notebook.community

Edit and run 





In [12]:



    


import pandas as pd
import numpy as np



    











In [13]:



    


timing_file = '../data/all_results_janus_single_node_1-14-17.csv'
properties_file = '../data/uflorida-features.csv'



    











In [14]:



    


timings = pd.read_csv(timing_file, header=0)
properties = pd.read_csv(properties_file, header=0)

Changing the name of the columns to more simple names for ease-of-use.





In [15]:



    


timings.columns= ['np', 'matrix', 'solver', 'prec', 'status', 'time', 'iters', 'resid']
properties.columns = ['rows', 'cols', 'min_nnz_row', 'row_var', 'col_var', 'diag_var', 'nnz', 'frob_norm', 'symm_frob_norm', 'antisymm_frob_norm', 'one_norm', 'inf_norm', 'symm_inf_norm', 'antisymm_inf_norm', 'max_nnz_row', 'trace', 'abs_trace', 'min_nnz_row', 'avg_nnz_row', 'dummy_rows', 'dummy_rows_kind', 'num_value_symm_1', 'nnz_pattern_symm_1', 'num_value_symm_2', 'nnz_pattern_symm_2', 'row_diag_dom', 'col_diag_dom', 'diag_avg', 'diag_sign', 'diag_nnz', 'lower_bw', 'upper_bw', 'row_log_val_spread', 'col_log_val_spread', 'symm', 'matrix']

Combining the two dataframes into a single dataframe called 'combined.'

Replacing the string data with numerical data.





In [16]:



    


combined = pd.merge(properties, timings)
combined.info()



    


















    






<class 'pandas.core.frame.DataFrame'>
Int64Index: 275286 entries, 0 to 275285
Data columns (total 43 columns):
rows                  275286 non-null int64
cols                  275286 non-null int64
min_nnz_row           275286 non-null int64
row_var               275286 non-null float64
col_var               275286 non-null float64
diag_var              275286 non-null float64
nnz                   275286 non-null int64
frob_norm             275286 non-null float64
symm_frob_norm        275286 non-null float64
antisymm_frob_norm    275286 non-null float64
one_norm              275286 non-null float64
inf_norm              275286 non-null float64
symm_inf_norm         275286 non-null float64
antisymm_inf_norm     275286 non-null float64
max_nnz_row           275286 non-null int64
trace                 275286 non-null float64
abs_trace             275286 non-null float64
min_nnz_row           275286 non-null int64
avg_nnz_row           275286 non-null int64
dummy_rows            275286 non-null int64
dummy_rows_kind       275286 non-null int64
num_value_symm_1      275286 non-null int64
nnz_pattern_symm_1    275286 non-null int64
num_value_symm_2      275286 non-null float64
nnz_pattern_symm_2    275286 non-null float64
row_diag_dom          275286 non-null int64
col_diag_dom          275286 non-null int64
diag_avg              275286 non-null float64
diag_sign             275286 non-null int64
diag_nnz              275286 non-null int64
lower_bw              275286 non-null int64
upper_bw              275286 non-null int64
row_log_val_spread    275286 non-null float64
col_log_val_spread    275286 non-null float64
symm                  275286 non-null int64
matrix                275286 non-null object
np                    275286 non-null int64
solver                275286 non-null object
prec                  275286 non-null object
status                275286 non-null object
time                  275286 non-null float64
iters                 156188 non-null float64
resid                 119113 non-null float64
dtypes: float64(20), int64(19), object(4)
memory usage: 92.4+ MB

















In [17]:



    


combined = combined.dropna()



    











In [18]:



    


combined['solver_num'] = combined.solver.map({'FIXED_POINT': 0, 'BICGSTAB': 1, 'MINRES': 2, 'PSEUDOBLOCK_CG': 3, 'PSEUDOBLOCK_STOCHASTIC_CG': 4, 'PSEUDOBLOCK_TFQMR': 5, 'TFQMR': 6, 'LSQR': 7, 'PSEUDOBLOCK_GMRES': 8}).astype(int)
combined['prec_num'] = combined.prec.map({'ILUT': 0, 'RILUK': 1, 'RELAXATION': 2, 'CHEBYSHEV': 3, 'NONE': 4}).astype(int)
combined['status_num'] = combined.status.map({'error': -1, 'unconverged': 0, 'converged': 1}).astype(int)

None of the above should be changed





In [19]:



    


good = combined[combined.status == 'converged']



    











In [20]:



    


good.groupby('solver').size()



    


















    
Out[20]:








solver
BICGSTAB             6935
FIXED_POINT          2147
MINRES               5241
PSEUDOBLOCK_CG       3593
PSEUDOBLOCK_GMRES    6951
PSEUDOBLOCK_TFQMR    5827
TFQMR                5707
dtype: int64

So let's see how big of a difference there is between TFQMR and P_TFQMR





In [76]:



    


values = {"TFQMR", "PSEUDOBLOCK_TFQMR"}
tfqmr = good.loc[good.solver.isin(values)]
tfqmr.solver.unique()
tfqmr = tfqmr.drop(tfqmr.columns[:36], axis=1)
tfqmr = tfqmr.drop(tfqmr.columns[-3:], axis=1)
tfqmr.info()



    


















    






<class 'pandas.core.frame.DataFrame'>
Int64Index: 11534 entries, 25 to 275273
Data columns (total 7 columns):
np        11534 non-null int64
solver    11534 non-null object
prec      11534 non-null object
status    11534 non-null object
time      11534 non-null float64
iters     11534 non-null float64
resid     11534 non-null float64
dtypes: float64(3), int64(1), object(3)
memory usage: 720.9+ KB

Shows how much difference there is between the two solver





In [77]:



    


tfqmr = tfqmr.groupby('solver')
tfqmr.describe()



    


















    
Out[77]:










  
    

      
      
      iters
      np
      resid
      time
    

    

      solver
      
      
      
      
      
    

  
  
    

      PSEUDOBLOCK_TFQMR
      count
      5827.000000
      5827.000000
      5.827000e+03
      5827.000000
    

    

      mean
      669.441737
      5.921229
      5.432610e-07
      1.237965
    

    

      std
      1574.980811
      3.809055
      3.468492e-07
      7.532847
    

    

      min
      1.000000
      1.000000
      4.560000e-33
      0.000746
    

    

      25%
      13.000000
      2.000000
      2.220000e-07
      0.018921
    

    

      50%
      91.000000
      6.000000
      6.000000e-07
      0.085300
    

    

      75%
      382.000000
      10.000000
      8.735000e-07
      0.426127
    

    

      max
      9959.000000
      12.000000
      1.000000e-06
      271.173000
    

    

      TFQMR
      count
      5707.000000
      5707.000000
      5.707000e+03
      5707.000000
    

    

      mean
      669.346066
      5.915017
      5.414734e-07
      1.134524
    

    

      std
      1583.168929
      3.828605
      3.471924e-07
      7.684904
    

    

      min
      1.000000
      1.000000
      4.560000e-33
      0.000563
    

    

      25%
      13.000000
      2.000000
      2.190000e-07
      0.013752
    

    

      50%
      92.000000
      6.000000
      5.980000e-07
      0.062503
    

    

      75%
      387.000000
      10.000000
      8.720000e-07
      0.306544
    

    

      max
      9959.000000
      12.000000
      1.000000e-06
      288.867000
    

  




















In [ ]:

Content source: patemotter/trilinos-prediction

Similar notebooks:

notebook.community | gallery | about