conmo.metrics.RMSPE
- class conmo.metrics.RMSPE(normalize: bool = True)[source]
- calculate(idx: int, algorithms: Iterable[str], last_preprocess_dir: str, algorithms_dir: str, metrics_dir: str) None[source]
Calculates specific metric for each of the algorithms’ results.
- Parameters
idx (str) – Index of the metric in the Experiment. Userful in case you want to calculate several metrics.
algoritmss (Iterable[str]) – List of names of the selected algorithms.
last_preprocess_dir – Name of the directory where the ground truth is located
algorithms_dir – Name of the directory where the results of the algorithms executions are stored.
metrics_dir – Name of th edirectory where the results will be stored.
- labels_per_sequence(labels: DataFrame) bool
Use only with time series datasets. Checks if the labels file of the chosen dataset has an index format with sequences only or sequences and time. This method in future updates will be changed to a specific class for time series.
- Parameters
labels (Pandas Dataframe) – Labels file of the dataset.
- Returns
True if the labels contains 1 level of index with sequence or False if the labels file contains 2 leves with sequence and time.
- Return type
bool
- Raises
RuntimeError – If the number of index levels is invalid.
- load_results(algorithm: str, algorithms_dir: str) DataFrame
Load results for a specific algorthm.
- Parameters
algoritm (str) – Name of the selected algorithm.
algorithms_dir (str) – Name of the directory where the results of the algorithms executions are stored.
- Returns
Dataframe cantainig the results (predictions).
- Return type
Pandas Dataframe
- load_truth(last_preprocess_dir: str)
Load labels from the last preprocess directory.
- Parameters
last_preprocess_dir (str) – Last diretory where the labels dataframe was stored.
- Returns
Dataframe cantainig the labels.
- Return type
Pandas Dataframe
- problem_label(truth: DataFrame) str
Determinates the nature of the problem by identifying the column’s name of the labels.
- Parameters
truth (Pandas Dataframe) – Labels file of the dataset.
- Returns
Returns the column for the metric.
- Return type
str
- Raises
RuntimeError – If the labels of the ground truth are invalid for the problem.
- rmspe(y_true: ndarray, y_pred: ndarray) ndarray[source]
Compute Root Mean Square Percentage Error between two arrays.
- save_output(metric: DataFrame, idx: int, metrics_dir: str) None
Save metric’s output to disk.
- Parameters
metric (Pandas Dataframe) – Dataframe containing the metric’s results.
idx (int) – Index of the metric in the Experiment. Userful in case you want to calculate several metrics.
metrics_dir (str) – Name of the directory where the results will be stored.
- show_start_message()
Simple method to print on the terminal the name of the used metric.
Methods
__init__([normalize])calculate(idx, algorithms, ...)Calculates specific metric for each of the algorithms' results.
labels_per_sequence(labels)Use only with time series datasets.
load_results(algorithm, algorithms_dir)Load results for a specific algorthm.
load_truth(last_preprocess_dir)Load labels from the last preprocess directory.
problem_label(truth)Determinates the nature of the problem by identifying the column's name of the labels.
rmspe(y_true, y_pred)Compute Root Mean Square Percentage Error between two arrays.
save_output(metric, idx, metrics_dir)Save metric's output to disk.
Simple method to print on the terminal the name of the used metric.