# Source code for skll.metrics

```
# License: BSD 3 clause
"""
Metrics that can be used to evaluate the performance of learners.
:author: Nitin Madnani (nmadnani@ets.org)
:author: Michael Heilman (mheilman@ets.org)
:author: Dan Blanchard (dblanchard@ets.org)
:organization: ETS
"""
import sys
from importlib import import_module
from inspect import signature
from os.path import abspath, basename, dirname, exists
import numpy as np
from scipy.stats import kendalltau, pearsonr, spearmanr
from sklearn.metrics import SCORERS, confusion_matrix, f1_score, make_scorer
# a set that will hold the names of any custom metrics;
# this is a private variable only meant for internal use
_CUSTOM_METRICS = set()
[docs]def kappa(y_true, y_pred, weights=None, allow_off_by_one=False):
"""
Calculates the kappa inter-rater agreement between two the gold standard
and the predicted ratings. Potential values range from -1 (representing
complete disagreement) to 1 (representing complete agreement). A kappa
value of 0 is expected if all agreement is due to chance.
In the course of calculating kappa, all items in ``y_true`` and ``y_pred`` will
first be converted to floats and then rounded to integers.
It is assumed that y_true and y_pred contain the complete range of possible
ratings.
This function contains a combination of code from yorchopolis's kappa-stats
and Ben Hamner's Metrics projects on Github.
Parameters
----------
y_true : array-like of float
The true/actual/gold labels for the data.
y_pred : array-like of float
The predicted/observed labels for the data.
weights : str or np.array, optional
Specifies the weight matrix for the calculation.
Options are ::
- None = unweighted-kappa
- 'quadratic' = quadratic-weighted kappa
- 'linear' = linear-weighted kappa
- two-dimensional numpy array = a custom matrix of
weights. Each weight corresponds to the
:math:`w_{ij}` values in the wikipedia description
of how to calculate weighted Cohen's kappa.
Defaults to None.
allow_off_by_one : bool, optional
If true, ratings that are off by one are counted as
equal, and all other differences are reduced by
one. For example, 1 and 2 will be considered to be
equal, whereas 1 and 3 will have a difference of 1
for when building the weights matrix.
Defaults to False.
Returns
-------
k : float
The kappa score, or weighted kappa score.
Raises
------
AssertionError
If ``y_true`` != ``y_pred``.
ValueError
If labels cannot be converted to int.
ValueError
If invalid weight scheme.
"""
# Ensure that the lists are both the same length
assert(len(y_true) == len(y_pred))
# This rather crazy looking typecast is intended to work as follows:
# If an input is an int, the operations will have no effect.
# If it is a float, it will be rounded and then converted to an int
# because the ml_metrics package requires ints.
# If it is a str like "1", then it will be converted to a (rounded) int.
# If it is a str that can't be typecast, then the user is
# given a hopefully useful error message.
try:
y_true = [int(np.round(float(y))) for y in y_true]
y_pred = [int(np.round(float(y))) for y in y_pred]
except ValueError:
raise ValueError("For kappa, the labels should be integers or strings"
" that can be converted to ints (E.g., '4.0' or "
"'3').")
# Figure out normalized expected values
min_rating = min(min(y_true), min(y_pred))
max_rating = max(max(y_true), max(y_pred))
# shift the values so that the lowest value is 0
# (to support scales that include negative values)
y_true = [y - min_rating for y in y_true]
y_pred = [y - min_rating for y in y_pred]
# Build the observed/confusion matrix
num_ratings = max_rating - min_rating + 1
observed = confusion_matrix(y_true, y_pred,
labels=list(range(num_ratings)))
num_scored_items = float(len(y_true))
# Build weight array if weren't passed one
if isinstance(weights, str):
wt_scheme = weights
weights = None
else:
wt_scheme = ''
if weights is None:
weights = np.empty((num_ratings, num_ratings))
for i in range(num_ratings):
for j in range(num_ratings):
diff = abs(i - j)
if allow_off_by_one and diff:
diff -= 1
if wt_scheme == 'linear':
weights[i, j] = diff
elif wt_scheme == 'quadratic':
weights[i, j] = diff ** 2
elif not wt_scheme: # unweighted
weights[i, j] = bool(diff)
else:
raise ValueError('Invalid weight scheme specified for '
f'kappa: {wt_scheme}')
hist_true = np.bincount(y_true, minlength=num_ratings)
hist_true = hist_true[: num_ratings] / num_scored_items
hist_pred = np.bincount(y_pred, minlength=num_ratings)
hist_pred = hist_pred[: num_ratings] / num_scored_items
expected = np.outer(hist_true, hist_pred)
# Normalize observed array
observed = observed / num_scored_items
# If all weights are zero, that means no disagreements matter.
k = 1.0
if np.count_nonzero(weights):
k -= (sum(sum(weights * observed)) / sum(sum(weights * expected)))
return k
[docs]def correlation(y_true, y_pred, corr_type='pearson'):
"""
Calculate given correlation between ``y_true`` and ``y_pred``. ``y_pred``
can be multi-dimensional. If ``y_pred`` is 1-dimensional, it may either
contain probabilities, most-likely classification labels, or regressor
predictions. In that case, we simply return the correlation between
``y_true`` and ``y_pred``. If ``y_pred`` is multi-dimensional,
it contains probabilties for multiple classes in which case, we infer
the most likely labels and then compute the correlation between those
and ``y_true``.
Parameters
----------
y_true : array-like of float
The true/actual/gold labels for the data.
y_pred : array-like of float
The predicted/observed labels for the data.
corr_type : str, optional
Which type of correlation to compute. Possible
choices are ``pearson``, ``spearman``,
and ``kendall_tau``.
Defaults to ``pearson``.
Returns
-------
ret_score : float
correlation value if well-defined, else 0.0
"""
# get the correlation function to use based on the given type
corr_func = pearsonr
if corr_type == 'spearman':
corr_func = spearmanr
elif corr_type == 'kendall_tau':
corr_func = kendalltau
# convert to numpy array in case we are passed a list
y_pred = np.array(y_pred)
# multi-dimensional -> probability array -> get label
if y_pred.ndim > 1:
labels = np.argmax(y_pred, axis=1)
ret_score = corr_func(y_true, labels)[0]
# 1-dimensional -> probabilities/labels -> use as is
else:
ret_score = corr_func(y_true, y_pred)[0]
return ret_score
[docs]def f1_score_least_frequent(y_true, y_pred):
"""
Calculate the F1 score of the least frequent label/class in ``y_true`` for
``y_pred``.
Parameters
----------
y_true : array-like of float
The true/actual/gold labels for the data.
y_pred : array-like of float
The predicted/observed labels for the data.
Returns
-------
ret_score : float
F1 score of the least frequent label.
"""
least_frequent = np.bincount(y_true).argmin()
return f1_score(y_true, y_pred, average=None)[least_frequent]
[docs]def register_custom_metric(custom_metric_path, custom_metric_name):
"""
Import, load, and register the custom metric function from the given path.
Parameters
----------
custom_metric_path : str
The path to a custom metric.
custom_metric_name : str
The name of the custom metric function to load.
This function must take only two array-like
arguments: the true labels and the predictions,
in that order.
Raises
------
ValueError
If the custom metric path does not end in '.py'.
NameError
If the name of the custom metric file conflicts
with an already existing attribute in ``skll.metrics``
or if the custom metric name conflicts with a scikit-learn
or SKLL metric.
"""
if not custom_metric_path:
raise ValueError(f"custom metric path was not set and "
f"metric {custom_metric_name} was not found.")
if not exists(custom_metric_path):
raise ValueError(f"custom metric path '{custom_metric_path}' "
f"does not exist.")
if not custom_metric_path.endswith('.py'):
raise ValueError(f"custom metric path must end in .py, you specified "
f"{custom_metric_path}")
# get the name of the module containing the custom metric
custom_metric_module_name = basename(custom_metric_path)[:-3]
# once we know that the module name is okay, we need to make sure
# that the metric function name is also okay
if custom_metric_name in SCORERS:
raise NameError(f"a metric called '{custom_metric_name}' already "
f"exists in SKLL; rename the metric function "
f"in {custom_metric_module_name}.py and try again.")
# dynamically import the module unless we have already done it
if custom_metric_module_name not in sys.modules:
sys.path.append(dirname(abspath(custom_metric_path)))
metric_module = import_module(custom_metric_module_name)
# this statement is only necessary so that if we end
# up using multiprocessing parallelization backend,
# things are serialized properly
globals()[custom_metric_module_name] = metric_module
# get the metric function from this imported module
metric_func = getattr(sys.modules[custom_metric_module_name],
custom_metric_name)
# again, we need this for multiprocessing serialization
metric_func.__module__ = f"skll.metrics.{custom_metric_module_name}"
# extract any "special" keyword arguments from the metric function
metric_func_parameters = signature(metric_func).parameters
make_scorer_kwargs = {}
for make_scorer_kwarg in ['greater_is_better',
'needs_proba',
'needs_threshold']:
if make_scorer_kwarg in metric_func_parameters:
parameter_value = metric_func_parameters.get(make_scorer_kwarg).default
make_scorer_kwargs.update({make_scorer_kwarg: parameter_value})
# make the scorer function with the extracted keyword arguments
# and add it to the `CUSTOM_METRICS` set
SCORERS[f"{custom_metric_name}"] = make_scorer(metric_func, **make_scorer_kwargs)
_CUSTOM_METRICS.add(custom_metric_name)
return metric_func
[docs]def use_score_func(func_name, y_true, y_pred):
"""
Call the scoring function in ``sklearn.metrics.SCORERS`` with the given
name. This takes care of handling keyword arguments that were pre-specified
when creating the scorer. This applies any sign-flipping that was specified
by ``make_scorer()`` when the scorer was created.
Parameters
----------
func_name : str
The name of the objective function to use from SCORERS.
y_true : array-like of float
The true/actual/gold labels for the data.
y_pred : array-like of float
The predicted/observed labels for the data.
Returns
-------
ret_score : float
The scored result from the given scorer.
"""
scorer = SCORERS[func_name]
return scorer._sign * scorer._score_func(y_true, y_pred, **scorer._kwargs)
```