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# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""TODO: Add a description here."""
import evaluate
import datasets
import numpy as np
from seametrics.horizon.utils import *
# TODO: Add BibTeX citation
_CITATION = """\
@InProceedings{huggingface:module,
title = {A great new module},
authors={huggingface, Inc.},
year={2020}
}
"""
# TODO: Add description of the module here
_DESCRIPTION = """\
This new module is designed to solve this great ML task and is crafted with a lot of care.
"""
# TODO: Add description of the arguments of the module here
_KWARGS_DESCRIPTION = """
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of predictions to score. Each predictions
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
Returns:
accuracy: description of the first score,
another_score: description of the second score,
Examples:
Examples should be written in doctest format, and should illustrate how
to use the function.
>>> my_new_module = evaluate.load("my_new_module")
>>> results = my_new_module.compute(references=[0, 1], predictions=[0, 1])
>>> print(results)
{'accuracy': 1.0}
"""
# TODO: Define external resources urls if needed
BAD_WORDS_URL = "http://url/to/external/resource/bad_words.txt"
@evaluate.utils.file_utils.add_start_docstrings(_DESCRIPTION,
_KWARGS_DESCRIPTION)
class horizonmetrics(evaluate.Metric):
"""TODO: Short description of my evaluation module."""
def __init__(self,
roll_threshold=0.5,
pitch_threshold=0.1,
vertical_fov_degrees=25.6,
**kwargs):
super().__init__(**kwargs)
self.slope_threshold = roll_to_slope(roll_threshold)
self.midpoint_threshold = pitch_to_midpoint(pitch_threshold,
vertical_fov_degrees)
self.predictions = None
self.ground_truth_det = None
self.slope_error_list = None
self.midpoint_error_list = None
def _info(self):
# TODO: Specifies the evaluate.EvaluationModuleInfo object
return evaluate.MetricInfo(
# This is the description that will appear on the modules page.
module_type="metric",
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
# This defines the format of each prediction and reference
features=datasets.Features({
'predictions': datasets.Value('int64'),
'references': datasets.Value('int64'),
}),
# Homepage of the module for documentation
homepage="http://module.homepage",
# Additional links to the codebase or references
codebase_urls=["http://github.com/path/to/codebase/of/new_module"],
reference_urls=["http://path.to.reference.url/new_module"])
def add(self, *, predictions, references, **kwargs):
"""
Update the predictions and ground truth detections.
Parameters
----------
predictions : list
List of predicted horizons.
ground_truth_det : list
List of ground truth horizons.
"""
# does not impact the metric, but is required for the interface x_x
super(evaluate.Metric, self).add(prediction=0, references=0, **kwargs)
self.predictions = predictions
self.ground_truth_det = references
self.slope_error_list = []
self.midpoint_error_list = []
for annotated_horizon, proposed_horizon in zip(self.ground_truth_det,
self.predictions):
slope_error, midpoint_error = calculate_horizon_error(
annotated_horizon, proposed_horizon)
self.slope_error_list.append(slope_error)
self.midpoint_error_list.append(midpoint_error)
def _compute(self, *, predictions, references, **kwargs):
"""
Compute the horizon error across the sequence.
Returns
-------
float
The computed horizon error.
"""
return calculate_horizon_error_across_sequence(
self.slope_error_list, self.midpoint_error_list,
self.slope_threshold, self.midpoint_threshold)
def _download_and_prepare(self, dl_manager):
"""Optional: download external resources useful to compute the scores"""
# TODO: Download external resources if needed
pass
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