Python sklearn.preprocessing.robust_scale() Examples

The following are 4 code examples of sklearn.preprocessing.robust_scale(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module sklearn.preprocessing , or try the search function .
Example #1
Source File: stats_test.py    From bayesmark with Apache License 2.0 5 votes vote down vote up 
def test_robust_standardize_to_sklearn(args):
    X, q_level = args

    q0, q1 = 0.5 * (1.0 - q_level), 0.5 * (1.0 + q_level)
    assert close_enough(q1 - q0, q_level)

    X_bo = stats.robust_standardize(X, q_level=q_level)

    X = X[:, None]
    X_skl = robust_scale(X, axis=0, with_centering=True, with_scaling=True, quantile_range=[100.0 * q0, 100.0 * q1])
    X_skl = X_skl[:, 0] * (sst.norm.ppf(q1) - sst.norm.ppf(q0))

    assert close_enough(X_bo, X_skl, equal_nan=True)
Example #2
Source File: neural_data.py    From neural-finance with Apache License 2.0 5 votes vote down vote up 
def sk_robust(X):
    return robust_scale(X)
Example #3
Source File: rasterPTSextraction_statistic_poi.py    From python-urbanPlanning with MIT License 5 votes vote down vote up 
def loopCalculate(xyzArray,eps,fn):
    robustScaleList=[]
    totalNumber=[]
    CTableDic={}
    partialCorrelationsList=[]
    counter=0
    
    #逐一计算所有距离的聚类
    for i in eps:        
        pred,predLable=affinityPropagationForPoints(xyzArray,i) #聚类计算，返回预测值及簇类标

        pt_lyrName_w=r'%s_POI'%i #字符串格式化输出文件名
        point2Shp(dataBunch,pred,fn,pt_lyrName_w) 
        print("%s has been written to disk"%i)

        counterData=Counter(pred)   #聚类簇类标频数统计
#        print(counterData)
        counterValue=np.array(list(counterData.values()))
        cvFloat=counterValue.astype(float)
        robustScale=preprocessing.robust_scale(cvFloat.reshape(-1,1))  #如果数据中含有异常值，那么使用均值和方差缩放数据的效果并不好，因此用preprocessing.robust_scale()缩放带有outlier的数据 
        cvF=robustScale.ravel() #展平，注意numpy的ravel() 和 flatten()函数的区别
        robustScaleList.append(cvF)        
        totalNumber.append(len(predLable)) #预测类标的数量
        
        CTable,partial_correlations=contingencyTableChi2andPOISpaceStructure(dataBunch,pred,class_mapping,predLable,pt_lyrName_w) #返回列联表与偏相关分析
        CTableDic[counter]=CTable
        counter+=1
        partialCorrelationsList.append(partial_correlations)
        
    return robustScaleList,totalNumber,CTableDic,partialCorrelationsList #返回所有计算距离：1.缩放后的聚类簇类标频数统计 2.预测类标的数量 3.列联表 4.偏相关分析
Example #4
Source File: OSM_data_cluster.py    From python-urbanPlanning with MIT License 5 votes vote down vote up 
def loopCalculate(df_osm,epsDegree,fn,eps):
    xyzArray=pd.DataFrame({"lon": df_osm['lon'] , "lat": df_osm['lat'] }).to_numpy()
    robustScaleList=[]
    totalNumber=[]
    CTableDic={}
    partialCorrelationsList=[]
    counter=0
    
    #逐一计算所有距离的聚类
    for i in range(len(epsDegree)):        
        pred,predLable=affinityPropagationForPoints(xyzArray,epsDegree[i]) #聚类计算，返回预测值及簇类标

        pt_lyrName_w=r'%s_POI'%eps[i] #字符串格式化输出文件名
        point2Shp(df_osm,pred,fn,pt_lyrName_w) 
        print("\n%s has been written to disk"%i)
        
        counterData=Counter(pred)   #聚类簇类标频数统计
#        print(counterData)
        counterValue=np.array(list(counterData.values()))
        cvFloat=counterValue.astype(float)
        robustScale=preprocessing.robust_scale(cvFloat.reshape(-1,1))  #如果数据中含有异常值，那么使用均值和方差缩放数据的效果并不好，因此用preprocessing.robust_scale()缩放带有outlier的数据 
        cvF=robustScale.ravel() #展平，注意numpy的ravel() 和 flatten()函数的区别
        robustScaleList.append(cvF)        
        totalNumber.append(len(predLable)) #预测类标的数量       
        
    return robustScaleList,totalNumber