How to get correlation matrix values pyspark

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You are almost there ! There is no need to use old rdd mllib api .

This is my method to generate pandas dataframe, you can export to excel or csv or others format.

def correlation_matrix(df, corr_columns, method='pearson'):
    vector_col = "corr_features"
    assembler = VectorAssembler(inputCols=corr_columns, outputCol=vector_col)
    df_vector = assembler.transform(df).select(vector_col)
    matrix = Correlation.corr(df_vector, vector_col, method)

    result = matrix.collect()[0]["pearson({})".format(vector_col)].values
    return pd.DataFrame(result.reshape(-1, len(corr_columns)), columns=corr_columns, index=corr_columns)

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Juan David
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Juan David

Updated on July 10, 2022

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  • Juan David
    Juan David over 1 year

    I have a correlation matrix calculated as follow on pyspark 2.2:

    from pyspark.ml.linalg import Vectors
    from pyspark.ml.stat import Correlation
    from pyspark.ml.linalg import Vectors
    from pyspark.ml.feature import VectorAssembler
    
    datos = sql("""select * from proceso_riesgos.jdgc_bd_train_mn_ingresos""")
    
    Variables_corr= ['ingreso_final_mix','ingreso_final_promedio',
    'ingreso_final_mediana','ingreso_final_trimedia','ingresos_serv_q1',
    'ingresos_serv_q2','ingresos_serv_q3','prom_ingresos_serv','y_correc']
    
    assembler = VectorAssembler(
    inputCols=Variables_corr,
    outputCol="features")
    
    datos1=datos.select(Variables_corr).filter("y_correc is not null")
    output = assembler.transform(datos)
    r1 = Correlation.corr(output, "features")
    

    the result is a data frame with a variable called "pearson(features): matrix":

    Row(pearson(features)=DenseMatrix(20, 20, [1.0, 0.9428, 0.8908, 0.913, 
    0.567, 0.5832, 0.6148, 0.6488, ..., -0.589, -0.6145, -0.5906, -0.5534, 
    -0.5346, -0.0797, -0.617, 1.0], False))]
    

    I need to take those values and export it to an excel, or to be able to manipulate the result. A list could be desiderable.

    Thanks for help!!