Usage:qiime feature-classifier classify-sklearn[OPTIONS]Classify readsbytaxon using a fitted classifier.Inputs:--i-reads ARTIFACT FeatureData[Sequence]The featuredatatobe classified.[required]--i-classifier ARTIFACT TaxonomicClassifier The taxonomic classifierforclassifying the reads.[required]Parameters:--p...
--o-classifier classifier.qza 下面就用训练好的分类器给自己的数据分类,然后是可视化,这里就不可视化了,基本就结束了。然后你得到taxonomy文件后就可以进行不同分类水平的菌属分析了。 qiime feature-classifier classify-sklearn \ --i-classifier classifier.qza \ --i-reads rep-seqs.qza \ --o-classificat...
classifier.qza: 生成分类器文件。查看 | 下载 测试分类集 Test the classifier 下面我们使用训练好的分析器,对《4人体各部位微生物组分析Moving Pictures》中的代表序列进行物种注释。 # 使用训练后的分类集对结果进行注释, 21s time qiime feature-classifier classify-sklearn \ --i-classifier classifier.qza \...
测试分类集 # 使用训练后的分类集对结果进行注释 qiime feature-classifier classify-sklearn --i-classifier classifier.qza --i-reads rep-seqs.qza --o-classification taxonomy.qza # 可视化注释的结果 qiime metadata tabulate --m-input-file taxonomy.qza --o-visualization taxonomy.qzv 下载并在线view....
from sklearn.base import BaseEstimator, ClassifierMixin, clone from sklearn.metrics import accuracy_score @@ -13,7 +14,7 @@ from ..._utils import _classifier_fit_depth_methods, _classifier_get_classes from ...exploratory.depth import Depth, ModifiedBandDepth from ...preprocessing.dim_reduction...
from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import train_test_split import matplotlib.pyplot as plt # Load the Covertype dataset data = pd.read_csv("https://archive.ics.uci.edu/ml/machine-learning-databases/covtype/covtype.data.gz", header=None) ...
>>> import sklearn.model_selection as modsel # Specify a search grid, then do a 5-fold grid search for each of the feature sets >>> param_grid_ = {'C': [1e-5, 1e-3, 1e-1, 1e0, 1e1, 1e2]} # Tune classifier for bag-of-words representation >>> bow_search = modsel....
Eg, this code first trains an audio segment classifier, given a set of WAV files stored in folders (each folder representing a different class) and then the trained classifier is used to classify an unknown audio WAV file from pyAudioAnalysis import audioTrainTest as aT aT.extract_features_...
To classify audio samples with the classifier you created above, # Classify a single file results = classify( file = "<path to audio>", feature_names=["gfcc", "spectral", "chroma"], classifier="svm", classifier_name="svm_test_clf" ) # Classify multiple files with known labels and loc...
sklearn.feature_extraction.text.CountVectorizer TFiDF In order to re-weight the count features into floating point values suitable for usage by a classifier Term frequency tf = 1 / x.sum(axis=1) [:,None] x = x * tf Inverse Document Frequency ...