本文整理汇总了Python中sklearn.decomposition.NMF.predict方法的典型用法代码示例。如果您正苦于以下问题：Python NMF.predict方法的具体用法？Python NMF.predict怎么用？Python NMF.predict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.decomposition.NMF的用法示例。
在下文中一共展示了NMF.predict方法的2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: Topicmodel
# 需要导入模块: from sklearn.decomposition import NMF [as 别名]
# 或者: from sklearn.decomposition.NMF import predict [as 别名]
class Topicmodel():
'''
Wrapper class for different topic models
'''
def __init__(self,folder='model',modeltype='kmeans',topics=100,topwords=10):
# the classifier, which also contains the trained BoW transformer
self.bow = cPickle.load(open(folder+'/BoW_transformer.pickle'))
self.folder = folder
self.modeltype = modeltype
self.topics = topics
self.topwords = topwords
if self.modeltype is 'kmeans':
from sklearn.cluster import KMeans
self.model = KMeans(n_clusters=topics,n_init=50)
if self.modeltype is 'kpcakmeans':
from sklearn.cluster import KMeans
from sklearn.decomposition import KernelPCA
self.model = {'kpca':KernelPCA(kernel='rbf',gamma=.1),\
'kmeans':KMeans(n_clusters=topics,n_init=50)}
if self.modeltype is 'nmf':
from sklearn.decomposition import NMF
self.model = NMF(n_components=topics)
def fit(self,X):
'''
fits a topic model
INPUT
X list of strings
'''
# transform list of strings into sparse BoW matrix
X = self.bow['tfidf_transformer'].fit_transform(\
self.bow['count_vectorizer'].fit_transform(X))
# transform word to BoW index into reverse lookup table
words = self.bow['count_vectorizer'].vocabulary_.values()
wordidx = self.bow['count_vectorizer'].vocabulary_.keys()
self.idx2word = dict(zip(words,wordidx))
# depending on the model, train
if self.modeltype is 'kmeans':
Xc = self.model.fit_predict(X)
if self.modeltype is 'kpcakmeans':
Xc = self.model['kpca'].fit_transform(X)
Xc = self.model['kmeans'].fit_predict(Xc)
if self.modeltype is 'nmf':
Xc = self.model.fit_transform(X).argmax(axis=0)
# for each cluster/topic compute covariance of word with cluster label
# this measure is indicative of the importance of the word for the topic
ass = zeros(self.topics)
self.topicstats = []
for cluster in range(self.topics):
# this is a binary vector, true if a data point was in this cluster
y = double(Xc==cluster)
# this is the covariance of the data with the cluster label
Xcov = X.T.dot(y)
# find the most strongly covarying (with the cluster label) words
wordidx = reversed(Xcov.argsort()[-self.topwords:])
topicwords = dict([(self.idx2word[idx],Xcov[idx]) for idx in wordidx])
self.topicstats.append({'assignments':y.sum(),'clusterid':cluster,\
'words': topicwords})
print 'Topic %d: %3d Assignments '%(cluster,y.sum())\
+ 'Topwords: ' + ' '.join(topicwords.keys()[:10])
datestr = datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
fn = self.folder+'/topicmodel-%s-'%self.modeltype +datestr+'.json'
print "Saving model stats to "+fn
open(fn,'wb').write(json.dumps(self.topicstats))
def predict(self,X):
'''
predicts cluster assignment from list of strings
INPUT
X list of strings
'''
if X is not list: X = [X]
X = self.bow['tfidf_transformer'].transform(\
self.bow['count_vectorizer'].transform(X))
if self.modeltype is 'kmeans':
return self.model.predict(X)
if self.modeltype is 'kpcakmeans':
return self.model['kmeans'].predict(self.model['kpca'].transform(X))
if self.modeltype is 'nmf':
return self.model.transform(X).argmax(axis=0)示例2: rows
# 需要导入模块: from sklearn.decomposition import NMF [as 别名]
# 或者: from sklearn.decomposition.NMF import predict [as 别名]
#.........这里部分代码省略.........
>>> explo.fit() # fitting!
>>> W = explo.model.transform(explo.X_train_sc) # getting the mixture array
>>> H = explo.X_scaler.inverse_transform(explo.model.components_) # components in the original space
>>> plt.plot(X,H.T) # plot the two components
"""
def __init__(self,x,**kwargs):
"""
Parameters
----------
x : array{Float64}
the spectra organised in rows (1 row = one spectrum). The spectra should share a common X axis.
"""
self.x = x
#
# Kwargs extractions
#
self.X_test = kwargs.get("X_test",[0.0])
self.algorithm = kwargs.get("algorithm","PCA")
self.test_size = kwargs.get("test_size",0.3)
self.scaling = kwargs.get("scaling",True)
self.scaler = kwargs.get("scaler","MinMaxScaler")
self.rand_state = kwargs.get("rand_state",42)
self.nb_compo = kwargs.get("n_components",2)
if len(self.X_test) == 1:
self.X_train, self.X_test = sklearn.model_selection.train_test_split(
self.x, test_size=self.test_size, random_state=self.rand_state)
elif self.X_test.shape[1] == self.x.shape[1]:
self.X_train = np.copy(self.x)
else:
ValueError("You tried to provide a testing dataset that has a different number of features (in columns) than the training set. Please correct this.")
# initialising the preprocessor scaler
if self.scaler == "StandardScaler":
self.X_scaler = sklearn.preprocessing.StandardScaler()
elif self.scaler == "MinMaxScaler":
self.X_scaler = sklearn.preprocessing.MinMaxScaler()
else:
InputError("Choose the scaler between MinMaxScaler and StandardScaler")
# fitting scaler
self.X_scaler.fit(self.X_train)
# scaling the data in all cases, it may not be used during the fit later
self.X_train_sc = self.X_scaler.transform(self.X_train)
self.X_test_sc = self.X_scaler.transform(self.X_test)
def fit(self):
"""Train the model with the indicated algorithm.
Do not forget to tune the hyperparameters.
"""
if self.algorithm == "PCA":
self.model = PCA(n_components=self.nb_compo)
elif self.algorithm == "NMF":
self.model = NMF(n_components=self.nb_compo,init = "nndsvd")
if self.scaling == True:
self.model.fit(self.X_train_sc)
else:
self.model.fit(self.X_train)
def refit(self):
"""Train the model with the indicated algorithm.
Do not forget to tune the hyperparameters.
"""
if self.scaling == True:
self.model.fit(self.X_train_sc)
else:
self.model.fit(self.X_train)
def predict(self,X):
"""Predict using the model.
Parameters
----------
X : {array-like, sparse matrix}, shape = (n_samples, n_features)
Samples.
Returns
-------
C : array, shape = (n_samples,)
Returns predicted values.
Remark
------
if self.scaling == "yes", scaling will be performed on the input X.
"""
if self.scaling == True:
X_sc = self.X_scaler.transform(X)
pred_sc = self.model.predict(X_sc)
return self.Y_scaler.inverse_transform(pred_sc.reshape(-1,1))
else:
return self.model.predict(self.X)