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multi_class.py
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multi_class.py
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#!usr/bin/env python
#-*- coding:utf-8 _*-
"""
@version: python3.6
@author: QLMX
@contact: wenruichn@gmail.com
@time: 2019-08-01 15:28
公众号:AI成长社
知乎:https://www.zhihu.com/people/qlmx-61/columns
"""
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import LabelEncoder, StandardScaler
from sklearn.model_selection import KFold
import gc
from keras.models import Sequential
from keras.layers import Dense,BatchNormalization,Dropout
from keras.utils import to_categorical
from keras import backend as K
import keras
## load data
train_data = pd.read_csv('../../data/train.csv')
test_data = pd.read_csv('../../data/test.csv')
epochs = 3
batch_size = 1024
classes = 33
## category feature one_hot
test_data['label'] = -1
data = pd.concat([train_data, test_data])
cate_feature = ['gender', 'cell_province', 'id_province', 'id_city', 'rate', 'term']
for item in cate_feature:
data[item] = LabelEncoder().fit_transform(data[item])
item_dummies = pd.get_dummies(data[item])
item_dummies.columns = [item + str(i + 1) for i in range(item_dummies.shape[1])]
data = pd.concat([data, item_dummies], axis=1)
data.drop(cate_feature,axis=1,inplace=True)
train = data[data['label'] != -1]
test = data[data['label'] == -1]
##Clean up the memory
del data, train_data, test_data
gc.collect()
## get train feature
del_feature = ['auditing_date', 'due_date', 'label']
features = [i for i in train.columns if i not in del_feature]
train_x = train[features]
train_y = train['label'].values
test = test[features]
## Fill missing value
for i in train_x.columns:
# print(i, train_x[i].isnull().sum(), test[i].isnull().sum())
if train_x[i].isnull().sum() != 0:
train_x[i].fillna(-1, inplace=True)
test[i].fillna(-1, inplace=True)
## normalized
scaler = StandardScaler()
train_X = scaler.fit_transform(train_x)
test_X = scaler.transform(test)
##label one_hot
y_categorical = to_categorical(train_y)
## simple mlp model
K.clear_session()
def MLP(dropout_rate=0.25, activation='relu'):
start_neurons = 512
model = Sequential()
model.add(Dense(start_neurons, input_dim=train_X.shape[1], activation=activation))
model.add(BatchNormalization())
model.add(Dropout(dropout_rate))
model.add(Dense(start_neurons // 2, activation=activation))
model.add(BatchNormalization())
model.add(Dropout(dropout_rate))
model.add(Dense(start_neurons // 4, activation=activation))
model.add(BatchNormalization())
model.add(Dropout(dropout_rate))
model.add(Dense(start_neurons // 8, activation=activation))
model.add(BatchNormalization())
model.add(Dropout(dropout_rate / 2))
model.add(Dense(classes, activation='softmax'))
return model
def plot_loss_acc(history, fold):
plt.plot(history.history['loss'][1:])
plt.plot(history.history['val_loss'][1:])
plt.title('model loss')
plt.ylabel('val_loss')
plt.xlabel('epoch')
plt.legend(['train', 'Validation'], loc='upper left')
plt.savefig('../../result/model_loss' + str(fold) + '.png')
plt.show()
plt.plot(history.history['acc'][1:])
plt.plot(history.history['val_acc'][1:])
plt.title('model Accuracy')
plt.ylabel('val_acc')
plt.xlabel('epoch')
plt.legend(['train', 'Validation'], loc='upper left')
plt.savefig('../../result/model_accuracy' + str(fold) + '.png')
plt.show()
# # https://www.kaggle.com/c/PLAsTiCC-2018/discussion/69795
# def mywloss(y_true,y_pred):
# yc=tf.clip_by_value(y_pred,1e-15,1-1e-15)
# loss=-(tf.reduce_mean(tf.reduce_mean(y_true*tf.log(yc),axis=0)))
# return loss
folds = KFold(n_splits=5, shuffle=True, random_state=2019)
NN_predictions = np.zeros((test_X.shape[0], classes))
oof_preds = np.zeros((train_X.shape[0], classes))
patience = 50 ## How many steps to stop
call_ES = keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=patience, verbose=1,
mode='auto', baseline=None)
for fold_, (trn_, val_) in enumerate(folds.split(train_x)):
print("fold {}".format(fold_ + 1))
x_train, y_train = train_X[trn_], y_categorical[trn_]
x_valid, y_valid = train_X[val_], y_categorical[val_]
model = MLP(dropout_rate=0.5, activation='relu')
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train,
validation_data=[x_valid, y_valid],
epochs=epochs,
batch_size=batch_size,
callbacks=[call_ES, ],
shuffle=True,
verbose=1)
# plot_loss_acc(history, fold_ + 1)
## Get predicted probabilities for each class
oof_preds[val_] = model.predict_proba(x_valid, batch_size=batch_size)
NN_predictions += model.predict_proba(test_X, batch_size=batch_size) / folds.n_splits
result = np.argmax(NN_predictions, axis=1)