gcForest.cascade_forest() - Code Metrics - Inspection of "Update LSTM/SDA training examples" - TinghuiWang/pyActLearn - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 391e01...d85813 )

by Tinghui

created 2018-03-13 15:37 UTC

gcForest.cascade_forest() C

↳ Parent: gcForest

Complexity

Conditions

Size

Total Lines

Duplication

Lines	0
Ratio	0 %

Importance

Changes	1
Bugs	0	Features	0

Metric	Value
cc	8
c	1
b	0
f	0
dl	0
loc	59
rs	5.8915

How to fix Long Method

#!usr/bin/env python
import itertools
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score


class gcForest(object):
    max_acc = 0.0
    max_pred_layer = []

    def __init__(self, n_mgsRFtree=30, cascade_test_size=0.2, n_cascadeRF=2,
                 n_cascadeRFtree=101, cascade_layer=np.inf,
                 min_samples_cascade=0.05, tolerance=0.0):
        setattr(self, 'n_layer', 0)
        setattr(self, '_n_samples', 0)
        setattr(self, 'n_cascadeRF', int(n_cascadeRF))
        setattr(self, 'cascade_test_size', cascade_test_size)
        setattr(self, 'n_mgsRFtree', int(n_mgsRFtree))
        setattr(self, 'n_cascadeRFtree', int(n_cascadeRFtree))
        setattr(self, 'cascade_layer', cascade_layer)
        setattr(self, 'min_samples_cascade', min_samples_cascade)
        setattr(self, 'tolerance', tolerance)

    def fit(self, X, y):
        _ = self.cascade_forest(X, y)

    def predict_proba(self, X):
        cascade_all_pred_prob = self.cascade_forest(X)
        predict_proba = np.mean(cascade_all_pred_prob, axis=0)
        return predict_proba

    def predict(self, X):
        pred_proba = self.predict_proba(X=X)
        predictions = np.argmax(pred_proba, axis=1)
        return predictions

    def cascade_forest(self, X, y=None):
        if y is not None:
            setattr(self, 'n_layer', 0)
            test_size = getattr(self, 'cascade_test_size')
            max_layers = getattr(self, 'cascade_layer')
            tol = getattr(self, 'tolerance')
            # test_size = int(np.floor(X.shape[0] * test_size))
            # train_size = X.shape[0] - test_size
            # X_train = X[0:train_size, :]
            # y_train = y[0:train_size]
            # X_test = X[train_size:train_size + test_size, :]
            # y_test = y[train_size:train_size + test_size]
            # X_train, X_test, y_train, y_test = \
            #     train_test_split(X, y, test_size=test_size)
            X_train = X
            X_test = X
            y_train = y
            y_test = y
            self.n_layer += 1
            prf_pred_ref = self._cascade_layer(X_train, y_train)
            accuracy_ref = self._cascade_evaluation(X_test, y_test)
            feat_arr = self._create_feat_arr(X_train, prf_pred_ref)

            self.n_layer += 1
            prf_pred_layer = self._cascade_layer(feat_arr, y_train)
            accuracy_layer = self._cascade_evaluation(X_test, y_test)
            max_acc = accuracy_ref
            max_pred_layer = prf_pred_layer

            while accuracy_layer > (accuracy_ref + tol) and self.n_layer <= max_layers:
            #while accuracy_layer > (accuracy_ref - 0.000001) and \
            #    self.n_layer <= max_layers:
                if accuracy_layer > max_acc:
                    max_acc = accuracy_layer
                    max_pred_layer = prf_pred_layer
                    accuracy_ref = accuracy_layer
                    prf_pred_ref = prf_pred_layer
                    feat_arr = self._create_feat_arr(X_train, prf_pred_ref)
                    self.n_layer += 1
                    prf_pred_layer = self._cascade_layer(feat_arr, y_train)
                    accuracy_layer = self._cascade_evaluation(X_test, y_test)

                if accuracy_layer < accuracy_ref:
                    n_cascadeRF = getattr(self, 'n_cascadeRF')
                    for irf in range(n_cascadeRF):
                        delattr(self, '_casprf{}_{}'.format(self.n_layer, irf))
                        delattr(self, '_cascrf{}_{}'.format(self.n_layer, irf))
                    self.n_layer -= 1

            print("layer %d - accuracy %f ref %f" % (self.n_layer, accuracy_layer, accuracy_ref))
        else:
            at_layer = 1
            prf_pred_ref = self._cascade_layer(X, layer=at_layer)
            while at_layer < getattr(self, 'n_layer'):
                at_layer += 1
                feat_arr = self._create_feat_arr(X, prf_pred_ref)
                prf_pred_ref = self._cascade_layer(feat_arr, layer=at_layer)

        return prf_pred_ref

    def _cascade_layer(self, X, y=None, layer=0):
        n_tree = getattr(self, 'n_cascadeRFtree')
        n_cascadeRF = getattr(self, 'n_cascadeRF')
        min_samples = getattr(self, 'min_samples_cascade')

        prf = RandomForestClassifier(
            n_estimators=100, max_features=8,
            bootstrap=True, criterion="entropy", min_samples_split=20,
            max_depth=None, class_weight='balanced', oob_score=True)
        crf = ExtraTreesClassifier(
            n_estimators=100, max_depth=None,
            bootstrap=True, oob_score=True)

        prf_pred = []
        if y is not None:
            # print('Adding/Training Layer, n_layer={}'.format(self.n_layer))
            for irf in range(n_cascadeRF):
                prf.fit(X, y)
                crf.fit(X, y)
                setattr(self, '_casprf{}_{}'.format(self.n_layer, irf), prf)
                setattr(self, '_cascrf{}_{}'.format(self.n_layer, irf), crf)
                probas = prf.oob_decision_function_
                probas += crf.oob_decision_function_
                prf_pred.append(probas)
        elif y is None:
            for irf in range(n_cascadeRF):
                prf = getattr(self, '_casprf{}_{}'.format(layer, irf))
                crf = getattr(self, '_cascrf{}_{}'.format(layer, irf))
                probas = prf.predict_proba(X)
                probas += crf.predict_proba(X)
                prf_pred.append(probas)

        return prf_pred

    def _cascade_evaluation(self, X_test, y_test):
        casc_pred_prob = np.mean(self.cascade_forest(X_test), axis=0)
        casc_pred = np.argmax(casc_pred_prob, axis=1)
        casc_accuracy = accuracy_score(y_true=y_test, y_pred=casc_pred)
        #print('Layer validation accuracy = {}'.format(casc_accuracy))

        return casc_accuracy

    def _create_feat_arr(self, X, prf_pred):
        swap_pred = np.swapaxes(prf_pred, 0, 1)
        add_feat = swap_pred.reshape([np.shape(X)[0], -1])
        feat_arr = np.concatenate([add_feat, X], axis=1)

        return feat_arr


1			#!usr/bin/env python
2			import itertools
3			import numpy as np
4			from sklearn.ensemble import RandomForestClassifier
5			from sklearn.ensemble import ExtraTreesClassifier
6			from sklearn.model_selection import train_test_split
7			from sklearn.metrics import accuracy_score
8
9
10			class gcForest(object):
11			max_acc = 0.0
12			max_pred_layer = []
13
14			def __init__(self, n_mgsRFtree=30, cascade_test_size=0.2, n_cascadeRF=2,
15			n_cascadeRFtree=101, cascade_layer=np.inf,
16			min_samples_cascade=0.05, tolerance=0.0):
17			setattr(self, 'n_layer', 0)
18			setattr(self, '_n_samples', 0)
19			setattr(self, 'n_cascadeRF', int(n_cascadeRF))
20			setattr(self, 'cascade_test_size', cascade_test_size)
21			setattr(self, 'n_mgsRFtree', int(n_mgsRFtree))
22			setattr(self, 'n_cascadeRFtree', int(n_cascadeRFtree))
23			setattr(self, 'cascade_layer', cascade_layer)
24			setattr(self, 'min_samples_cascade', min_samples_cascade)
25			setattr(self, 'tolerance', tolerance)
26
27			def fit(self, X, y):
28			_ = self.cascade_forest(X, y)
29
30			def predict_proba(self, X):
31			cascade_all_pred_prob = self.cascade_forest(X)
32			predict_proba = np.mean(cascade_all_pred_prob, axis=0)
33			return predict_proba
34
35			def predict(self, X):
36			pred_proba = self.predict_proba(X=X)
37			predictions = np.argmax(pred_proba, axis=1)
38			return predictions
39
40			def cascade_forest(self, X, y=None):
41			if y is not None:
42			setattr(self, 'n_layer', 0)
43			test_size = getattr(self, 'cascade_test_size')
44			max_layers = getattr(self, 'cascade_layer')
45			tol = getattr(self, 'tolerance')
46			# test_size = int(np.floor(X.shape[0] * test_size))
47			# train_size = X.shape[0] - test_size
48			# X_train = X[0:train_size, :]
49			# y_train = y[0:train_size]
50			# X_test = X[train_size:train_size + test_size, :]
51			# y_test = y[train_size:train_size + test_size]
52			# X_train, X_test, y_train, y_test = \
53			# train_test_split(X, y, test_size=test_size)
54			X_train = X
55			X_test = X
56			y_train = y
57			y_test = y
58			self.n_layer += 1
59			prf_pred_ref = self._cascade_layer(X_train, y_train)
60			accuracy_ref = self._cascade_evaluation(X_test, y_test)
61			feat_arr = self._create_feat_arr(X_train, prf_pred_ref)
62
63			self.n_layer += 1
64			prf_pred_layer = self._cascade_layer(feat_arr, y_train)
65			accuracy_layer = self._cascade_evaluation(X_test, y_test)
66			max_acc = accuracy_ref
67			max_pred_layer = prf_pred_layer
68
69			while accuracy_layer > (accuracy_ref + tol) and self.n_layer <= max_layers:
70			#while accuracy_layer > (accuracy_ref - 0.000001) and \
71			# self.n_layer <= max_layers:
72			if accuracy_layer > max_acc:
73			max_acc = accuracy_layer
74			max_pred_layer = prf_pred_layer
75			accuracy_ref = accuracy_layer
76			prf_pred_ref = prf_pred_layer
77			feat_arr = self._create_feat_arr(X_train, prf_pred_ref)
78			self.n_layer += 1
79			prf_pred_layer = self._cascade_layer(feat_arr, y_train)
80			accuracy_layer = self._cascade_evaluation(X_test, y_test)
81
82			if accuracy_layer < accuracy_ref:
83			n_cascadeRF = getattr(self, 'n_cascadeRF')
84			for irf in range(n_cascadeRF):
85			delattr(self, '_casprf{}_{}'.format(self.n_layer, irf))
86			delattr(self, '_cascrf{}_{}'.format(self.n_layer, irf))
87			self.n_layer -= 1
88
89			print("layer %d - accuracy %f ref %f" % (self.n_layer, accuracy_layer, accuracy_ref))
90			else:
91			at_layer = 1
92			prf_pred_ref = self._cascade_layer(X, layer=at_layer)
93			while at_layer < getattr(self, 'n_layer'):
94			at_layer += 1
95			feat_arr = self._create_feat_arr(X, prf_pred_ref)
96			prf_pred_ref = self._cascade_layer(feat_arr, layer=at_layer)
97
98			return prf_pred_ref
99
100			def _cascade_layer(self, X, y=None, layer=0):
101			n_tree = getattr(self, 'n_cascadeRFtree')
102			n_cascadeRF = getattr(self, 'n_cascadeRF')
103			min_samples = getattr(self, 'min_samples_cascade')
104
105			prf = RandomForestClassifier(
106			n_estimators=100, max_features=8,
107			bootstrap=True, criterion="entropy", min_samples_split=20,
108			max_depth=None, class_weight='balanced', oob_score=True)
109			crf = ExtraTreesClassifier(
110			n_estimators=100, max_depth=None,
111			bootstrap=True, oob_score=True)
112
113			prf_pred = []
114			if y is not None:
115			# print('Adding/Training Layer, n_layer={}'.format(self.n_layer))
116			for irf in range(n_cascadeRF):
117			prf.fit(X, y)
118			crf.fit(X, y)
119			setattr(self, '_casprf{}_{}'.format(self.n_layer, irf), prf)
120			setattr(self, '_cascrf{}_{}'.format(self.n_layer, irf), crf)
121			probas = prf.oob_decision_function_
122			probas += crf.oob_decision_function_
123			prf_pred.append(probas)
124			elif y is None:
125			for irf in range(n_cascadeRF):
126			prf = getattr(self, '_casprf{}_{}'.format(layer, irf))
127			crf = getattr(self, '_cascrf{}_{}'.format(layer, irf))
128			probas = prf.predict_proba(X)
129			probas += crf.predict_proba(X)
130			prf_pred.append(probas)
131
132			return prf_pred
133
134			def _cascade_evaluation(self, X_test, y_test):
135			casc_pred_prob = np.mean(self.cascade_forest(X_test), axis=0)
136			casc_pred = np.argmax(casc_pred_prob, axis=1)
137			casc_accuracy = accuracy_score(y_true=y_test, y_pred=casc_pred)
138			#print('Layer validation accuracy = {}'.format(casc_accuracy))
139
140			return casc_accuracy
141
142			def _create_feat_arr(self, X, prf_pred):
143			swap_pred = np.swapaxes(prf_pred, 0, 1)
144			add_feat = swap_pred.reshape([np.shape(X)[0], -1])
145			feat_arr = np.concatenate([add_feat, X], axis=1)
146
147			return feat_arr
148

TinghuiWang / pyActLearn

Push — master ( 391e01...d85813 )

gcForest.cascade_forest() C

Complexity

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Duplication

Importance

How to fix Long Method

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