Clinical UPDRS¶

class clinical_updrs.Clinical_UPDRS(labels_file_path, data_frame=None, data_frame_file_path=None)¶

Supervised Learning for UPDRS using the evaluation of the clinical staff. It calculates classifiers implementing the k-nearest neighbors vote

>>> import pdkit
>>> clinical_UPDRS = pdkit.Clinical_UPDRS(labels_file_path, data_frame)

where the labels_file_path is the path to the clinical data file, data_frame is the result of the testResultSet.

To score a new measurement against the trained knn clusters.

>>> clinical_UPDRS.predict(measurement)

To read the testResultSet data from a file. See TestResultSet class for more details.

>>> clinical_UPDRS = pdkit.Clinical_UPDRS(labels_file_path, data_frame_file_path=file_path_to_testResultSet_file)

Parameters:

labels_file_path (string) – the path to the clinical data
data_frame (pandas.DataFrame) – testResultSet
data_frame_file_path (pandas.DataFrame) – the path to read the data frame from

predict(measurement, output_format='array')¶

Method to predict the class labels for the provided data

Parameters:

measurement (pandas.DataFrame) – the point to classify
output_format (string) – the format to return the scores (‘array’ or ‘str’)

Return prediction:

the prediction for a given test/point

Rtype prediction:

np.array

PDkit