How to compute Spearman correlation in Tensorflow












1















Problem



I need to compute the Pearson and Spearman correlations, and use it as metrics in tensorflow.



For Pearson, it's trivial :



tf.contrib.metrics.streaming_pearson_correlation(y_pred, y_true)


But for Spearman, I am clueless !



What I tried :



From this answer :



    samples = 1
predictions_rank = tf.nn.top_k(y_pred, k=samples, sorted=True, name='prediction_rank').indices
real_rank = tf.nn.top_k(y_true, k=samples, sorted=True, name='real_rank').indices
rank_diffs = predictions_rank - real_rank
rank_diffs_squared_sum = tf.reduce_sum(rank_diffs * rank_diffs)
six = tf.constant(6)
one = tf.constant(1.0)
numerator = tf.cast(six * rank_diffs_squared_sum, dtype=tf.float32)
divider = tf.cast(samples * samples * samples - samples, dtype=tf.float32)
spearman_batch = one - numerator / divider


But this return NaN...





Following the definition of Wikipedia :
enter image description here



I tried :



size = tf.size(y_pred)
indice_of_ranks_pred = tf.nn.top_k(y_pred, k=size)[1]
indice_of_ranks_label = tf.nn.top_k(y_true, k=size)[1]
rank_pred = tf.nn.top_k(-indice_of_ranks_pred, k=size)[1]
rank_label = tf.nn.top_k(-indice_of_ranks_label, k=size)[1]
rank_pred = tf.to_float(rank_pred)
rank_label = tf.to_float(rank_label)
spearman = tf.contrib.metrics.streaming_pearson_correlation(rank_pred, rank_label)


But running this I got the following error :




tensorflow.python.framework.errors_impl.InvalidArgumentError: input
must have at least k columns. Had 1, needed 32



[[{{node metrics/spearman/TopKV2}} = TopKV2[T=DT_FLOAT, sorted=true,
_device="/job:localhost/replica:0/task:0/device:CPU:0"](lambda_1/add, metrics/pearson/pearson_r/variance_predictions/Size)]]











share|improve this question



























    1















    Problem



    I need to compute the Pearson and Spearman correlations, and use it as metrics in tensorflow.



    For Pearson, it's trivial :



    tf.contrib.metrics.streaming_pearson_correlation(y_pred, y_true)


    But for Spearman, I am clueless !



    What I tried :



    From this answer :



        samples = 1
    predictions_rank = tf.nn.top_k(y_pred, k=samples, sorted=True, name='prediction_rank').indices
    real_rank = tf.nn.top_k(y_true, k=samples, sorted=True, name='real_rank').indices
    rank_diffs = predictions_rank - real_rank
    rank_diffs_squared_sum = tf.reduce_sum(rank_diffs * rank_diffs)
    six = tf.constant(6)
    one = tf.constant(1.0)
    numerator = tf.cast(six * rank_diffs_squared_sum, dtype=tf.float32)
    divider = tf.cast(samples * samples * samples - samples, dtype=tf.float32)
    spearman_batch = one - numerator / divider


    But this return NaN...





    Following the definition of Wikipedia :
    enter image description here



    I tried :



    size = tf.size(y_pred)
    indice_of_ranks_pred = tf.nn.top_k(y_pred, k=size)[1]
    indice_of_ranks_label = tf.nn.top_k(y_true, k=size)[1]
    rank_pred = tf.nn.top_k(-indice_of_ranks_pred, k=size)[1]
    rank_label = tf.nn.top_k(-indice_of_ranks_label, k=size)[1]
    rank_pred = tf.to_float(rank_pred)
    rank_label = tf.to_float(rank_label)
    spearman = tf.contrib.metrics.streaming_pearson_correlation(rank_pred, rank_label)


    But running this I got the following error :




    tensorflow.python.framework.errors_impl.InvalidArgumentError: input
    must have at least k columns. Had 1, needed 32



    [[{{node metrics/spearman/TopKV2}} = TopKV2[T=DT_FLOAT, sorted=true,
    _device="/job:localhost/replica:0/task:0/device:CPU:0"](lambda_1/add, metrics/pearson/pearson_r/variance_predictions/Size)]]











    share|improve this question

























      1












      1








      1








      Problem



      I need to compute the Pearson and Spearman correlations, and use it as metrics in tensorflow.



      For Pearson, it's trivial :



      tf.contrib.metrics.streaming_pearson_correlation(y_pred, y_true)


      But for Spearman, I am clueless !



      What I tried :



      From this answer :



          samples = 1
      predictions_rank = tf.nn.top_k(y_pred, k=samples, sorted=True, name='prediction_rank').indices
      real_rank = tf.nn.top_k(y_true, k=samples, sorted=True, name='real_rank').indices
      rank_diffs = predictions_rank - real_rank
      rank_diffs_squared_sum = tf.reduce_sum(rank_diffs * rank_diffs)
      six = tf.constant(6)
      one = tf.constant(1.0)
      numerator = tf.cast(six * rank_diffs_squared_sum, dtype=tf.float32)
      divider = tf.cast(samples * samples * samples - samples, dtype=tf.float32)
      spearman_batch = one - numerator / divider


      But this return NaN...





      Following the definition of Wikipedia :
      enter image description here



      I tried :



      size = tf.size(y_pred)
      indice_of_ranks_pred = tf.nn.top_k(y_pred, k=size)[1]
      indice_of_ranks_label = tf.nn.top_k(y_true, k=size)[1]
      rank_pred = tf.nn.top_k(-indice_of_ranks_pred, k=size)[1]
      rank_label = tf.nn.top_k(-indice_of_ranks_label, k=size)[1]
      rank_pred = tf.to_float(rank_pred)
      rank_label = tf.to_float(rank_label)
      spearman = tf.contrib.metrics.streaming_pearson_correlation(rank_pred, rank_label)


      But running this I got the following error :




      tensorflow.python.framework.errors_impl.InvalidArgumentError: input
      must have at least k columns. Had 1, needed 32



      [[{{node metrics/spearman/TopKV2}} = TopKV2[T=DT_FLOAT, sorted=true,
      _device="/job:localhost/replica:0/task:0/device:CPU:0"](lambda_1/add, metrics/pearson/pearson_r/variance_predictions/Size)]]











      share|improve this question














      Problem



      I need to compute the Pearson and Spearman correlations, and use it as metrics in tensorflow.



      For Pearson, it's trivial :



      tf.contrib.metrics.streaming_pearson_correlation(y_pred, y_true)


      But for Spearman, I am clueless !



      What I tried :



      From this answer :



          samples = 1
      predictions_rank = tf.nn.top_k(y_pred, k=samples, sorted=True, name='prediction_rank').indices
      real_rank = tf.nn.top_k(y_true, k=samples, sorted=True, name='real_rank').indices
      rank_diffs = predictions_rank - real_rank
      rank_diffs_squared_sum = tf.reduce_sum(rank_diffs * rank_diffs)
      six = tf.constant(6)
      one = tf.constant(1.0)
      numerator = tf.cast(six * rank_diffs_squared_sum, dtype=tf.float32)
      divider = tf.cast(samples * samples * samples - samples, dtype=tf.float32)
      spearman_batch = one - numerator / divider


      But this return NaN...





      Following the definition of Wikipedia :
      enter image description here



      I tried :



      size = tf.size(y_pred)
      indice_of_ranks_pred = tf.nn.top_k(y_pred, k=size)[1]
      indice_of_ranks_label = tf.nn.top_k(y_true, k=size)[1]
      rank_pred = tf.nn.top_k(-indice_of_ranks_pred, k=size)[1]
      rank_label = tf.nn.top_k(-indice_of_ranks_label, k=size)[1]
      rank_pred = tf.to_float(rank_pred)
      rank_label = tf.to_float(rank_label)
      spearman = tf.contrib.metrics.streaming_pearson_correlation(rank_pred, rank_label)


      But running this I got the following error :




      tensorflow.python.framework.errors_impl.InvalidArgumentError: input
      must have at least k columns. Had 1, needed 32



      [[{{node metrics/spearman/TopKV2}} = TopKV2[T=DT_FLOAT, sorted=true,
      _device="/job:localhost/replica:0/task:0/device:CPU:0"](lambda_1/add, metrics/pearson/pearson_r/variance_predictions/Size)]]








      python python-3.x tensorflow metrics






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      asked Nov 21 '18 at 1:58









      AstariulAstariul

      23610




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