本文整理汇总了Python中sklearn.covariance.LedoitWolf.get_precision方法的典型用法代码示例。如果您正苦于以下问题：Python LedoitWolf.get_precision方法的具体用法？Python LedoitWolf.get_precision怎么用？Python LedoitWolf.get_precision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.covariance.LedoitWolf的用法示例。
在下文中一共展示了LedoitWolf.get_precision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.covariance import LedoitWolf [as 别名]
# 或者: from sklearn.covariance.LedoitWolf import get_precision [as 别名]
def main():
'''
Constructs a co-occurence network from gene expression data.
Main entry point to code.
'''
# Read in the data
if os.path.isfile(DATA_PICKLE):
print("reading previously saved data from pickle %s" % (DATA_PICKLE))
with open(DATA_PICKLE, 'rb') as file:
df = pickle.load(file)
lwe = pickle.load(file)
pmat = pickle.load(file)
pcore_indices = pickle.load(file)
pcor = pickle.load(file)
lfdr_pcor = pickle.load(file)
#prob = pickle.load(file)
else:
print("reading in data from %s" % (FILENAME))
df = pd.read_csv(FILENAME, sep='\t')
print("found %d rows and %d columns" % (df.shape[0], df.shape[1]))
# compute the row means and sort the data frame by descinding means
df['row_means'] = df.mean(axis=1)
df.sort_values('row_means', axis=0, ascending=False, inplace=True)
df.drop('row_means', axis=1, inplace=True)
# take the most abundant genes
df = df.head(PRUNE_GENES)
# Ledoit-Wolf optimal shrinkage coefficient estimate
print("computing Ledoit-Wolf optimal shrinkage coeffecient estimate")
lwe = LedoitWolf().fit(df.transpose())
pmat = lwe.get_precision()
# Convert symmetric matrix to array, first by getting indices
# of the off diagonal elements, second by pulling them into
# separate array (pcor).
print("extracting off diagnol elements of precision matrix")
pcor_indices = np.triu_indices(pmat.shape[0], 1)
pcor = pmat[pcor_indices]
# Determine edges by computing lfdr of pcor.
print("computing lfdr of partial correlations")
fdrtool = importr('fdrtool')
lfdr_pcor = fdrtool.fdrtool(FloatVector(pcor), statistic="correlation", plot=False)
#prob = 1-lfdr_pcor['lfdr']
with open(DATA_PICKLE, 'wb') as file:
pickle.dump(df, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(lwe, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(pmat, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(pcor_indices, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(pcor, file, pickle.HIGHEST_PROTOCOL)
pickle.dump(lfdr_pcor, file, pickle.HIGHEST_PROTOCOL)
#pickle.dump(prob, file, pickle.HIGHEST_PROTOCOL)
print("making 1-lfdr vs. pcor plot")
prob = 1-np.array(lfdr_pcor.rx2('lfdr'))
with PdfPages(PDF_FILENAME) as pdf:
plt.figure(figsize=(3, 3))
plt.plot(range(7), [3, 1, 4, 1, 5, 9, 2], 'r-o')
plt.title('Page One')
pdf.savefig() # saves the current figure into a pdf page
plt.close()
plt.plot(pcor[0:10000:10], prob[0:10000:10], 'o', markeredgecolor='k', markersize=3)
plt.title("THIS IS A PLOT TITLE, YOU BET")
plt.xlabel('partial correlation')
plt.ylabel('lfdr')
pdf.savefig
plt.close()