diff options
| -rw-r--r-- | tree.py | 95 |
1 files changed, 47 insertions, 48 deletions
@@ -1,10 +1,10 @@ import numpy as np -# import cProfile -# import pstats +import cProfile +import pstats # import tqdm # from tqdm import trange -# from pstats import SortKey +from pstats import SortKey from sklearn import metrics # age,married,house,income,gender,class @@ -88,7 +88,6 @@ class Tree: # # De index van de row van x die we in de boom willen droppen drop = 0 - node = nodes[0] while nodes.size != 0: node = nodes[0] if node.col is None: @@ -368,44 +367,44 @@ if __name__ == '__main__': delimiter=',', skip_header=True) - print("\nDataset: credit data") - tree_pred(x=credit_data[:, :5], - tr=tree_grow(x=credit_data[:, 0:5], - y=credit_data[:, 5], - nmin=2, - minleaf=1, - nfeat=5), - training=credit_data[:, 5]) - - print("\nDataset: credit data") - tree_pred_b(x=credit_data[:, :5], - tr=tree_grow_b(x=credit_data[:, 0:5], - y=credit_data[:, 5], - nmin=2, - minleaf=1, - nfeat=4, - m=50), - training=credit_data[:, 5]) - - print('\nDataset: pima indians') - tree_pred(x=pima_indians[:, :8], - tr=tree_grow(x=pima_indians[:, :8], - y=pima_indians[:, 8], - nmin=20, - minleaf=5, - nfeat=pima_indians.shape[1] - 1), - training=pima_indians[:, 8]) - - - print('\nDataset: pima indians') - tree_pred_b(x=pima_indians[:, :8], - tr=tree_grow_b(x=pima_indians[:, :8], - y=pima_indians[:, 8], - nmin=20, - minleaf=5, - nfeat=4, - m=5), - training=pima_indians[:, 8]) + # print("\nDataset: credit data") + # tree_pred(x=credit_data[:, :5], + # tr=tree_grow(x=credit_data[:, 0:5], + # y=credit_data[:, 5], + # nmin=2, + # minleaf=1, + # nfeat=5), + # training=credit_data[:, 5]) + + # print("\nDataset: credit data") + # tree_pred_b(x=credit_data[:, :5], + # tr=tree_grow_b(x=credit_data[:, 0:5], + # y=credit_data[:, 5], + # nmin=2, + # minleaf=1, + # nfeat=4, + # m=50), + # training=credit_data[:, 5]) + + # print('\nDataset: pima indians') + # tree_pred(x=pima_indians[:, :8], + # tr=tree_grow(x=pima_indians[:, :8], + # y=pima_indians[:, 8], + # nmin=20, + # minleaf=5, + # nfeat=pima_indians.shape[1] - 1), + # training=pima_indians[:, 8]) + + + # print('\nDataset: pima indians') + # tree_pred_b(x=pima_indians[:, :8], + # tr=tree_grow_b(x=pima_indians[:, :8], + # y=pima_indians[:, 8], + # nmin=20, + # minleaf=5, + # nfeat=4, + # m=5), + # training=pima_indians[:, 8]) @@ -416,10 +415,10 @@ if __name__ == '__main__': # Time profile of pima indians data prediction with single tree # print("prediction metrics single tree pima indians:") - # cProfile.run( - # "tree_pred_b(x=pima_indians[:, :8], tr=tree_grow_b(x=pima_indians[:, :8], y=pima_indians[:, 8], nmin=20, minleaf=5, nfeat=4, m=5), training=pima_indians[:, 8])", - # 'restats') + cProfile.run( + "tree_pred_b(x=pima_indians[:, :8], tr=tree_grow_b(x=pima_indians[:, :8], y=pima_indians[:, 8], nmin=20, minleaf=5, nfeat=4, m=5), training=pima_indians[:, 8])", + 'restats') - # p = pstats.Stats('restats') - # p.sort_stats(SortKey.TIME) - # p.print_stats() + p = pstats.Stats('restats') + p.sort_stats(SortKey.TIME) + p.print_stats() |