistic used for evaluation.
Returns: | the statistic |
---|---|
Return type: | SelectedTag |
Returns the list of currently set search parameters.
Returns: | the list of AbstractSearchParameter objects |
---|---|
Return type: | list |
Bases: weka.classifiers.Classifier
Wrapper class for classifiers that use a multiple base classifiers.
Returns the list of base classifiers.
Returns: | the classifier list |
---|---|
Return type: | list |
Bases: weka.classifiers.Prediction
Wrapper class for a nominal prediction.
Returns the class distribution.
Returns: | the class distribution list |
---|---|
Return type: | ndarray |
Returns the margin.
Returns: | the margin |
---|---|
Return type: | float |
Bases: weka.classifiers.Prediction
Wrapper class for a numeric prediction.
Returns the error.
Returns: | the error |
---|---|
Return type: | float |
Returns the prediction intervals.
Returns: | the intervals |
---|---|
Return type: | ndarray |
Bases: weka.core.classes.JavaObject
Wrapper class for a prediction.
Returns the actual value.
Returns: | the actual value (internal representation) |
---|---|
Return type: | float |
Returns the predicted value.
Returns: | the predicted value (internal representation) |
---|---|
Return type: | float |
Returns the weight.
Returns: | the weight of the Instance that was used |
---|---|
Return type: | float |
Bases: weka.core.classes.OptionHandler
For collecting predictions and generating output from. Must be derived from weka.classifiers.evaluation.output.prediction.AbstractOutput
Returns the content of the buffer as string.
Returns: | The buffer content |
---|---|
Return type: | str |
Returns the header format.
Returns: | The dataset format |
---|---|
Return type: | Instances |
Prints the header, classifications and footer to the buffer.
Parameters: |
|
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Prints the classification to the buffer.
Parameters: |
|
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Prints the classifications to the buffer.
Parameters: |
|
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Prints the footer to the buffer.
Prints the header to the buffer.
Bases: weka.classifiers.Classifier
Wrapper class for classifiers that use a single base classifier.
Returns the base classifier.
;return: the base classifier :rtype: Classifier
Runs a classifier from the command-line. Calls JVM start/stop automatically. Use -h to see all options.
Turns the predictions turned into an Instances object.
Parameters: |
|
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Returns: | the predictions, None if no predictions present |
Return type: | Instances |
Bases: weka.core.classes.JavaObject
Evaluation class for clusterers.
Return the array (ordered by cluster number) of minimum error class to cluster mappings.
Returns: | the mappings |
---|---|
Return type: | ndarray |
Return an array of cluster assignments corresponding to the most recent set of instances clustered.
Returns: | the cluster assignments |
---|---|
Return type: | ndarray |
The cluster results as string.
Returns: | the results string |
---|---|
Return type: | str |
Cross-validates the clusterer and returns the loglikelihood.
Parameters: |
|
---|---|
Returns: | the cross-validated loglikelihood |
Return type: | float |
Evaluates the clusterer with the given options.
Parameters: |
|
---|---|
Returns: | the evaluation result |
Return type: | str |
Returns the log likelihood.
Returns: | the log likelihood |
---|---|
Return type: | float |
Returns the number of clusters.
Returns: | the number of clusters |
---|---|
Return type: | int |
Sets the built clusterer to evaluate.
Parameters: | clusterer (Clusterer) – the clusterer to evaluate |
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Evaluates the currently set clusterer on the test set.
Parameters: | test (Instances) – the test set to use for evaluating |
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Bases: weka.core.classes.OptionHandler
Wrapper class for clusterers.
Builds the clusterer with the data.
Parameters: | data (Instances) – the data to use for training the clusterer |
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Returns the capabilities of the clusterer.
Returns: | the capabilities |
---|---|
Return type: | Capabilities |
Peforms a prediction.
Parameters: | inst (Instance) – the instance to determine the cluster for |
---|---|
Returns: | the clustering result |
Return type: | float |
Peforms a prediction, returning the cluster distribution.
Parameters: | inst (Instance) – the Instance to get the cluster distribution for |
---|---|
Returns: | the cluster distribution |
Return type: | float[] |
Returns the graph if classifier implements weka.core.Drawable, otherwise None.
Returns: | the graph or None if not available |
---|---|
Return type: | str |
Returns the graph type if classifier implements weka.core.Drawable, otherwise -1.
Returns: | the type |
---|---|
Return type: | int |
Creates a copy of the clusterer.
Parameters: | clusterer (Clusterer) – the clustererto copy |
---|---|
Returns: | the copy of the clusterer |
Return type: | Clusterer |
Returns the number of clusters found.
Returns: | the number fo clusters |
---|---|
Return type: | int |
Updates the clusterer with the instance.
Parameters: | inst (Instance) – the Instance to update the clusterer with |
---|
Signals the clusterer that updating with new data has finished.
Bases: weka.clusterers.SingleClustererEnhancer
Wrapper class for the filtered clusterer.
Returns the filter.
Returns: | the filter |
---|---|
Return type: | Filter |
Bases: weka.clusterers.Clusterer
Wrapper class for clusterers that use a single base clusterer.
Returns the base clusterer.
Returns: | the clusterer |
---|---|
Return type: | Clusterer |
Runs a clusterer from the command-line. Calls JVM start/stop automatically. Use -h to see all options.
Bases: weka.core.classes.OptionHandler
Wrapper class for datagenerators.
Returns the dataset format.
Returns: | the format |
---|---|
Return type: | Instances |
Returns the data format.
Returns: | the data format |
---|---|
Return type: | Instances |
Returns a single Instance.
Returns: | the next example |
---|---|
Return type: | Instance |
Returns complete dataset.
Returns: | the generated dataset |
---|---|
Return type: | Instances |
Returns a “finish” string.
Returns: | a finish comment |
---|---|
Return type: | str |
Returns a “start” string.
Returns: | the start comment |
---|---|
Return type: | str |
Creates a copy of the generator.
Parameters: | generator (DataGenerator) – the generator to copy |
---|---|
Returns: | the copy of the generator |
Return type: | DataGenerator |
Generates data using the generator and commandline arguments.
Parameters: |
|
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Returns a actual number of examples to generate.
Returns: | the number of examples |
---|---|
Return type: | int |
Returns whether data is generated row by row (True) or in one go (False).
Returns: | whether incremental |
---|---|
Return type: | bool |
Runs a datagenerator from the command-line. Calls JVM start/stop automatically. Use -h to see all options.
Bases: weka.core.classes.OptionHandler
Wrapper class for an experiment.
Bases: weka.core.classes.OptionHandler
For generating results from an Experiment run.
Returns the average mean at this location (if valid location).
Parameters: | col (int) – the 0-based column index |
---|---|
Returns: | the mean |
Return type: | float |
Returns the column count.
Returns: | the count |
---|---|
Return type: | int |
Returns the column name.
Parameters: | index (int) – the 0-based row index |
---|---|
Returns: | the column name, None if invalid index |
Return type: | str |
Returns the mean at this location (if valid location).
Parameters: |
|
---|---|
Returns: | the mean |
Return type: | float |
Returns the row name.
Parameters: | index (int) – the 0-based row index |
---|---|
Returns: | the row name, None if invalid index |
Return type: | str |
Returns the standard deviation at this location (if valid location).
Parameters: |
|
---|---|
Returns: | the standard deviation |
Return type: | float |
Hides the column.
Parameters: | index (int) – the 0-based column index |
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Hides the row.
Parameters: | index (int) – the 0-based row index |
---|
Returns whether the column is hidden.
Parameters: | index (int) – the 0-based column index |
---|---|
Returns: | true if hidden |
Return type: | bool |
Returns whether the row is hidden.
Parameters: | index (int) – the 0-based row index |
---|---|
Returns: | true if hidden |
Return type: | bool |
Returns the row count.
Returns: | the count |
---|---|
Return type: | int |
Sets the column name.
Parameters: |
|
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Sets the mean at this location (if valid location).
Parameters: |
|
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Sets the row name.
Parameters: |
|
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Sets the standard deviation at this location (if valid location).
Parameters: |
|
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Shows the column.
Parameters: | index (int) – the 0-based column index |
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Shows the row.
Parameters: | index (int) – the 0-based row index |
---|
Returns the header of the matrix as a string.
Returns: | the header |
---|---|
Return type: | str |
Returns a key for all the col names, for better readability if the names got cut off.
Returns: | the key |
---|---|
Return type: | str |
Returns the matrix as a string.
Returns: | the generated output |
---|---|
Return type: | str |
Returns the ranking in a string representation.
Returns: | the ranking |
---|---|
Return type: | str |
returns the summary as string.
Returns: | the summary |
---|---|
Return type: | str |
Bases: weka.experiments.SimpleExperiment
Performs a simple cross-validation experiment. Can output the results either in ARFF or CSV.
Configures and returns the ResultProducer and PropertyPath as tuple.
Returns: | producer and property path |
---|---|
Return type: | tuple |
Bases: weka.core.classes.OptionHandler
Ancestor for simple experiments.
See following URL for how to use the Experiment API: http://weka.wikispaces.com/Using+the+Experiment+API
Configures and returns the ResultProducer and PropertyPath as tuple.
Returns: | producer and property path |
---|---|
Return type: | tuple |
Configures and returns the SplitEvaluator and Classifier instance as tuple.
Returns: | evaluator and classifier |
---|---|
Return type: | tuple |
Returns the internal experiment, if set up, otherwise None.
Returns: | the internal experiment |
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Return type: | Experiment |
Loads the experiment from disk.
Parameters: | filename (str) – the filename of the experiment to load |
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Returns: | the experiment |
Return type: | Experiment |
Executes the experiment.
Saves the experiment to disk.
Parameters: |
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Initializes the experiment.
Bases: weka.experiments.SimpleExperiment
Performs a simple random split experiment. Can output the results either in ARFF or CSV.
Configures and returns the ResultProducer and PropertyPath as tuple.
Returns: | producer and property path |
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Return type: | tuple |
Bases: weka.core.classes.OptionHandler
For generating statistical results from an experiment.
Returns the list of column names that identify uniquely a dataset.
Returns: | the list of attributes names |
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Return type: | list |
Returns the column name that holds the Fold number.
Returns: | the attribute name |
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Return type: | str |
Creates a “header” string describing the current resultsets.
Parameters: | comparison_column (int) – the index of the column to compare against |
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Returns: | the header |
Return type: | str |
Sets the column indices based on the supplied names if necessary.
Returns the data used in the analysis.
Returns: | the data in use |
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Return type: | Instances |
Creates a comparison table where a base resultset is compared to the other resultsets.
Parameters: |
|
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Returns: | the comparison |
Return type: | str |
Creates a ranking.
Parameters: | comparison_column (int) – the 0-based index of the column to compare against |
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Returns: | the ranking |
Return type: | str |
Carries out a comparison between all resultsets, counting the number of datsets where one resultset outperforms the other.
Parameters: | comparison_column (int) – the 0-based index of the column to compare against |
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Returns: | the summary |
Return type: | str |
Returns the list of column names that identify uniquely a result (eg classifier + options + ID).
Returns: | the list of attribute names |
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Return type: | list |
Returns the ResultMatrix instance in use.
Returns: | the matrix in use |
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Return type: | ResultMatrix |
Returns the column name that holds the Run number.
Returns: | the attribute name |
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Return type: | str |
Bases: weka.core.classes.OptionHandler
Wrapper class for filters.
Signals the filter that the batch of data has finished.
Returns: | True if instances can be collected from the output |
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Return type: | bool |
Returns the capabilities of the filter.
Returns: | the capabilities |
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Return type: | Capabilities |
Filters the dataset(s). When providing a list, this can be used to create compatible train/test sets, since the filter only gets initialized with the first dataset and all subsequent datasets get transformed using the same setup.
NB: inputformat(Instances) must have been called beforehand.
Parameters: | data (Instances or list of Instances) – the Instances to filter |
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Returns: | the filtered Instances object(s) |
Return type: | Instances or list of Instances |
Inputs the Instance.
Parameters: | inst (Instance) – the instance to filter |
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Returns: | True if filtered can be collected from output |
Return type: | bool |
Sets the input format.
Parameters: | data (Instances) – the data to use as input |
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Creates a copy of the filter.
Parameters: | flter (Filter) – the filter to copy |
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Returns: | the copy of the filter |
Return type: | Filter |
Outputs the filtered Instance.
Returns: | the filtered instance |
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Return type: | an Instance object |
Returns the output format.
Returns: | the output format |
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Return type: | Instances |
Bases: weka.filters.Filter
Wrapper class for weka.filters.MultiFilter.
Returns the list of base filters.
Returns: | the filter list |
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Return type: | list |
Bases: weka.filters.Filter
Wrapper class for weka.filters.unsupervised.attribute.StringToWordVector.
Returns the stemmer.
Returns: | the stemmer |
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Return type: | Stemmer |
Returns the stopwords handler.
Returns: | the stopwords handler |
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Return type: | Stopwords |
Returns the tokenizer.
Returns: | the tokenizer |
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Return type: | Tokenizer |
Runs a filter from the command-line. Calls JVM start/stop automatically. Use -h to see all options.