/
transductor_train.py
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/
transductor_train.py
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'''
run this script until enough transductor models get dumped.
'enough' is any number reasonable for you.
quality increases with quantity, I dumped 300, but 1-2 is okay too
'''
import numpy as np
import pandas as pd
import misc as pt
from misc import add_features
from keras.models import Sequential
from keras.layers.core import Dense, Activation
from keras.utils import np_utils
from sklearn.preprocessing import StandardScaler
from evaluation import roc_auc_truncated, compute_ks, compute_cvm
import cPickle
from scipy.optimize import minimize
import logging
np.random.seed(1337) # for reproducibility
logger = logging.getLogger()
hdlr = logging.FileHandler(pt.transductor_log_file)
formatter = logging.Formatter('%(asctime)s %(levelname)s %(message)s')
hdlr.setFormatter(formatter)
logger.addHandler(hdlr)
logger.setLevel(logging.INFO)
def preprocess_data(X, scaler=None):
if not scaler:
scaler = StandardScaler()
scaler.fit(X[:,:-1]) # don't scale last col - prediction
X[:,:-1] = scaler.transform(X[:,:-1])
return X, scaler
def load(data_file, prediction_file, tail=None, weight=False, mass=False):
data = pd.read_csv(data_file)
data = add_features(data)
prediction = pd.read_csv(prediction_file)
data['prediction'] = prediction["prediction"]
if tail is not None:
data = data[-tail:]
# shuffle
data = data.iloc[np.random.permutation(len(data))].reset_index(drop=True)
filter_out = ['id', 'min_ANNmuon', 'production', 'mass', 'weight', 'signal']
features = list(f for f in data.columns if f not in filter_out)
X = data[features].values
y = data['signal'].values if not mass else None
w = data['weight'].values if weight else None
m = data['mass'].values if mass else None
return X, y, w, m
def create_model(input_shape):
np.random.seed(11) # for reproducibility
model = Sequential()
model.add(Dense(input_shape, 50))
model.add(Activation('tanh'))
model.add(Dense(50, 50))
model.add(Activation('tanh'))
model.add(Dense(50, 30))
model.add(Activation('tanh'))
model.add(Dense(30, 25))
model.add(Activation('tanh'))
model.add(Dense(25, 2))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
return model
def get_weights(model):
weights = model.get_weights()
return np.concatenate([x.ravel() for x in weights])
def set_weights(model, parameters):
weights = model.get_weights()
start = 0
for i,w in enumerate(weights):
size = w.size
weights[i] = parameters[start:start+size].reshape(w.shape)
start += size
model.set_weights(weights)
return model
def dump_transductor_model(model, transductor_model_file):
with open(transductor_model_file, 'wb') as fid:
cPickle.dump(model, fid)
def create_objective(model, transductor_model_file, X, y, Xa, ya, wa, Xc, mc,
ks_threshold=0.09, cvm_threshold=0.002, verbose=True):
i = []
d = []
auc_log = [0]
def objective(parameters):
i.append(0)
set_weights(model, parameters)
p = model.predict(X, batch_size=256, verbose=0)[:, 1]
auc = roc_auc_truncated(y, p)
pa = model.predict(Xa, batch_size=256, verbose=0)[:, 1]
ks = compute_ks(pa[ya == 0], pa[ya == 1], wa[ya == 0], wa[ya == 1])
pc = model.predict(Xc, batch_size=256, verbose=0)[:, 1]
cvm = compute_cvm(pc, mc)
ks_importance = 1 # relative KS importance
ks_target = ks_threshold
cvm_importance = 1 # relative CVM importance
cvm_target = cvm_threshold
alpha = 0.001 # LeakyReLU
ks_loss = (1 if ks > ks_target else alpha) * (ks - ks_target)
cvm_loss = (1 if cvm > cvm_target else alpha) * (cvm - cvm_target)
loss = -auc + ks_importance*ks_loss + cvm_importance*cvm_loss
if ks < ks_threshold and cvm < cvm_threshold and auc > auc_log[0]:
d.append(0)
dump_transductor_model(model, transductor_model_file.format(len(d)))
auc_log.pop()
auc_log.append(auc)
message = "iteration {:7}: Best AUC={:7.5f} achieved, KS={:7.5f}, CVM={:7.5f}".format(len(i), auc, ks, cvm)
logger.info(message)
if verbose:
print("iteration {:7}: AUC: {:7.5f}, KS: {:7.5f}, CVM: {:7.5f}, loss: {:8.5f}".format(len(i),
auc, ks, cvm, loss))
return loss
return objective
Xt, yt, _, _ = load(pt.training_file, pt.train_prediction_file) # shuffled
Xa, ya, wa, _ = load(pt.check_agreement_file, pt.check_agreement_prediction_file,
tail=len(yt), weight=True)
Xc, yc, _, mc = load(pt.check_correlation_file, pt.check_correlation_prediction_file,
mass=True)
Xt, scaler = preprocess_data(Xt)
Xa = preprocess_data(Xa, scaler)[0]
Xc = preprocess_data(Xc, scaler)[0]
with open(pt.transductor_scaler_file, 'wb') as fid:
cPickle.dump(scaler, fid)
AUC = roc_auc_truncated(yt, Xt[:,-1])
print ('AUC before transductor', AUC)
model = create_model(Xt.shape[1])
pretrain = True
if pretrain:
# pretrain model
print("Pretrain model")
yt_categorical = np_utils.to_categorical(yt, nb_classes=2)
model.fit(Xt, yt_categorical, batch_size=64, nb_epoch=1,
validation_data=None, verbose=2, show_accuracy=True)
print("Save pretrained model")
with open(pt.transductor_pretrained_model_file, 'wb') as fid:
cPickle.dump(model, fid)
else:
print("Load pretrained model")
with open(pt.transductor_pretrained_model_file, 'rb') as fid:
model = cPickle.load(fid)
x0 = get_weights(model)
print("Optimize %d weights" % len(x0))
objective = create_objective(model, pt.transductor_model_file,
Xt, yt, Xa, ya, wa, Xc, mc, verbose=True)
minimize(objective, x0, args=(), method='Powell')