本文整理汇总了Python中sklearn.ensemble.RandomForestRegressor.metrics方法的典型用法代码示例。如果您正苦于以下问题：Python RandomForestRegressor.metrics方法的具体用法？Python RandomForestRegressor.metrics怎么用？Python RandomForestRegressor.metrics使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类sklearn.ensemble.RandomForestRegressor的用法示例。
在下文中一共展示了RandomForestRegressor.metrics方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from sklearn.ensemble import RandomForestRegressor [as 别名]
# 或者: from sklearn.ensemble.RandomForestRegressor import metrics [as 别名]
def main():
"""
Main script function.
:return:
"""
def to_proportion(string):
string = float(string)
if string <= 0 or string > 100:
raise ValueError(string)
if 1 < string:
string /= 100
return string
def to_pos(string):
string = int(string)
if string < 0:
raise ValueError(string)
parser = argparse.ArgumentParser(__doc__)
subset = parser.add_mutually_exclusive_group()
subset.add_argument("-r", "--random", type=int,
default=None,
help="A fixed of models to select for training.")
subset.add_argument("-p", "--proportion", type=float,
default=None,
help="Proportion of the models to be used for training.")
parser.add_argument("-c", "--conf",
required=True,
help="File with the configuration for selecting best and worst transcripts.")
parser.add_argument("--regress", action="store_true", default=False)
# parser.add_argument("-t", "--tmap",
# help="The TMAP file with the comparison results.",
# required=True)
parser.add_argument("-m", "--metrics", help="The metrics file.", required=True)
parser.add_argument("-o", "--out", help="Output file.", default="forest.model")
parser.add_argument("-s", "--scoring", help="The original scoring file, to retrieve info on fragments.")
args = parser.parse_args()
# X should contain a matrix of features derived from the portcullis tab file
# y should contain the labels (0 not a valid junction, 1 a valid junction).
# Confirmed with the reference.
# Load tab file and produce matrix
# bed, tab = loadtab(args.input)
# tmap_results = load_tmap(args.tmap)
# scores = dict()
# for tid in tmap_results:
# if tmap_results[tid].ccode == ("u",):
# continue
# recall = np.mean([tmap_results[tid].j_recall,
# tmap_results[tid].e_recall,
# tmap_results[tid].n_recall])
# precision = np.mean([tmap_results[tid].j_prec,
# tmap_results[tid].e_prec,
# tmap_results[tid].n_prec])
# if min(recall, precision) > 0:
# scores[tid] = hmean([recall, precision])
# else:
# scores[tid] = 0
#
# print("# TMAP results: " + str(len(tmap_results)))
# Load reference and add labels
# ref = bed12.loadbed(args.reference, False, False)
# metrics = pandas.read_csv(args.metrics, delimiter="\t")
metrics_pandas = pandas.read_csv(args.metrics, delimiter="\t")
try:
zeros = metrics_pandas[(((metrics_pandas.exon_num==1) & (metrics_pandas.combined_cds_length==0) & (metrics_pandas.cdna_length < 300 )) | ((metrics_pandas.exon_num>1) & (metrics_pandas.combined_cds_intron_fraction==0) & (metrics_pandas.retained_fraction>0.5 )) )].tid
except AttributeError as exc:
raise AttributeError("\n".join([str(exc), str("\n\t".join(list(metrics_pandas.columns)))]))
hundreds = metrics_pandas[(metrics_pandas.proportion_verified_introns_inlocus==1) & (metrics_pandas.snowy_blast_score>10) & (metrics_pandas.retained_fraction==0) & (((metrics_pandas.exon_num>1) & (metrics_pandas.verified_introns_num>2)) | ((metrics_pandas.exon_num==1) & (metrics_pandas.utr_num==2)) )].tid
metrics = load_metrics(args.metrics)
scores = dict()
for z in zeros:
scores[z] = 0
for h in hundreds:
scores[h] = 100
print("# metered transcripts:", len(metrics))
if args.random is not None or args.proportion is not None:
if args.random is not None:
selected = random.sample(scores.keys(), args.random)
else:
selected = random.sample(scores.keys(),
int(floor(len(scores) * args.proportion)))
scores = dict(_ for _ in scores.items() if _[0] in selected)
metrics = dict(_ for _ in metrics.items() if _[0] in selected)
X = np.zeros((len(scores), len(MetricEntry.metrics)))
#.........这里部分代码省略.........