决策树模型可输出解释性较强的应用规则，以下示例探索python后决策树展示：

    1. import pandas as pd
    2. from sklearn.datasets import load_breast_cancer
    3. iris = load_breast_cancer()
    4. X = pd.DataFrame(iris.data, columns=['Xvar'+str(i+1)+"S" for i in range(iris.data.shape[1])])
    5. X
    6. """
    7. Xvar1S    Xvar2S    Xvar3S    Xvar4S    Xvar5S    Xvar6S    Xvar7S    Xvar8S    Xvar9S    Xvar10S    ...    Xvar21S    Xvar22S    Xvar23S    Xvar24S    Xvar25S    Xvar26S    Xvar27S    Xvar28S    Xvar29S    Xvar30S
    8. 0    17.990    10.38    122.80    1001.0    0.11840    0.27760    0.300100    0.147100    0.2419    0.07871    ...    25.380    17.33    184.60    2019.0    0.16220    0.66560    0.71190    0.26540    0.4601    0.11890
    9. 1    20.570    17.77    132.90    1326.0    0.08474    0.07864    0.086900    0.070170    0.1812    0.05667    ...    24.990    23.41    158.80    1956.0    0.12380    0.18660    0.24160    0.18600    0.2750    0.08902
    10. 2    19.690    21.25    130.00    1203.0    0.10960    0.15990    0.197400    0.127900    0.2069    0.05999    ...    23.570    25.53    152.50    1709.0    0.14440    0.42450    0.45040    0.24300    0.3613    0.08758
    11. 3    11.420    20.38    77.58    386.1    0.14250    0.28390    0.241400    0.105200    0.2597    0.09744    ...    14.910    26.50    98.87    567.7    0.20980    0.86630    0.68690    0.25750    0.6638    0.17300
    12. 4    20.290    14.34    135.10    1297.0    0.10030    0.13280    0.198000    0.104300    0.1809    0.05883    ...    22.540    16.67    152.20    1575.0    0.13740    0.20500    0.40000    0.16250    0.2364    0.07678
    13. 5    12.450    15.70    82.57    477.1    0.12780    0.17000    0.157800    0.080890    0.2087    0.07613    ...    15.470    23.75    103.40    741.6    0.17910    0.52490    0.53550    0.17410    0.3985    0.12440
    14. 6    18.250    19.98    119.60    1040.0    0.09463    0.10900    0.112700    0.074000    0.1794    0.05742    ...    22.880    27.66    153.20    1606.0    0.14420    0.25760    0.37840    0.19320    0.3063    0.08368
    15. 7    13.710    20.83    90.20    577.9    0.11890    0.16450    0.093660    0.059850    0.2196    0.07451    ...    17.060    28.14    110.60    897.0    0.16540    0.36820    0.26780    0.15560    0.3196    0.11510
    16. 8    13.000    21.82    87.50    519.8    0.12730    0.19320    0.185900    0.093530    0.2350    0.07389    ...    15.490    30.73    106.20    739.3    0.17030    0.54010    0.53900    0.20600    0.4378    0.10720
    17. 9    12.460    24.04    83.97    475.9    0.11860    0.23960    0.227300    0.085430    0.2030    0.08243    ...    15.090    40.68    97.65    711.4    0.18530    1.05800    1.10500    0.22100    0.4366    0.20750
    18. 10    16.020    23.24    102.70    797.8    0.08206    0.06669    0.032990    0.033230    0.1528    0.05697    ...    19.190    33.88    123.80    1150.0    0.11810    0.15510    0.14590    0.09975    0.2948    0.08452
    19. 11    15.780    17.89    103.60    781.0    0.09710    0.12920    0.099540    0.066060    0.1842    0.06082    ...    20.420    27.28    136.50    1299.0    0.13960    0.56090    0.39650    0.18100    0.3792    0.10480
    20. 12    19.170    24.80    132.40    1123.0    0.09740    0.24580    0.206500    0.111800    0.2397    0.07800    ...    20.960    29.94    151.70    1332.0    0.10370    0.39030    0.36390    0.17670    0.3176    0.10230
    21. 13    15.850    23.95    103.70    782.7    0.08401    0.10020    0.099380    0.053640    0.1847    0.05338    ...    16.840    27.66    112.00    876.5    0.11310    0.19240    0.23220    0.11190    0.2809    0.06287
    22. 14    13.730    22.61    93.60    578.3    0.11310    0.22930    0.212800    0.080250    0.2069    0.07682    ...    15.030    32.01    108.80    697.7    0.16510    0.77250    0.69430    0.22080    0.3596    0.14310
    23. 15    14.540    27.54    96.73    658.8    0.11390    0.15950    0.163900    0.073640    0.2303    0.07077    ...    17.460    37.13    124.10    943.2    0.16780    0.65770    0.70260    0.17120    0.4218    0.13410
    24. 16    14.680    20.13    94.74    684.5    0.09867    0.07200    0.073950    0.052590    0.1586    0.05922    ...    19.070    30.88    123.40    1138.0    0.14640    0.18710    0.29140    0.16090    0.3029    0.08216
    25. 17    16.130    20.68    108.10    798.8    0.11700    0.20220    0.172200    0.102800    0.2164    0.07356    ...    20.960    31.48    136.80    1315.0    0.17890    0.42330    0.47840    0.20730    0.3706    0.11420
    26. 18    19.810    22.15    130.00    1260.0    0.09831    0.10270    0.147900    0.094980    0.1582    0.05395    ...    27.320    30.88    186.80    2398.0    0.15120    0.31500    0.53720    0.23880    0.2768    0.07615
    27. 19    13.540    14.36    87.46    566.3    0.09779    0.08129    0.066640    0.047810    0.1885    0.05766    ...    15.110    19.26    99.70    711.2    0.14400    0.17730    0.23900    0.12880    0.2977    0.07259
    28. 20    13.080    15.71    85.63    520.0    0.10750    0.12700    0.045680    0.031100    0.1967    0.06811    ...    14.500    20.49    96.09    630.5    0.13120    0.27760    0.18900    0.07283    0.3184    0.08183
    29. 21    9.504    12.44    60.34    273.9    0.10240    0.06492    0.029560    0.020760    0.1815    0.06905    ...    10.230    15.66    65.13    314.9    0.13240    0.11480    0.08867    0.06227    0.2450    0.07773
    30. 22    15.340    14.26    102.50    704.4    0.10730    0.21350    0.207700    0.097560    0.2521    0.07032    ...    18.070    19.08    125.10    980.9    0.13900    0.59540    0.63050    0.23930    0.4667    0.09946
    31. 23    21.160    23.04    137.20    1404.0    0.09428    0.10220    0.109700    0.086320    0.1769    0.05278    ...    29.170    35.59    188.00    2615.0    0.14010    0.26000    0.31550    0.20090    0.2822    0.07526
    32. 24    16.650    21.38    110.00    904.6    0.11210    0.14570    0.152500    0.091700    0.1995    0.06330    ...    26.460    31.56    177.00    2215.0    0.18050    0.35780    0.46950    0.20950    0.3613    0.09564
    33. 25    17.140    16.40    116.00    912.7    0.11860    0.22760    0.222900    0.140100    0.3040    0.07413    ...    22.250    21.40    152.40    1461.0    0.15450    0.39490    0.38530    0.25500    0.4066    0.10590
    34. 26    14.580    21.53    97.41    644.8    0.10540    0.18680    0.142500    0.087830    0.2252    0.06924    ...    17.620    33.21    122.40    896.9    0.15250    0.66430    0.55390    0.27010    0.4264    0.12750
    35. 27    18.610    20.25    122.10    1094.0    0.09440    0.10660    0.149000    0.077310    0.1697    0.05699    ...    21.310    27.26    139.90    1403.0    0.13380    0.21170    0.34460    0.14900    0.2341    0.07421
    36. 28    15.300    25.27    102.40    732.4    0.10820    0.16970    0.168300    0.087510    0.1926    0.06540    ...    20.270    36.71    149.30    1269.0    0.16410    0.61100    0.63350    0.20240    0.4027    0.09876
    37. 29    17.570    15.05    115.00    955.1    0.09847    0.11570    0.098750    0.079530    0.1739    0.06149    ...    20.010    19.52    134.90    1227.0    0.12550    0.28120    0.24890    0.14560    0.2756    0.07919
    38. ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...    ...
    39. 539    7.691    25.44    48.34    170.4    0.08668    0.11990    0.092520    0.013640    0.2037    0.07751    ...    8.678    31.89    54.49    223.6    0.15960    0.30640    0.33930    0.05000    0.2790    0.10660
    40. 540    11.540    14.44    74.65    402.9    0.09984    0.11200    0.067370    0.025940    0.1818    0.06782    ...    12.260    19.68    78.78    457.8    0.13450    0.21180    0.17970    0.06918    0.2329    0.08134
    41. 541    14.470    24.99    95.81    656.4    0.08837    0.12300    0.100900    0.038900    0.1872    0.06341    ...    16.220    31.73    113.50    808.9    0.13400    0.42020    0.40400    0.12050    0.3187    0.10230
    42. 542    14.740    25.42    94.70    668.6    0.08275    0.07214    0.041050    0.030270    0.1840    0.05680    ...    16.510    32.29    107.40    826.4    0.10600    0.13760    0.16110    0.10950    0.2722    0.06956
    43. 543    13.210    28.06    84.88    538.4    0.08671    0.06877    0.029870    0.032750    0.1628    0.05781    ...    14.370    37.17    92.48    629.6    0.10720    0.13810    0.10620    0.07958    0.2473    0.06443
    44. 544    13.870    20.70    89.77    584.8    0.09578    0.10180    0.036880    0.023690    0.1620    0.06688    ...    15.050    24.75    99.17    688.6    0.12640    0.20370    0.13770    0.06845    0.2249    0.08492
    45. 545    13.620    23.23    87.19    573.2    0.09246    0.06747    0.029740    0.024430    0.1664    0.05801    ...    15.350    29.09    97.58    729.8    0.12160    0.15170    0.10490    0.07174    0.2642    0.06953
    46. 546    10.320    16.35    65.31    324.9    0.09434    0.04994    0.010120    0.005495    0.1885    0.06201    ...    11.250    21.77    71.12    384.9    0.12850    0.08842    0.04384    0.02381    0.2681    0.07399
    47. 547    10.260    16.58    65.85    320.8    0.08877    0.08066    0.043580    0.024380    0.1669    0.06714    ...    10.830    22.04    71.08    357.4    0.14610    0.22460    0.17830    0.08333    0.2691    0.09479
    48. 548    9.683    19.34    61.05    285.7    0.08491    0.05030    0.023370    0.009615    0.1580    0.06235    ...    10.930    25.59    69.10    364.2    0.11990    0.09546    0.09350    0.03846    0.2552    0.07920
    49. 549    10.820    24.21    68.89    361.6    0.08192    0.06602    0.015480    0.008160    0.1976    0.06328    ...    13.030    31.45    83.90    505.6    0.12040    0.16330    0.06194    0.03264    0.3059    0.07626
    50. 550    10.860    21.48    68.51    360.5    0.07431    0.04227    0.000000    0.000000    0.1661    0.05948    ...    11.660    24.77    74.08    412.3    0.10010    0.07348    0.00000    0.00000    0.2458    0.06592
    51. 551    11.130    22.44    71.49    378.4    0.09566    0.08194    0.048240    0.022570    0.2030    0.06552    ...    12.020    28.26    77.80    436.6    0.10870    0.17820    0.15640    0.06413    0.3169    0.08032
    52. 552    12.770    29.43    81.35    507.9    0.08276    0.04234    0.019970    0.014990    0.1539    0.05637    ...    13.870    36.00    88.10    594.7    0.12340    0.10640    0.08653    0.06498    0.2407    0.06484
    53. 553    9.333    21.94    59.01    264.0    0.09240    0.05605    0.039960    0.012820    0.1692    0.06576    ...    9.845    25.05    62.86    295.8    0.11030    0.08298    0.07993    0.02564    0.2435    0.07393
    54. 554    12.880    28.92    82.50    514.3    0.08123    0.05824    0.061950    0.023430    0.1566    0.05708    ...    13.890    35.74    88.84    595.7    0.12270    0.16200    0.24390    0.06493    0.2372    0.07242
    55. 555    10.290    27.61    65.67    321.4    0.09030    0.07658    0.059990    0.027380    0.1593    0.06127    ...    10.840    34.91    69.57    357.6    0.13840    0.17100    0.20000    0.09127    0.2226    0.08283
    56. 556    10.160    19.59    64.73    311.7    0.10030    0.07504    0.005025    0.011160    0.1791    0.06331    ...    10.650    22.88    67.88    347.3    0.12650    0.12000    0.01005    0.02232    0.2262    0.06742
    57. 557    9.423    27.88    59.26    271.3    0.08123    0.04971    0.000000    0.000000    0.1742    0.06059    ...    10.490    34.24    66.50    330.6    0.10730    0.07158    0.00000    0.00000    0.2475    0.06969
    58. 558    14.590    22.68    96.39    657.1    0.08473    0.13300    0.102900    0.037360    0.1454    0.06147    ...    15.480    27.27    105.90    733.5    0.10260    0.31710    0.36620    0.11050    0.2258    0.08004
    59. 559    11.510    23.93    74.52    403.5    0.09261    0.10210    0.111200    0.041050    0.1388    0.06570    ...    12.480    37.16    82.28    474.2    0.12980    0.25170    0.36300    0.09653    0.2112    0.08732
    60. 560    14.050    27.15    91.38    600.4    0.09929    0.11260    0.044620    0.043040    0.1537    0.06171    ...    15.300    33.17    100.20    706.7    0.12410    0.22640    0.13260    0.10480    0.2250    0.08321
    61. 561    11.200    29.37    70.67    386.0    0.07449    0.03558    0.000000    0.000000    0.1060    0.05502    ...    11.920    38.30    75.19    439.6    0.09267    0.05494    0.00000    0.00000    0.1566    0.05905
    62. 562    15.220    30.62    103.40    716.9    0.10480    0.20870    0.255000    0.094290    0.2128    0.07152    ...    17.520    42.79    128.70    915.0    0.14170    0.79170    1.17000    0.23560    0.4089    0.14090
    63. 563    20.920    25.09    143.00    1347.0    0.10990    0.22360    0.317400    0.147400    0.2149    0.06879    ...    24.290    29.41    179.10    1819.0    0.14070    0.41860    0.65990    0.25420    0.2929    0.09873
    64. 564    21.560    22.39    142.00    1479.0    0.11100    0.11590    0.243900    0.138900    0.1726    0.05623    ...    25.450    26.40    166.10    2027.0    0.14100    0.21130    0.41070    0.22160    0.2060    0.07115
    65. 565    20.130    28.25    131.20    1261.0    0.09780    0.10340    0.144000    0.097910    0.1752    0.05533    ...    23.690    38.25    155.00    1731.0    0.11660    0.19220    0.32150    0.16280    0.2572    0.06637
    66. 566    16.600    28.08    108.30    858.1    0.08455    0.10230    0.092510    0.053020    0.1590    0.05648    ...    18.980    34.12    126.70    1124.0    0.11390    0.30940    0.34030    0.14180    0.2218    0.07820
    67. 567    20.600    29.33    140.10    1265.0    0.11780    0.27700    0.351400    0.152000    0.2397    0.07016    ...    25.740    39.42    184.60    1821.0    0.16500    0.86810    0.93870    0.26500    0.4087    0.12400
    68. 568    7.760    24.54    47.92    181.0    0.05263    0.04362    0.000000    0.000000    0.1587    0.05884    ...    9.456    30.37    59.16    268.6    0.08996    0.06444    0.00000    0.00000    0.2871    0.07039
    69. 569 rows × 30 columns
    70. """
    72. Y = pd.DataFrame(iris.target, columns=['Y'])
    73. Y.T
    74. """
    75. 0    1    2    3    4    5    6    7    8    9    ...    559    560    561    562    563    564    565    566    567    568
    76. Y    0    0    0    0    0    0    0    0    0    0    ...    1    1    1    0    0    0    0    0    0    1
    77. 1 rows × 569 columns
    78. """
    80. from sklearn.cross_validation import train_test_split
    81. X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3)
    82. X_train.shape, X_test.shape, Y_train.shape, Y_test.shape
    83. """
    84. /data/soft/py3/lib/python3.6/site-packages/sklearn/cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
    85.   "This module will be removed in 0.20.", DeprecationWarning)
    86. ((398, 30), (171, 30), (398, 1), (171, 1))
    87. """
    89. from sklearn import tree
    90. from sklearn import metrics
    91. import numpy as np
    92. model = tree.DecisionTreeClassifier(min_samples_leaf=50)
    93. model.fit(X_train, Y_train)
    94. with open("013.Test_DecisionTreeClassifier_var.result.dot", "w") as f:
    95.     tree.export_graphviz(model, f)
    97. ! cat "013.Test_DecisionTreeClassifier_var.result.dot"
    98. """
    99. digraph Tree {
    100. node [shape=box] ;
    101. 0 [label="X[27] <= 0.142\ngini = 0.472\nsamples = 398\nvalue = [152, 246]"] ;
    102. 1 [label="X[20] <= 15.68\ngini = 0.15\nsamples = 258\nvalue = [21, 237]"] ;
    103. 0 -> 1 [labeldistance=2.5, labelangle=45, headlabel="True"] ;
    104. 2 [label="X[15] <= 0.012\ngini = 0.028\nsamples = 208\nvalue = [3, 205]"] ;
    105. 1 -> 2 ;
    106. 3 [label="gini = 0.105\nsamples = 54\nvalue = [3, 51]"] ;
    107. 2 -> 3 ;
    108. 4 [label="gini = 0.0\nsamples = 154\nvalue = [0, 154]"] ;
    109. 2 -> 4 ;
    110. 5 [label="gini = 0.461\nsamples = 50\nvalue = [18, 32]"] ;
    111. 1 -> 5 ;
    112. 6 [label="X[0] <= 16.1\ngini = 0.12\nsamples = 140\nvalue = [131, 9]"] ;
    113. 0 -> 6 [labeldistance=2.5, labelangle=-45, headlabel="False"] ;
    114. 7 [label="gini = 0.295\nsamples = 50\nvalue = [41, 9]"] ;
    115. 6 -> 7 ;
    116. 8 [label="gini = 0.0\nsamples = 90\nvalue = [90, 0]"] ;
    117. 6 -> 8 ;
    118. }
    119. """
    121. col = X_train.columns
    122. for i in range(len(model.tree_.threshold)):
    124.     if model.tree_.feature[i] == -2:
    125.         print(model.tree_.feature[i])
    126.     else:
    127.         print(col[model.tree_.feature[i]],model.tree_.feature[i],model.tree_.threshold[i], model.tree_.impurity[i])
    128. """
    129. Xvar28S 27 0.1423499882221222 0.47210929016944014
    130. Xvar21S 20 15.680000305175781 0.1495402920497566
    131. Xvar16S 15 0.012025000527501106 0.02843010355029585
    132. -2
    133. -2
    134. -2
    135. Xvar1S 0 16.099998474121094 0.12030612244897965
    136. -2
    137. -2
    138. """
    140. rule = []
    141. arule = {}
    142. r = []
    143. with open("013.Test_DecisionTreeClassifier_var.result.dot", "r") as f:
    144.     for i in f:
    145.         i = i.strip('\n')
    146.         if "digraph Tree {" in i or "node [shape=box] ;" in i or "}" in i:
    147.             pass
    148.         elif "->" in i:
    149.             if "[" in i:
    150.                 i = i[:i.find("[")]
    151.             i = i.replace(" ", "").replace(";", "")
    152.             [i0, i1] = i.split("->")
    153.             i0, i1 = int(i0), int(i1) 
    154.             i2 = 1 if i1 == (i0 + 1) else 0
    155.             rule.append([i0, i1, i2])
    156.         else:
    157.             ii = i[i.find('[label="')+len('[label="'):i.find("\\n")]
    158.             if "gini" in ii:
    159.                 ii = ""
    160.             arule[int(i.split(" ")[0])] = ii
    162.         if 'label="gini =' in i:
    163.             t0 = int(i.split(' ')[0])
    164.             t = i[i.find("nvalue ="):].replace(']"] ;','').replace('nvalue = [',"")
    165.             [t1, t2] = t.replace('\n', "").split(",")
    166.             t1, t2 = int(t1), int(t2)
    167.             r.append([t0, t1, t2, t2/(t1+t2)*100])
    169. print(rule, arule)
    171. vrule = arule
    172. for i, j, k in rule:
    173.     t = arule[i]
    174.     if k == 1:
    175.         pass
    176.     else:
    177.         t = t.replace("<=", ">")
    179.     if arule[j] != "":
    180.         vrule[j] = t + " and " + arule[j]
    181.     else:
    182.         vrule[j] = t
    184. for i, j in vrule.items():
    185.     n = 0 
    186.     for k in col:
    187.         j = j.replace("X["+str(n)+"]", k)
    188.         n += 1
    189.     print(i, j)
    190. r = pd.DataFrame(r, columns=['No', 'G', 'B', 'B/(G+B)'])
    191. r.sort_values('B/(G+B)', ascending=False)
    192. """
    193. [[0, 1, 1], [1, 2, 1], [2, 3, 1], [2, 4, 0], [1, 5, 0], [0, 6, 0], [6, 7, 1], [6, 8, 0]] {0: 'X[27] <= 0.142', 1: 'X[20] <= 15.68', 2: 'X[15] <= 0.012', 3: '', 4: '', 5: '', 6: 'X[0] <= 16.1', 7: '', 8: ''}
    194. 0 Xvar28S <= 0.142
    195. 1 Xvar28S <= 0.142 and Xvar21S <= 15.68
    196. 2 Xvar28S <= 0.142 and Xvar21S <= 15.68 and Xvar16S <= 0.012
    197. 3 Xvar28S <= 0.142 and Xvar21S <= 15.68 and Xvar16S <= 0.012
    198. 4 Xvar28S > 0.142 and Xvar21S > 15.68 and Xvar16S > 0.012
    199. 5 Xvar28S > 0.142 and Xvar21S > 15.68
    200. 6 Xvar28S > 0.142 and Xvar1S <= 16.1
    201. 7 Xvar28S > 0.142 and Xvar1S <= 16.1
    202. 8 Xvar28S > 0.142 and Xvar1S > 16.1
    203. No    G    B    B/(G+B)
    204. 1    4    0    154    100.000000
    205. 0    3    3    51    94.444444
    206. 2    5    18    32    64.000000
    207. 3    7    41    9    18.000000
    208. 4    8    90    0    0.000000
    209. """
    211. Q = "Xvar24S > 884.55 and Xvar28S > 0.111 and Xvar19S > 0.016"
    212. d = pd.merge(X, Y, left_index=True, right_index=True)
    213. d = d.query(Q)
    214. pd.value_counts(d['Y'], normalize=True)
    215. """
    216. 0    0.99
    217. 1    0.01
    218. Name: Y, dtype: float64
    219. """
    220. d = pd.merge(X_train, Y_train, left_index=True, right_index=True)
    221. d = d.query(Q)
    222. pd.value_counts(d['Y'], normalize=True)
    223. """
    224. 0    0.986486
    225. 1    0.013514
    226. Name: Y, dtype: float64
    227. """
    228. d = pd.merge(X_test, Y_test, left_index=True, right_index=True)
    229. d = d.query(Q)
    230. pd.value_counts(d['Y'], normalize=True)
    231. """
    232. 0    1.0
    233. Name: Y, dtype: float64
    234. """