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clf_xgboost_2_levels.py
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clf_xgboost_2_levels.py
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"""
"""
import sys
import numpy as np
from sklearn.preprocessing import LabelEncoder
#sys.path.append('/home/sandrovegapons/anaconda/src/xgboost/wrapper')
sys.path.append('E:\Competitions\OttoGroup\py_ml_utils\lib')
from xgboost import DMatrix
from clf_xgboost import Clf_xgboost, my_train_xgboost
import pdb
class Clf_xgboost_2_levels(Clf_xgboost):
"""
Simple xgboost based classifier.
"""
def __init__(self, num_classes=9):
"""
"""
self.prefix = 'xgb_2lv'
#Classifier 0
self.param0 = {}
self.param0['objective'] = 'multi:softprob'
# scale weight of positive examples
self.param0['eta'] = 0.015
self.param0['gamma'] = 0.5
self.param0['min_child_weight'] = 3.5
self.param0['max_delta_step'] = 0
self.param0['subsample'] = 0.3
self.param0['colsample_bytree'] = 0.3
self.param0['max_depth'] = 19
self.param0['silent'] = 1
self.param0['nthread'] = 7
self.param0['eval_metric'] = 'mlogloss'
self.param0['num_class'] = 2
self.num_round0 = 1200
self.w0 = [1., 1.03]
self.rt0_eta=1.00055
self.rt0_ssp=1.0007
self.rt0_clb=1.0007
self.rt0_dpt=0.998
#Classifier 1
self.param1 = {}
self.param1['objective'] = 'multi:softprob'
self.param1['eta'] = 0.01
self.param1['gamma'] = 0.7
self.param1['min_child_weight'] = 4
self.param1['subsample'] = 0.5
self.param1['max_depth'] = 17
self.param1['max_delta_step'] = 10
self.param1['colsample_bytree'] = 0.5
self.param1['silent'] = 1
self.param1['nthread'] = 7
self.param1['eval_metric'] = 'mlogloss'
self.param1['num_class'] = 3
self.num_round1 = 1800
self.w1 = [1., 1.17, 1.23]
self.rt1_eta=1.00009
self.rt1_ssp=1.0003
self.rt1_clb=1.0003
self.rt1_dpt=0.9996
#Classifier 2
self.param2 = {}
self.param2['objective'] = 'multi:softprob'
self.param2['eta'] = 0.015
self.param2['gamma'] = 0.7
self.param2['min_child_weight'] = 3
self.param2['subsample'] = 0.5
self.param2['max_depth'] = 13
self.param2['max_delta_step'] = 6
self.param2['colsample_bytree'] = 0.5
self.param2['silent'] = 1
self.param2['nthread'] = 7
self.param2['eval_metric'] = 'mlogloss'
self.param2['num_class'] = 6
self.num_round2 = 1100
self.w2 = [ 1.2, 1.2, 1., 1.2, 1.05, 1.1]
self.rt2_eta=1.00055
self.rt2_ssp=1.00055
self.rt2_clb=1.00055
self.rt2_dpt=0.9998
def train_validate(self, X_train, y_train, X_valid, y_valid):
"""
"""
#training
le = LabelEncoder()
id_123 = np.logical_or(np.logical_or(y_train==1, y_train==2),
y_train==3)
y0_train = np.zeros(len(y_train), dtype=np.int32)
y0_train[id_123] = 1
X0_train = np.copy(X_train)
y0_train = le.fit_transform(y0_train).astype(np.int32)
X1_train = X_train[id_123]
y1_train = y_train[id_123]
y1_train = le.fit_transform(y1_train).astype(np.int32)
X2_train = X_train[np.logical_not(id_123)]
y2_train = y_train[np.logical_not(id_123)]
y2_train = le.fit_transform(y2_train).astype(np.int32)
#Validation
id_123_valid = np.logical_or(np.logical_or(y_valid==1, y_valid==2),
y_valid==3)
y0_valid = np.zeros(len(y_valid), dtype=np.int32)
y0_valid[id_123_valid] = 1
X0_valid = np.copy(X_valid)
y0_valid = le.fit_transform(y0_valid).astype(np.int32)
X1_valid = X_valid[id_123_valid]
y1_valid = y_valid[id_123_valid]
y1_valid = le.fit_transform(y1_valid).astype(np.int32)
X2_valid = X_valid[np.logical_not(id_123_valid)]
y2_valid = y_valid[np.logical_not(id_123_valid)]
y2_valid = le.fit_transform(y2_valid).astype(np.int32)
xg_valid = DMatrix(X_valid)
#Classifier 0
w0_train = np.zeros(len(y0_train))
for i in range(len(w0_train)):
w0_train[i] = self.w0[int(y0_train[i])]
xg0_train = DMatrix(X0_train, label=y0_train, weight=w0_train)
xg0_valid = DMatrix(X0_valid, label=y0_valid)
watchlist0 = [(xg0_train,'train'), (xg0_valid, 'validation')]
bst0 = my_train_xgboost(self.param0, xg0_train, self.num_round0,
watchlist0, rt_eta=self.rt0_eta,
rt_ssp=self.rt0_ssp, rt_clb=self.rt0_clb,
rt_dpt=self.rt0_dpt)
y0_pred = bst0.predict(xg_valid).reshape(y_valid.shape[0], 2)
# pdb.set_trace()
#Classifier 1
w1_train = np.zeros(len(y1_train))
for i in range(len(w1_train)):
w1_train[i] = self.w1[int(y1_train[i])]
xg1_train = DMatrix(X1_train, label=y1_train, weight=w1_train)
xg1_valid = DMatrix(X1_valid, label=y1_valid)
watchlist1 = [(xg1_train,'train'), (xg1_valid, 'validation')]
bst1 = my_train_xgboost(self.param1, xg1_train, self.num_round1,
watchlist1, rt_eta=self.rt1_eta,
rt_ssp=self.rt1_ssp, rt_clb=self.rt1_clb,
rt_dpt=self.rt1_dpt)
y1_pred = bst1.predict(xg_valid).reshape(y_valid.shape[0], 3)
#Classifier 2
w2_train = np.zeros(len(y2_train))
for i in range(len(w2_train)):
w2_train[i] = self.w2[int(y2_train[i])]
xg2_train = DMatrix(X2_train, label=y2_train, weight=w2_train)
xg2_valid = DMatrix(X2_valid, label=y2_valid)
watchlist2 = [(xg2_train,'train'), (xg2_valid, 'validation')]
bst2 = my_train_xgboost(self.param2, xg2_train, self.num_round2,
watchlist2, rt_eta=self.rt2_eta,
rt_ssp=self.rt2_ssp, rt_clb=self.rt2_clb,
rt_dpt=self.rt2_dpt)
y2_pred = bst2.predict(xg_valid).reshape(y_valid.shape[0], 6)
y_pred = np.zeros((y0_pred.shape[0], 9))
y_pred[:,0] = y0_pred[:,0]*y2_pred[:,0]
y_pred[:,1] = y0_pred[:,1]*y1_pred[:,0]
y_pred[:,2] = y0_pred[:,1]*y1_pred[:,1]
y_pred[:,3] = y0_pred[:,1]*y1_pred[:,2]
y_pred[:,4] = y0_pred[:,0]*y2_pred[:,1]
y_pred[:,5] = y0_pred[:,0]*y2_pred[:,2]
y_pred[:,6] = y0_pred[:,0]*y2_pred[:,3]
y_pred[:,7] = y0_pred[:,0]*y2_pred[:,4]
y_pred[:,8] = y0_pred[:,0]*y2_pred[:,5]
return y_pred
def train_test(self, X, y, X_test):
"""
"""
#training
le = LabelEncoder()
id_123 = np.logical_or(np.logical_or(y==1, y==2),
y==3)
y0 = np.zeros(len(y), dtype=np.int32)
y0[id_123] = 1
X0 = np.copy(X)
y0 = le.fit_transform(y0).astype(np.int32)
X1 = X[id_123]
y1 = y[id_123]
y1 = le.fit_transform(y1).astype(np.int32)
X2 = X[np.logical_not(id_123)]
y2 = y[np.logical_not(id_123)]
y2 = le.fit_transform(y2).astype(np.int32)
xg_test = DMatrix(X_test)
#Classifier 0
w0_train = np.zeros(len(y0))
for i in range(len(w0_train)):
w0_train[i] = self.w0[int(y0[i])]
xg0_train = DMatrix(X0, label=y0, weight=w0_train)
bst0 = my_train_xgboost(self.param0, xg0_train, self.num_round0,
rt_eta=self.rt0_eta,
rt_ssp=self.rt0_ssp, rt_clb=self.rt0_clb,
rt_dpt=self.rt0_dpt)
y0_pred = bst0.predict(xg_test).reshape(X_test.shape[0], 2)
#Classifier 1
w1_train = np.zeros(len(y1))
for i in range(len(w1_train)):
w1_train[i] = self.w1[int(y1[i])]
xg1_train = DMatrix(X1, label=y1, weight=w1_train)
bst1 = my_train_xgboost(self.param1, xg1_train, self.num_round1,
rt_eta=self.rt1_eta,
rt_ssp=self.rt1_ssp, rt_clb=self.rt1_clb,
rt_dpt=self.rt1_dpt)
y1_pred = bst1.predict(xg_test).reshape(X_test.shape[0], 3)
#Classifier 2
w2_train = np.zeros(len(y2))
for i in range(len(w2_train)):
w2_train[i] = self.w2[int(y2[i])]
xg2_train = DMatrix(X2, label=y2, weight=w2_train)
bst2 = my_train_xgboost(self.param2, xg2_train, self.num_round2,
rt_eta=self.rt2_eta,
rt_ssp=self.rt2_ssp, rt_clb=self.rt2_clb,
rt_dpt=self.rt2_dpt)
y2_pred = bst2.predict(xg_test).reshape(X_test.shape[0], 6)
y_pred = np.zeros((y0_pred.shape[0], 9))
y_pred[:,0] = y0_pred[:,0]*y2_pred[:,0]
y_pred[:,1] = y0_pred[:,1]*y1_pred[:,0]
y_pred[:,2] = y0_pred[:,1]*y1_pred[:,1]
y_pred[:,3] = y0_pred[:,1]*y1_pred[:,2]
y_pred[:,4] = y0_pred[:,0]*y2_pred[:,1]
y_pred[:,5] = y0_pred[:,0]*y2_pred[:,2]
y_pred[:,6] = y0_pred[:,0]*y2_pred[:,3]
y_pred[:,7] = y0_pred[:,0]*y2_pred[:,4]
y_pred[:,8] = y0_pred[:,0]*y2_pred[:,5]
return y_pred