"""
Appends new scorers in to the SCORERS constant.
"""
from sklearn.metrics import SCORERS
from sklearn.metrics import make_scorer
from imblearn.metrics import geometric_mean_score
from ..metrics.classification import tp_score, tn_score, fp_score, fn_score
from ..metrics.regression import weighted_mean_squared_error
__all__ = [
'geometric_mean_score', 'tp_score', 'tn_score', 'fp_score', 'fn_score',
'weighted_mean_squared_error', 'SCORERS'
]
APPENDED_SCORERS = {
'tp': (tp_score, ),
'tn': (tn_score, ),
'fp': (fp_score, False),
'fn': (fn_score, False),
'geometric_mean_score': (geometric_mean_score, ),
'weighted_mean_squared_error': (weighted_mean_squared_error, )
}
APPENDED_SCORERS = {
name: make_scorer(*sc_func)
for name, sc_func in APPENDED_SCORERS.items()
}
SCORERS.update(APPENDED_SCORERS)
_scorers = {
'f1_score_micro':
make_scorer(f1_score, average='micro', pos_label=None),
'f1_score_macro':
make_scorer(f1_score, average='macro', pos_label=None),
'f1_score_weighted':
make_scorer(f1_score, average='weighted', pos_label=None),
'f1_score_least_frequent':
make_scorer(f1_score_least_frequent),
'pearson':
make_scorer(pearson),
'spearman':
make_scorer(spearman),
'kendall_tau':
make_scorer(kendall_tau),
'unweighted_kappa':
make_scorer(kappa),
'quadratic_weighted_kappa':
make_scorer(kappa, weights='quadratic'),
'linear_weighted_kappa':
make_scorer(kappa, weights='linear'),
'qwk_off_by_one':
make_scorer(kappa, weights='quadratic', allow_off_by_one=True),
'lwk_off_by_one':
make_scorer(kappa, weights='linear', allow_off_by_one=True),
'uwk_off_by_one':
make_scorer(kappa, allow_off_by_one=True)
}
SCORERS.update(_scorers)
from .version import __version__, VERSION
__all__ = ['Learner', 'load_examples', 'kappa', 'kendall_tau', 'spearman',
'pearson', 'f1_score_least_frequent', 'run_configuration',
'run_ablation', 'write_feature_file', 'convert_examples']
# Add our scorers to the sklearn dictionary here so that they will always be
# available if you import anything from skll
_scorers = {'f1_score_micro': make_scorer(f1_score, average='micro',
pos_label=None),
'f1_score_macro': make_scorer(f1_score, average='macro',
pos_label=None),
'f1_score_weighted': make_scorer(f1_score, average='weighted',
pos_label=None),
'f1_score_least_frequent': make_scorer(f1_score_least_frequent),
'pearson': make_scorer(pearson),
'spearman': make_scorer(spearman),
'kendall_tau': make_scorer(kendall_tau),
'unweighted_kappa': make_scorer(kappa),
'quadratic_weighted_kappa': make_scorer(kappa,
weights='quadratic'),
'linear_weighted_kappa': make_scorer(kappa, weights='linear'),
'qwk_off_by_one': make_scorer(kappa, weights='quadratic',
allow_off_by_one=True),
'lwk_off_by_one': make_scorer(kappa, weights='linear',
allow_off_by_one=True),
'uwk_off_by_one': make_scorer(kappa, allow_off_by_one=True)}
SCORERS.update(_scorers)
'neg_root_mean_squared_error': 'neg_root_mean_squared_error',
'neg_mean_poisson_deviance': 'neg_mean_poisson_deviance',
'neg_mean_gamma_deviance': 'neg_mean_gamma_deviance'
},
}
AVALIABLE['categorical'] = AVALIABLE['binary'].copy()
# Set defaults
AVALIABLE['binary']['default'] = 'roc_auc'
AVALIABLE['regression']['default'] = 'r2'
AVALIABLE['categorical']['default'] = 'roc_auc_ovr'
SCORERS.update({
'neg_hamming':
M.make_scorer(score_func=M.hamming_loss,
greater_is_better=False,
needs_threshold=False),
'matthews':
M.make_scorer(score_func=M.matthews_corrcoef, greater_is_better=True)
})
def get_scorer_from_str(scorer_str):
return SCORERS[scorer_str]
def get_scorers_by_type(problem_type):
objs = []
for scorer_str in AVALIABLE[problem_type]:
conv = AVALIABLE[problem_type][scorer_str]
