forked from briandalessandro/DataScienceCourse
-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathClassifierBakeoff.py
More file actions
169 lines (126 loc) · 5.17 KB
/
Copy pathClassifierBakeoff.py
File metadata and controls
169 lines (126 loc) · 5.17 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
import numpy as np
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score
from sklearn.ensemble import GradientBoostingClassifier, AdaBoostClassifier, RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
def liftTable(pred, truth, b):
df = pd.DataFrame({'p':pred + np.random.rand(len(pred))*0.000001, 'y':truth})
df['b'] = b - pd.qcut(df['p'], b, labels=False)
df['n'] = np.ones(df.shape[0])
df_grp = df.groupby(['b']).sum()
base = np.sum(df_grp['y'])/float(df.shape[0])
df_grp['n_cum'] = np.cumsum(df_grp['n'])/float(df.shape[0])
df_grp['y_cum'] = np.cumsum(df_grp['y'])
df_grp['p_y_b'] = df_grp['y']/df_grp['n']
df_grp['lift_b'] = df_grp['p_y_b']/base
df_grp['cum_lift_b'] = (df_grp['y_cum']/(float(df.shape[0])*df_grp['n_cum']))/base
return df_grp
def getMetrics(preds, labels):
'''
Takes in non-binary predictions and labels and returns AUC, and several Lifts
'''
auc = roc_auc_score(labels, preds)
ltab = liftTable(preds, labels, 100)
lift1 = ltab.ix[1].cum_lift_b
lift5 = ltab.ix[5].cum_lift_b
lift10 = ltab.ix[10].cum_lift_b
lift25 = ltab.ix[25].cum_lift_b
return [auc, lift1, lift5, lift10, lift25]
def dToString(d, dm1, dm2):
'''
Takes key-values and makes a string, d1 seprates k:v, d2 separates pairs
'''
arg_str = ''
for k in sorted(d.keys()):
if len(arg_str) == 0:
arg_str = '{}{}{}'.format(k, dm1, d[k])
else:
arg_str = arg_str + '{}{}{}{}'.format(dm2, k, dm1, d[k])
return arg_str
def getArgCombos(arg_lists):
'''
Takes every combination and returns an iterable of dicts
'''
keys = sorted(arg_lists.keys())
#Initialize the final iterable
tot = 1
for k in keys:
tot = tot * len(arg_lists[k])
iter = []
#Fill it with empty dicts
for i in range(tot):
iter.append({})
#Now fill each dictionary
kpass = 1
for k in keys:
klist = arg_lists[k]
ktot = len(klist)
for i in range(tot):
iter[i][k] = klist[(i/kpass) % ktot]
kpass = ktot * kpass
return iter
class LRAdaptor(object):
'''
This adapts the LogisticRegression() Classifier so that LR can be used as an init for GBT
This just overwrites the predict method to be predict_proba
'''
def __init__(self, est):
self.est = est
def predict(self, X):
return self.est.predict_proba(X)[:,1][:, np.newaxis]
def fit(self, X, y):
self.est.fit(X, y)
class GenericClassifier(object):
def __init__(self, modclass, dictargs):
self.classifier = modclass(**dictargs)
def fit(self, X, Y):
self.classifier.fit(X,Y)
def predict_proba(self, Xt):
return self.classifier.predict_proba(Xt)
class GenericClassifierOptimizer(object):
def __init__(self, classtype, arg_lists):
self.name = classtype.__name__
self.classtype = classtype
self.arg_lists = arg_lists
self.results = self._initDict()
def _initDict(self):
return {'alg':[], 'opt':[], 'auc':[], 'lift1':[], 'lift5':[], 'lift10':[], 'lift25':[]}
def _updateResDict(self, opt, perf):
self.results['alg'].append(self.name)
self.results['opt'].append(opt)
self.results['auc'].append(perf[0])
self.results['lift1'].append(perf[1])
self.results['lift5'].append(perf[2])
self.results['lift10'].append(perf[3])
self.results['lift25'].append(perf[4])
def runClassBake(self, X_train, Y_train, X_test, Y_test):
arg_loop = getArgCombos(self.arg_lists)
for d in arg_loop:
mod = GenericClassifier(self.classtype, d)
mod.fit(X_train, Y_train)
perf = getMetrics(mod.predict_proba(X_test)[:,1], Y_test)
self._updateResDict(dToString(d, ':', '|'), perf)
class ClassifierBakeoff(object):
def __init__(self, X_train, Y_train, X_test, Y_test, setup):
self.instructions = setup
self.X_train = X_train
self.Y_train = Y_train
self.X_test = X_test
self.Y_test = Y_test
self.results = self._initDict()
def _initDict(self):
return {'alg':[], 'opt':[], 'auc':[], 'lift1':[], 'lift5':[], 'lift10':[], 'lift25':[]}
def _updateResDict(self, clfr_results):
self.results['alg'] = self.results['alg'] + clfr_results['alg']
self.results['opt'] = self.results['opt'] + clfr_results['opt']
self.results['auc'] = self.results['auc'] + clfr_results['auc']
self.results['lift1'] = self.results['lift1'] + clfr_results['lift1']
self.results['lift5'] = self.results['lift5'] + clfr_results['lift5']
self.results['lift10'] = self.results['lift10'] + clfr_results['lift10']
self.results['lift25'] = self.results['lift25'] + clfr_results['lift25']
def bake(self):
for clfr in self.instructions:
classifierBake = GenericClassifierOptimizer(clfr, self.instructions[clfr])
classifierBake.runClassBake(self.X_train, self.Y_train, self.X_test, self.Y_test)
self._updateResDict(classifierBake.results)