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update, add random forest
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commit
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@ -3,7 +3,6 @@
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|
<element signature="e#0#19#0" expanded="true" />
|
||||||
@ -406,7 +342,7 @@
|
|||||||
</entry>
|
</entry>
|
||||||
<entry file="file://$PROJECT_DIR$/train.py">
|
<entry file="file://$PROJECT_DIR$/train.py">
|
||||||
<provider selected="true" editor-type-id="text-editor">
|
<provider selected="true" editor-type-id="text-editor">
|
||||||
<state relative-caret-position="960">
|
<state relative-caret-position="810">
|
||||||
<caret line="64" column="22" selection-start-line="64" selection-start-column="12" selection-end-line="64" selection-end-column="22" />
|
<caret line="64" column="22" selection-start-line="64" selection-start-column="12" selection-end-line="64" selection-end-column="22" />
|
||||||
<folding>
|
<folding>
|
||||||
<element signature="e#0#28#0" expanded="true" />
|
<element signature="e#0#28#0" expanded="true" />
|
||||||
@ -423,7 +359,7 @@
|
|||||||
</entry>
|
</entry>
|
||||||
<entry file="file://$PROJECT_DIR$/model_tensorflow.py">
|
<entry file="file://$PROJECT_DIR$/model_tensorflow.py">
|
||||||
<provider selected="true" editor-type-id="text-editor">
|
<provider selected="true" editor-type-id="text-editor">
|
||||||
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|
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|
||||||
<caret line="32" column="58" selection-start-line="32" selection-start-column="58" selection-end-line="32" selection-end-column="58" />
|
<caret line="32" column="58" selection-start-line="32" selection-start-column="58" selection-end-line="32" selection-end-column="58" />
|
||||||
<folding>
|
<folding>
|
||||||
<element signature="e#0#23#0" expanded="true" />
|
<element signature="e#0#23#0" expanded="true" />
|
||||||
@ -431,25 +367,38 @@
|
|||||||
</state>
|
</state>
|
||||||
</provider>
|
</provider>
|
||||||
</entry>
|
</entry>
|
||||||
|
<entry file="file://$PROJECT_DIR$/README.md">
|
||||||
|
<provider selected="true" editor-type-id="split-provider[text-editor;markdown-preview-editor]">
|
||||||
|
<state split_layout="SPLIT">
|
||||||
|
<first_editor relative-caret-position="480">
|
||||||
|
<caret line="32" selection-start-line="32" selection-end-line="32" />
|
||||||
|
</first_editor>
|
||||||
|
<second_editor />
|
||||||
|
</state>
|
||||||
|
</provider>
|
||||||
|
</entry>
|
||||||
<entry file="file://$PROJECT_DIR$/serve.py">
|
<entry file="file://$PROJECT_DIR$/serve.py">
|
||||||
<provider selected="true" editor-type-id="text-editor">
|
<provider selected="true" editor-type-id="text-editor">
|
||||||
<state relative-caret-position="414">
|
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|
||||||
<caret line="211" column="31" selection-start-line="211" selection-start-column="31" selection-end-line="211" selection-end-column="31" />
|
<caret line="14" column="27" lean-forward="true" selection-start-line="14" selection-start-column="27" selection-end-line="14" selection-end-column="27" />
|
||||||
<folding>
|
<folding>
|
||||||
<element signature="e#18#46#0" expanded="true" />
|
|
||||||
<marker date="1588426314177" expanded="true" signature="1271:1274" ph="..." />
|
<marker date="1588426314177" expanded="true" signature="1271:1274" ph="..." />
|
||||||
<marker date="1588426314177" expanded="true" signature="3519:3521" ph="..." />
|
<marker date="1588426314177" expanded="true" signature="3519:3521" ph="..." />
|
||||||
</folding>
|
</folding>
|
||||||
</state>
|
</state>
|
||||||
</provider>
|
</provider>
|
||||||
</entry>
|
</entry>
|
||||||
<entry file="file://$PROJECT_DIR$/README.md">
|
<entry file="file://$PROJECT_DIR$/rf.py">
|
||||||
<provider selected="true" editor-type-id="split-provider[text-editor;markdown-preview-editor]">
|
<provider selected="true" editor-type-id="text-editor">
|
||||||
<state split_layout="SPLIT">
|
<state relative-caret-position="90">
|
||||||
<first_editor relative-caret-position="336">
|
<caret line="7" column="36" lean-forward="true" selection-start-line="7" selection-start-column="36" selection-end-line="7" selection-end-column="36" />
|
||||||
<caret line="31" column="26" selection-start-line="31" selection-start-column="26" selection-end-line="31" selection-end-column="26" />
|
</state>
|
||||||
</first_editor>
|
</provider>
|
||||||
<second_editor />
|
</entry>
|
||||||
|
<entry file="file://$PROJECT_DIR$/serve_rf.py">
|
||||||
|
<provider selected="true" editor-type-id="text-editor">
|
||||||
|
<state relative-caret-position="165">
|
||||||
|
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|
||||||
</state>
|
</state>
|
||||||
</provider>
|
</provider>
|
||||||
</entry>
|
</entry>
|
||||||
|
|||||||
149
rf.py
Normal file
149
rf.py
Normal file
@ -0,0 +1,149 @@
|
|||||||
|
# _*_coding:utf-8_*_
|
||||||
|
import numpy as np
|
||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
|
||||||
|
def load_data(trainfile, testfile):
|
||||||
|
traindata = pd.read_csv(trainfile)
|
||||||
|
testdata = pd.read_csv(testfile)
|
||||||
|
feature_data = traindata.iloc[:, 1:-1]
|
||||||
|
label_data = traindata.iloc[:, -1]
|
||||||
|
test_feature = testdata.iloc[:, 1:]
|
||||||
|
return feature_data, label_data, test_feature
|
||||||
|
|
||||||
|
|
||||||
|
def random_forest_train(feature_data, label_data, test_feature, submitfile):
|
||||||
|
from sklearn.ensemble import RandomForestRegressor
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from sklearn.metrics import mean_squared_error
|
||||||
|
|
||||||
|
X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.01)
|
||||||
|
params = {
|
||||||
|
'n_estimators': 70,
|
||||||
|
'max_depth': 13,
|
||||||
|
'min_samples_split': 10,
|
||||||
|
'min_samples_leaf': 5, # 10
|
||||||
|
'max_features': 7
|
||||||
|
}
|
||||||
|
print(X_test)
|
||||||
|
model = RandomForestRegressor(**params)
|
||||||
|
model.fit(X_train, y_train)
|
||||||
|
# 对测试集进行预测
|
||||||
|
y_pred = model.predict(X_test)
|
||||||
|
# 计算准确率
|
||||||
|
MSE = mean_squared_error(y_test, y_pred)
|
||||||
|
RMSE = np.sqrt(MSE)
|
||||||
|
print(RMSE)
|
||||||
|
|
||||||
|
submit = pd.read_csv(submitfile)
|
||||||
|
print(submit)
|
||||||
|
print(model.predict(test_feature))
|
||||||
|
submit['CPU'] = model.predict(test_feature)
|
||||||
|
submit.to_csv('my_random_forest_prediction1.csv', index=False)
|
||||||
|
print(submit)
|
||||||
|
|
||||||
|
|
||||||
|
def random_forest_parameter_tuning1(feature_data, label_data, test_feature):
|
||||||
|
from sklearn.ensemble import RandomForestRegressor
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from sklearn.metrics import mean_squared_error
|
||||||
|
from sklearn.model_selection import GridSearchCV
|
||||||
|
|
||||||
|
X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23)
|
||||||
|
param_test1 = {
|
||||||
|
'n_estimators': range(10, 71, 10)
|
||||||
|
}
|
||||||
|
model = GridSearchCV(estimator=RandomForestRegressor(
|
||||||
|
min_samples_split=100, min_samples_leaf=20, max_depth=8, max_features='sqrt',
|
||||||
|
random_state=10), param_grid=param_test1, cv=5
|
||||||
|
)
|
||||||
|
model.fit(X_train, y_train)
|
||||||
|
# 对测试集进行预测
|
||||||
|
y_pred = model.predict(X_test)
|
||||||
|
# 计算准确率
|
||||||
|
MSE = mean_squared_error(y_test, y_pred)
|
||||||
|
RMSE = np.sqrt(MSE)
|
||||||
|
print(RMSE)
|
||||||
|
return model.best_score_, model.best_params_
|
||||||
|
|
||||||
|
|
||||||
|
def random_forest_parameter_tuning2(feature_data, label_data, test_feature):
|
||||||
|
from sklearn.ensemble import RandomForestRegressor
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from sklearn.metrics import mean_squared_error
|
||||||
|
from sklearn.model_selection import GridSearchCV
|
||||||
|
|
||||||
|
X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23)
|
||||||
|
param_test2 = {
|
||||||
|
'max_depth': range(3, 14, 2),
|
||||||
|
'min_samples_split': range(50, 201, 20)
|
||||||
|
}
|
||||||
|
model = GridSearchCV(estimator=RandomForestRegressor(
|
||||||
|
n_estimators=70, min_samples_leaf=20, max_features='sqrt', oob_score=True,
|
||||||
|
random_state=10), param_grid=param_test2, cv=5
|
||||||
|
)
|
||||||
|
model.fit(X_train, y_train)
|
||||||
|
# 对测试集进行预测
|
||||||
|
y_pred = model.predict(X_test)
|
||||||
|
# 计算准确率
|
||||||
|
MSE = mean_squared_error(y_test, y_pred)
|
||||||
|
RMSE = np.sqrt(MSE)
|
||||||
|
print(RMSE)
|
||||||
|
return model.best_score_, model.best_params_
|
||||||
|
|
||||||
|
|
||||||
|
def random_forest_parameter_tuning3(feature_data, label_data, test_feature):
|
||||||
|
from sklearn.ensemble import RandomForestRegressor
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from sklearn.metrics import mean_squared_error
|
||||||
|
from sklearn.model_selection import GridSearchCV
|
||||||
|
|
||||||
|
X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23)
|
||||||
|
param_test3 = {
|
||||||
|
'min_samples_split': range(10, 90, 20),
|
||||||
|
'min_samples_leaf': range(10, 60, 10),
|
||||||
|
}
|
||||||
|
model = GridSearchCV(estimator=RandomForestRegressor(
|
||||||
|
n_estimators=70, max_depth=13, max_features='sqrt', oob_score=True,
|
||||||
|
random_state=10), param_grid=param_test3, cv=5
|
||||||
|
)
|
||||||
|
model.fit(X_train, y_train)
|
||||||
|
# 对测试集进行预测
|
||||||
|
y_pred = model.predict(X_test)
|
||||||
|
# 计算准确率
|
||||||
|
MSE = mean_squared_error(y_test, y_pred)
|
||||||
|
RMSE = np.sqrt(MSE)
|
||||||
|
print(RMSE)
|
||||||
|
return model.best_score_, model.best_params_
|
||||||
|
|
||||||
|
|
||||||
|
def random_forest_parameter_tuning4(feature_data, label_data, test_feature):
|
||||||
|
from sklearn.ensemble import RandomForestRegressor
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from sklearn.metrics import mean_squared_error
|
||||||
|
from sklearn.model_selection import GridSearchCV
|
||||||
|
|
||||||
|
X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.23)
|
||||||
|
param_test4 = {
|
||||||
|
'max_features': range(3, 9, 2)
|
||||||
|
}
|
||||||
|
model = GridSearchCV(estimator=RandomForestRegressor(
|
||||||
|
n_estimators=70, max_depth=13, min_samples_split=10, min_samples_leaf=10, oob_score=True,
|
||||||
|
random_state=10), param_grid=param_test4, cv=5
|
||||||
|
)
|
||||||
|
model.fit(X_train, y_train)
|
||||||
|
# 对测试集进行预测
|
||||||
|
y_pred = model.predict(X_test)
|
||||||
|
# 计算准确率
|
||||||
|
MSE = mean_squared_error(y_test, y_pred)
|
||||||
|
RMSE = np.sqrt(MSE)
|
||||||
|
print(RMSE)
|
||||||
|
return model.best_score_, model.best_params_
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
trainfile = 'data/train.csv'
|
||||||
|
testfile = 'data/test.csv'
|
||||||
|
submitfile = 'data/sample_submit.csv'
|
||||||
|
feature_data, label_data, test_feature = load_data(trainfile, testfile)
|
||||||
|
random_forest_train(feature_data, label_data, test_feature, submitfile)
|
||||||
280
serve.py
280
serve.py
@ -6,187 +6,106 @@ import cgi
|
|||||||
import json
|
import json
|
||||||
from urllib import parse
|
from urllib import parse
|
||||||
import pandas as pd
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
import csv
|
import csv
|
||||||
from pandas import DataFrame
|
|
||||||
from pandas import Series
|
|
||||||
from pandas import concat
|
|
||||||
from pandas import read_csv
|
|
||||||
from sklearn.metrics import mean_squared_error
|
|
||||||
from sklearn.preprocessing import MinMaxScaler
|
|
||||||
from keras.models import Sequential
|
|
||||||
from keras.layers import Dense
|
|
||||||
from keras.layers import LSTM
|
|
||||||
from math import sqrt
|
|
||||||
import numpy
|
|
||||||
import random
|
import random
|
||||||
import traceback
|
import traceback
|
||||||
from keras.models import load_model
|
import pickle
|
||||||
from sklearn.externals import joblib
|
import os
|
||||||
|
from sklearn.ensemble import RandomForestRegressor
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
from sklearn.metrics import mean_squared_error
|
||||||
|
|
||||||
PORT_NUMBER = 8080
|
PORT_NUMBER = int(os.getenv('Port', 8080))
|
||||||
lock = Lock()
|
lock = Lock()
|
||||||
models = {}
|
models = {}
|
||||||
|
|
||||||
|
|
||||||
# frame a sequence as a supervised learning problem
|
def load_data(trainfile, testfile):
|
||||||
def timeseries_to_supervised(data, lag=1):
|
traindata = pd.read_csv(trainfile)
|
||||||
df = DataFrame(data)
|
testdata = pd.read_csv(testfile)
|
||||||
columns = [df.shift(i) for i in range(1, lag + 1)]
|
feature_data = traindata.iloc[:, 1:-1]
|
||||||
columns.append(df)
|
label_data = traindata.iloc[:, -1]
|
||||||
df = concat(columns, axis=1)
|
test_feature = testdata.iloc[:, 1:]
|
||||||
df = df.drop(0)
|
return feature_data, label_data, test_feature
|
||||||
return df
|
|
||||||
|
|
||||||
|
|
||||||
# create a differenced series
|
|
||||||
def difference(dataset, interval=1):
|
|
||||||
diff = list()
|
|
||||||
for i in range(interval, len(dataset)):
|
|
||||||
value = dataset[i] - dataset[i - interval]
|
|
||||||
diff.append(value)
|
|
||||||
return Series(diff)
|
|
||||||
|
|
||||||
|
|
||||||
# invert differenced value
|
|
||||||
def inverse_difference(history, yhat, interval=1):
|
|
||||||
return yhat + history[-interval]
|
|
||||||
|
|
||||||
|
|
||||||
# inverse scaling for a forecasted value
|
|
||||||
def invert_scale(scaler, X, yhat):
|
|
||||||
new_row = [x for x in X] + [yhat]
|
|
||||||
array = numpy.array(new_row)
|
|
||||||
array = array.reshape(1, len(array))
|
|
||||||
inverted = scaler.inverse_transform(array)
|
|
||||||
return inverted[0, -1]
|
|
||||||
|
|
||||||
|
|
||||||
# fit an LSTM network to training data
|
|
||||||
def fit_lstm(train, batch_size2, nb_epoch, neurons):
|
|
||||||
X, y = train[:, 0:-1], train[:, -1]
|
|
||||||
X = X.reshape(X.shape[0], 1, X.shape[1])
|
|
||||||
model = Sequential()
|
|
||||||
model.add(LSTM(neurons, batch_input_shape=(batch_size2, X.shape[1], X.shape[2]), stateful=True))
|
|
||||||
model.add(Dense(1))
|
|
||||||
model.compile(loss='mean_squared_error', optimizer='adam')
|
|
||||||
for i in range(nb_epoch):
|
|
||||||
model.fit(X, y, epochs=1, batch_size=batch_size2, verbose=0, shuffle=False)
|
|
||||||
# loss = model.evaluate(X, y)
|
|
||||||
# print("Epoch {}/{}, loss = {}".format(i, nb_epoch, loss))
|
|
||||||
print("Epoch {}/{}".format(i, nb_epoch))
|
|
||||||
model.reset_states()
|
|
||||||
return model
|
|
||||||
|
|
||||||
|
|
||||||
def train_models(job):
|
def train_models(job):
|
||||||
lock.acquire()
|
if job not in models or 'features' not in models[job]:
|
||||||
if job not in models:
|
return
|
||||||
models[job] = {
|
|
||||||
'lock': Lock()
|
|
||||||
}
|
|
||||||
lock.release()
|
|
||||||
|
|
||||||
models[job]['lock'].acquire()
|
models[job]['lock'].acquire()
|
||||||
|
|
||||||
# load dataset
|
for label in models[job]['labels']:
|
||||||
series = read_csv('./data/' + job + '.csv', header=0, index_col=0, squeeze=True)
|
trainfile = './data/' + job + '_' + label + '.csv'
|
||||||
|
traindata = pd.read_csv(trainfile)
|
||||||
|
feature_data = traindata.iloc[:, 1:-1]
|
||||||
|
label_data = traindata.iloc[:, -1]
|
||||||
|
|
||||||
# transform data to be stationary
|
X_train, X_test, y_train, y_test = train_test_split(feature_data, label_data, test_size=0.01)
|
||||||
raw_values = series.values
|
params = {
|
||||||
diff_values = difference(raw_values, 1)
|
'n_estimators': 70,
|
||||||
# transform data to be supervised learning
|
'max_depth': 13,
|
||||||
lag = 4
|
'min_samples_split': 10,
|
||||||
supervised = timeseries_to_supervised(diff_values, lag)
|
'min_samples_leaf': 5, # 10
|
||||||
supervised_values = supervised.values
|
'max_features': len(models[job]['features']) - 1 # 7
|
||||||
|
}
|
||||||
|
# print(params)
|
||||||
|
model = RandomForestRegressor(**params)
|
||||||
|
model.fit(X_train, y_train)
|
||||||
|
|
||||||
batch_size = 32
|
# save the model to disk
|
||||||
if supervised_values.shape[0] < 100:
|
modelname = './data/' + job + '_' + label + '.sav'
|
||||||
batch_size = 16
|
pickle.dump(model, open(modelname, 'wb'))
|
||||||
if supervised_values.shape[0] < 60:
|
|
||||||
batch_size = 8
|
|
||||||
|
|
||||||
# split data into train and test-sets
|
# 对测试集进行预测
|
||||||
train = supervised_values
|
y_pred = model.predict(X_test)
|
||||||
# transform the scale of the data
|
# 计算准确率
|
||||||
|
MSE = mean_squared_error(y_test, y_pred)
|
||||||
# scale data to [-1, 1]
|
RMSE = np.sqrt(MSE)
|
||||||
# fit scaler
|
print('RMSE of ' + job + ' is ' + str(RMSE))
|
||||||
scaler = MinMaxScaler(feature_range=(-1, 1))
|
|
||||||
scaler = scaler.fit(train)
|
|
||||||
# transform train
|
|
||||||
train = train.reshape(train.shape[0], train.shape[1])
|
|
||||||
train_scaled = scaler.transform(train)
|
|
||||||
|
|
||||||
# fit the model
|
|
||||||
t1 = train.shape[0] % batch_size
|
|
||||||
|
|
||||||
train_trimmed = train_scaled[t1:, :]
|
|
||||||
model = fit_lstm(train_trimmed, batch_size, 30, 4)
|
|
||||||
|
|
||||||
model.save('./data/checkpoint-' + job)
|
|
||||||
scaler_filename = './data/checkpoint-' + job + "-scaler.save"
|
|
||||||
joblib.dump(scaler, scaler_filename)
|
|
||||||
|
|
||||||
models[job]['batch_size'] = batch_size
|
|
||||||
|
|
||||||
models[job]['lock'].release()
|
models[job]['lock'].release()
|
||||||
|
|
||||||
|
|
||||||
def predict(job, seq):
|
def predict(job, features):
|
||||||
if job not in models or 'batch_size' not in models[job]:
|
if job not in models or 'features' not in models[job]:
|
||||||
return -1, False
|
return -1, False
|
||||||
|
|
||||||
batch_size = int(models[job]['batch_size'])
|
values = [job]
|
||||||
|
for feature in models[job]['features']:
|
||||||
|
values.append(features[feature])
|
||||||
|
|
||||||
data = {
|
datafile = './data/' + job + '.' + str(random.randint(1000, 9999)) + '.csv'
|
||||||
'seq': seq,
|
t = ['job']
|
||||||
'value': 0,
|
t.extend(models[job]['features'])
|
||||||
}
|
with open(datafile, 'w', newline='') as csvfile:
|
||||||
model = load_model('./data/checkpoint-' + job)
|
spamwriter = csv.writer(
|
||||||
scaler_filename = './data/checkpoint-' + job + "-scaler.save"
|
csvfile, delimiter=',',
|
||||||
scaler = joblib.load(scaler_filename)
|
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
||||||
|
)
|
||||||
|
spamwriter.writerow(t)
|
||||||
|
|
||||||
file = './data/' + job + '.' + str(random.randint(1000, 9999)) + '.csv'
|
with open(datafile, 'a+', newline='') as csvfile:
|
||||||
df = pd.read_csv('./data/' + job + '.csv', usecols=['seq', 'value'])
|
spamwriter = csv.writer(
|
||||||
df = df.tail(batch_size * 2 - 1)
|
csvfile, delimiter=',',
|
||||||
df = df.append(data, ignore_index=True)
|
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
||||||
df.to_csv(file, index=False)
|
)
|
||||||
|
spamwriter.writerow(values)
|
||||||
|
|
||||||
# load dataset
|
testdata = pd.read_csv(datafile)
|
||||||
df = read_csv(file, header=0, index_col=0, squeeze=True)
|
test_feature = testdata.iloc[:, 1:]
|
||||||
|
|
||||||
# transform data to be stationary
|
predictions = {}
|
||||||
raw_values = df.values
|
for label in models[job]['labels']:
|
||||||
diff_values = difference(raw_values, 1)
|
# load the model from disk
|
||||||
|
modelfile = './data/' + job + '_' + label + '.sav'
|
||||||
|
model = pickle.load(open(modelfile, 'rb'))
|
||||||
|
preds = model.predict(test_feature)
|
||||||
|
predictions[label] = preds[0]
|
||||||
|
|
||||||
# transform data to be supervised learning
|
if os.path.exists(datafile):
|
||||||
lag = 4
|
os.remove(datafile)
|
||||||
supervised = timeseries_to_supervised(diff_values, lag)
|
return predictions, True
|
||||||
supervised_values = supervised[-batch_size:]
|
|
||||||
test = supervised_values.values
|
|
||||||
|
|
||||||
test = test.reshape(test.shape[0], test.shape[1])
|
|
||||||
test_scaled = scaler.transform(test)
|
|
||||||
|
|
||||||
# forecast the entire training dataset to build up state for forecasting
|
|
||||||
test_reshaped = test_scaled[:, 0:-1]
|
|
||||||
test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, lag)
|
|
||||||
output = model.predict(test_reshaped, batch_size=batch_size)
|
|
||||||
predictions = list()
|
|
||||||
for i in range(len(output)):
|
|
||||||
yhat = output[i, 0]
|
|
||||||
X = test_scaled[i, 0:-1]
|
|
||||||
# invert scaling
|
|
||||||
yhat = invert_scale(scaler, X, yhat)
|
|
||||||
# invert differencing
|
|
||||||
yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i)
|
|
||||||
# store forecast
|
|
||||||
predictions.append(yhat)
|
|
||||||
# report performance
|
|
||||||
|
|
||||||
rmse = sqrt(mean_squared_error(raw_values[-batch_size:], predictions))
|
|
||||||
print(predictions, raw_values[-batch_size:])
|
|
||||||
return predictions[-1], True
|
|
||||||
|
|
||||||
|
|
||||||
class MyHandler(BaseHTTPRequestHandler):
|
class MyHandler(BaseHTTPRequestHandler):
|
||||||
@ -204,15 +123,15 @@ class MyHandler(BaseHTTPRequestHandler):
|
|||||||
elif req.path == "/predict":
|
elif req.path == "/predict":
|
||||||
try:
|
try:
|
||||||
job = query.get('job')[0]
|
job = query.get('job')[0]
|
||||||
seq = query.get('seq')[0]
|
features = json.loads(query.get('features')[0])
|
||||||
msg = {'code': 0, 'error': ""}
|
msg = {'code': 0, 'error': ""}
|
||||||
|
|
||||||
pred, success = predict(job, int(seq))
|
pred, success = predict(job, features)
|
||||||
|
|
||||||
if not success:
|
if not success:
|
||||||
msg = {'code': 2, 'error': "Job " + job + " not exist"}
|
msg = {'code': 2, 'error': "Job " + job + " not exist"}
|
||||||
else:
|
else:
|
||||||
msg = {'code': 0, 'error': "", "total": int(pred)}
|
msg = {'code': 0, 'error': "", "labels": json.dumps(pred)}
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
track = traceback.format_exc()
|
track = traceback.format_exc()
|
||||||
print(track)
|
print(track)
|
||||||
@ -226,26 +145,50 @@ class MyHandler(BaseHTTPRequestHandler):
|
|||||||
elif req.path == "/feed":
|
elif req.path == "/feed":
|
||||||
try:
|
try:
|
||||||
job = query.get('job')[0]
|
job = query.get('job')[0]
|
||||||
seq = query.get('seq')[0]
|
features = json.loads(query.get('features')[0])
|
||||||
value = query.get('value')[0]
|
labels = json.loads(query.get('labels')[0])
|
||||||
|
|
||||||
if int(seq) == 1:
|
lock.acquire()
|
||||||
with open('./data/' + job + '.csv', 'w', newline='') as csvfile:
|
flag = False
|
||||||
|
if job not in models:
|
||||||
|
models[job] = {
|
||||||
|
'lock': Lock(),
|
||||||
|
'features': list(features.keys()),
|
||||||
|
'labels': list(labels.keys())
|
||||||
|
}
|
||||||
|
flag = True
|
||||||
|
lock.release()
|
||||||
|
models[job]['lock'].acquire()
|
||||||
|
|
||||||
|
for label in models[job]['labels']:
|
||||||
|
values = [job]
|
||||||
|
for feature in models[job]['features']:
|
||||||
|
values.append(features[feature])
|
||||||
|
values.append(labels[label])
|
||||||
|
if flag:
|
||||||
|
t = ['job']
|
||||||
|
t.extend(models[job]['features'])
|
||||||
|
t.append(label)
|
||||||
|
with open('./data/' + job + '_' + label + '.csv', 'w', newline='') as csvfile:
|
||||||
|
spamwriter = csv.writer(
|
||||||
|
csvfile, delimiter=',',
|
||||||
|
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
||||||
|
)
|
||||||
|
spamwriter.writerow(t)
|
||||||
|
|
||||||
|
with open('./data/' + job + '_' + label + '.csv', 'a+', newline='') as csvfile:
|
||||||
spamwriter = csv.writer(
|
spamwriter = csv.writer(
|
||||||
csvfile, delimiter=',',
|
csvfile, delimiter=',',
|
||||||
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
||||||
)
|
)
|
||||||
spamwriter.writerow(["seq", "value"])
|
spamwriter.writerow(values)
|
||||||
|
|
||||||
with open('./data/' + job + '.csv', 'a+', newline='') as csvfile:
|
models[job]['lock'].release()
|
||||||
spamwriter = csv.writer(
|
|
||||||
csvfile, delimiter=',',
|
|
||||||
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
|
||||||
)
|
|
||||||
spamwriter.writerow([seq, value])
|
|
||||||
msg = {'code': 0, 'error': ""}
|
msg = {'code': 0, 'error': ""}
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
msg = {'code': 1, 'error': str(e)}
|
msg = {'code': 1, 'error': str(e)}
|
||||||
|
track = traceback.format_exc()
|
||||||
|
print(track)
|
||||||
self.send_response(200)
|
self.send_response(200)
|
||||||
self.send_header('Content-type', 'application/json')
|
self.send_header('Content-type', 'application/json')
|
||||||
self.end_headers()
|
self.end_headers()
|
||||||
@ -289,7 +232,6 @@ class MyHandler(BaseHTTPRequestHandler):
|
|||||||
self.send_header('Content-type', 'application/json')
|
self.send_header('Content-type', 'application/json')
|
||||||
self.end_headers()
|
self.end_headers()
|
||||||
self.wfile.write(bytes(json.dumps(msg), "utf-8"))
|
self.wfile.write(bytes(json.dumps(msg), "utf-8"))
|
||||||
|
|
||||||
else:
|
else:
|
||||||
self.send_error(404, 'File Not Found: %s' % self.path)
|
self.send_error(404, 'File Not Found: %s' % self.path)
|
||||||
|
|
||||||
|
|||||||
310
serve_lstm.py
Normal file
310
serve_lstm.py
Normal file
@ -0,0 +1,310 @@
|
|||||||
|
#!/usr/bin/python
|
||||||
|
from threading import Thread
|
||||||
|
from threading import Lock
|
||||||
|
from http.server import BaseHTTPRequestHandler, HTTPServer
|
||||||
|
import cgi
|
||||||
|
import json
|
||||||
|
from urllib import parse
|
||||||
|
import pandas as pd
|
||||||
|
import csv
|
||||||
|
from pandas import DataFrame
|
||||||
|
from pandas import Series
|
||||||
|
from pandas import concat
|
||||||
|
from pandas import read_csv
|
||||||
|
from sklearn.metrics import mean_squared_error
|
||||||
|
from sklearn.preprocessing import MinMaxScaler
|
||||||
|
from keras.models import Sequential
|
||||||
|
from keras.layers import Dense
|
||||||
|
from keras.layers import LSTM
|
||||||
|
from math import sqrt
|
||||||
|
import numpy
|
||||||
|
import random
|
||||||
|
import traceback
|
||||||
|
from keras.models import load_model
|
||||||
|
from sklearn.externals import joblib
|
||||||
|
|
||||||
|
PORT_NUMBER = 8080
|
||||||
|
lock = Lock()
|
||||||
|
models = {}
|
||||||
|
|
||||||
|
|
||||||
|
# frame a sequence as a supervised learning problem
|
||||||
|
def timeseries_to_supervised(data, lag=1):
|
||||||
|
df = DataFrame(data)
|
||||||
|
columns = [df.shift(i) for i in range(1, lag + 1)]
|
||||||
|
columns.append(df)
|
||||||
|
df = concat(columns, axis=1)
|
||||||
|
df = df.drop(0)
|
||||||
|
return df
|
||||||
|
|
||||||
|
|
||||||
|
# create a differenced series
|
||||||
|
def difference(dataset, interval=1):
|
||||||
|
diff = list()
|
||||||
|
for i in range(interval, len(dataset)):
|
||||||
|
value = dataset[i] - dataset[i - interval]
|
||||||
|
diff.append(value)
|
||||||
|
return Series(diff)
|
||||||
|
|
||||||
|
|
||||||
|
# invert differenced value
|
||||||
|
def inverse_difference(history, yhat, interval=1):
|
||||||
|
return yhat + history[-interval]
|
||||||
|
|
||||||
|
|
||||||
|
# inverse scaling for a forecasted value
|
||||||
|
def invert_scale(scaler, X, yhat):
|
||||||
|
new_row = [x for x in X] + [yhat]
|
||||||
|
array = numpy.array(new_row)
|
||||||
|
array = array.reshape(1, len(array))
|
||||||
|
inverted = scaler.inverse_transform(array)
|
||||||
|
return inverted[0, -1]
|
||||||
|
|
||||||
|
|
||||||
|
# fit an LSTM network to training data
|
||||||
|
def fit_lstm(train, batch_size2, nb_epoch, neurons):
|
||||||
|
X, y = train[:, 0:-1], train[:, -1]
|
||||||
|
X = X.reshape(X.shape[0], 1, X.shape[1])
|
||||||
|
model = Sequential()
|
||||||
|
model.add(LSTM(neurons, batch_input_shape=(batch_size2, X.shape[1], X.shape[2]), stateful=True))
|
||||||
|
model.add(Dense(1))
|
||||||
|
model.compile(loss='mean_squared_error', optimizer='adam')
|
||||||
|
for i in range(nb_epoch):
|
||||||
|
model.fit(X, y, epochs=1, batch_size=batch_size2, verbose=0, shuffle=False)
|
||||||
|
# loss = model.evaluate(X, y)
|
||||||
|
# print("Epoch {}/{}, loss = {}".format(i, nb_epoch, loss))
|
||||||
|
print("Epoch {}/{}".format(i, nb_epoch))
|
||||||
|
model.reset_states()
|
||||||
|
return model
|
||||||
|
|
||||||
|
|
||||||
|
def train_models(job):
|
||||||
|
lock.acquire()
|
||||||
|
if job not in models:
|
||||||
|
models[job] = {
|
||||||
|
'lock': Lock()
|
||||||
|
}
|
||||||
|
lock.release()
|
||||||
|
|
||||||
|
models[job]['lock'].acquire()
|
||||||
|
|
||||||
|
# load dataset
|
||||||
|
series = read_csv('./data/' + job + '.csv', header=0, index_col=0, squeeze=True)
|
||||||
|
|
||||||
|
# transform data to be stationary
|
||||||
|
raw_values = series.values
|
||||||
|
diff_values = difference(raw_values, 1)
|
||||||
|
# transform data to be supervised learning
|
||||||
|
lag = 4
|
||||||
|
supervised = timeseries_to_supervised(diff_values, lag)
|
||||||
|
supervised_values = supervised.values
|
||||||
|
|
||||||
|
batch_size = 32
|
||||||
|
if supervised_values.shape[0] < 100:
|
||||||
|
batch_size = 16
|
||||||
|
if supervised_values.shape[0] < 60:
|
||||||
|
batch_size = 8
|
||||||
|
|
||||||
|
# split data into train and test-sets
|
||||||
|
train = supervised_values
|
||||||
|
# transform the scale of the data
|
||||||
|
|
||||||
|
# scale data to [-1, 1]
|
||||||
|
# fit scaler
|
||||||
|
scaler = MinMaxScaler(feature_range=(-1, 1))
|
||||||
|
scaler = scaler.fit(train)
|
||||||
|
# transform train
|
||||||
|
train = train.reshape(train.shape[0], train.shape[1])
|
||||||
|
train_scaled = scaler.transform(train)
|
||||||
|
|
||||||
|
# fit the model
|
||||||
|
t1 = train.shape[0] % batch_size
|
||||||
|
|
||||||
|
train_trimmed = train_scaled[t1:, :]
|
||||||
|
model = fit_lstm(train_trimmed, batch_size, 30, 4)
|
||||||
|
|
||||||
|
model.save('./data/checkpoint-' + job)
|
||||||
|
scaler_filename = './data/checkpoint-' + job + "-scaler.save"
|
||||||
|
joblib.dump(scaler, scaler_filename)
|
||||||
|
|
||||||
|
models[job]['batch_size'] = batch_size
|
||||||
|
|
||||||
|
models[job]['lock'].release()
|
||||||
|
|
||||||
|
|
||||||
|
def predict(job, seq):
|
||||||
|
if job not in models or 'batch_size' not in models[job]:
|
||||||
|
return -1, False
|
||||||
|
|
||||||
|
batch_size = int(models[job]['batch_size'])
|
||||||
|
|
||||||
|
data = {
|
||||||
|
'seq': seq,
|
||||||
|
'value': 0,
|
||||||
|
}
|
||||||
|
model = load_model('./data/checkpoint-' + job)
|
||||||
|
scaler_filename = './data/checkpoint-' + job + "-scaler.save"
|
||||||
|
scaler = joblib.load(scaler_filename)
|
||||||
|
|
||||||
|
file = './data/' + job + '.' + str(random.randint(1000, 9999)) + '.csv'
|
||||||
|
df = pd.read_csv('./data/' + job + '.csv', usecols=['seq', 'value'])
|
||||||
|
df = df.tail(batch_size * 2 - 1)
|
||||||
|
df = df.append(data, ignore_index=True)
|
||||||
|
df.to_csv(file, index=False)
|
||||||
|
|
||||||
|
# load dataset
|
||||||
|
df = read_csv(file, header=0, index_col=0, squeeze=True)
|
||||||
|
|
||||||
|
# transform data to be stationary
|
||||||
|
raw_values = df.values
|
||||||
|
diff_values = difference(raw_values, 1)
|
||||||
|
|
||||||
|
# transform data to be supervised learning
|
||||||
|
lag = 4
|
||||||
|
supervised = timeseries_to_supervised(diff_values, lag)
|
||||||
|
supervised_values = supervised[-batch_size:]
|
||||||
|
test = supervised_values.values
|
||||||
|
|
||||||
|
test = test.reshape(test.shape[0], test.shape[1])
|
||||||
|
test_scaled = scaler.transform(test)
|
||||||
|
|
||||||
|
# forecast the entire training dataset to build up state for forecasting
|
||||||
|
test_reshaped = test_scaled[:, 0:-1]
|
||||||
|
test_reshaped = test_reshaped.reshape(len(test_reshaped), 1, lag)
|
||||||
|
output = model.predict(test_reshaped, batch_size=batch_size)
|
||||||
|
predictions = list()
|
||||||
|
for i in range(len(output)):
|
||||||
|
yhat = output[i, 0]
|
||||||
|
X = test_scaled[i, 0:-1]
|
||||||
|
# invert scaling
|
||||||
|
yhat = invert_scale(scaler, X, yhat)
|
||||||
|
# invert differencing
|
||||||
|
yhat = inverse_difference(raw_values, yhat, len(test_scaled) + 1 - i)
|
||||||
|
# store forecast
|
||||||
|
predictions.append(yhat)
|
||||||
|
# report performance
|
||||||
|
|
||||||
|
rmse = sqrt(mean_squared_error(raw_values[-batch_size:], predictions))
|
||||||
|
print(predictions, raw_values[-batch_size:])
|
||||||
|
return predictions[-1], True
|
||||||
|
|
||||||
|
|
||||||
|
class MyHandler(BaseHTTPRequestHandler):
|
||||||
|
# Handler for the GET requests
|
||||||
|
def do_GET(self):
|
||||||
|
req = parse.urlparse(self.path)
|
||||||
|
query = parse.parse_qs(req.query)
|
||||||
|
|
||||||
|
if req.path == "/ping":
|
||||||
|
self.send_response(200)
|
||||||
|
self.send_header('Content-type', 'application/json')
|
||||||
|
self.end_headers()
|
||||||
|
self.wfile.write(bytes("pong", "utf-8"))
|
||||||
|
|
||||||
|
elif req.path == "/predict":
|
||||||
|
try:
|
||||||
|
job = query.get('job')[0]
|
||||||
|
seq = query.get('seq')[0]
|
||||||
|
msg = {'code': 0, 'error': ""}
|
||||||
|
|
||||||
|
pred, success = predict(job, int(seq))
|
||||||
|
|
||||||
|
if not success:
|
||||||
|
msg = {'code': 2, 'error': "Job " + job + " not exist"}
|
||||||
|
else:
|
||||||
|
msg = {'code': 0, 'error': "", "total": int(pred)}
|
||||||
|
except Exception as e:
|
||||||
|
track = traceback.format_exc()
|
||||||
|
print(track)
|
||||||
|
msg = {'code': 1, 'error': str(e)}
|
||||||
|
|
||||||
|
self.send_response(200)
|
||||||
|
self.send_header('Content-type', 'application/json')
|
||||||
|
self.end_headers()
|
||||||
|
self.wfile.write(bytes(json.dumps(msg), "utf-8"))
|
||||||
|
|
||||||
|
elif req.path == "/feed":
|
||||||
|
try:
|
||||||
|
job = query.get('job')[0]
|
||||||
|
seq = query.get('seq')[0]
|
||||||
|
value = query.get('value')[0]
|
||||||
|
|
||||||
|
if int(seq) == 1:
|
||||||
|
with open('./data/' + job + '.csv', 'w', newline='') as csvfile:
|
||||||
|
spamwriter = csv.writer(
|
||||||
|
csvfile, delimiter=',',
|
||||||
|
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
||||||
|
)
|
||||||
|
spamwriter.writerow(["seq", "value"])
|
||||||
|
|
||||||
|
with open('./data/' + job + '.csv', 'a+', newline='') as csvfile:
|
||||||
|
spamwriter = csv.writer(
|
||||||
|
csvfile, delimiter=',',
|
||||||
|
quotechar='|', quoting=csv.QUOTE_MINIMAL
|
||||||
|
)
|
||||||
|
spamwriter.writerow([seq, value])
|
||||||
|
msg = {'code': 0, 'error': ""}
|
||||||
|
except Exception as e:
|
||||||
|
msg = {'code': 1, 'error': str(e)}
|
||||||
|
self.send_response(200)
|
||||||
|
self.send_header('Content-type', 'application/json')
|
||||||
|
self.end_headers()
|
||||||
|
self.wfile.write(bytes(json.dumps(msg), "utf-8"))
|
||||||
|
|
||||||
|
elif req.path == "/train":
|
||||||
|
try:
|
||||||
|
job = query.get('job')[0]
|
||||||
|
t = Thread(target=train_models, name='train_models', args=(job,))
|
||||||
|
t.start()
|
||||||
|
msg = {'code': 0, 'error': ""}
|
||||||
|
except Exception as e:
|
||||||
|
msg = {'code': 1, 'error': str(e)}
|
||||||
|
self.send_response(200)
|
||||||
|
self.send_header('Content-type', 'application/json')
|
||||||
|
self.end_headers()
|
||||||
|
self.wfile.write(bytes(json.dumps(msg), "utf-8"))
|
||||||
|
|
||||||
|
else:
|
||||||
|
self.send_error(404, 'File Not Found: %s' % self.path)
|
||||||
|
|
||||||
|
# Handler for the POST requests
|
||||||
|
def do_POST(self):
|
||||||
|
if self.path == "/train2":
|
||||||
|
form = cgi.FieldStorage(
|
||||||
|
fp=self.rfile,
|
||||||
|
headers=self.headers,
|
||||||
|
environ={
|
||||||
|
'REQUEST_METHOD': 'POST',
|
||||||
|
'CONTENT_TYPE': self.headers['Content-Type'],
|
||||||
|
})
|
||||||
|
try:
|
||||||
|
job = form.getvalue('job')[0]
|
||||||
|
seq = form.getvalue('seq')[0]
|
||||||
|
t = Thread(target=train_models(), name='train_models', args=(job, seq,))
|
||||||
|
t.start()
|
||||||
|
msg = {"code": 0, "error": ""}
|
||||||
|
except Exception as e:
|
||||||
|
msg = {"code": 1, "error": str(e)}
|
||||||
|
self.send_response(200)
|
||||||
|
self.send_header('Content-type', 'application/json')
|
||||||
|
self.end_headers()
|
||||||
|
self.wfile.write(bytes(json.dumps(msg), "utf-8"))
|
||||||
|
|
||||||
|
else:
|
||||||
|
self.send_error(404, 'File Not Found: %s' % self.path)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
try:
|
||||||
|
# Create a web server and define the handler to manage the
|
||||||
|
# incoming request
|
||||||
|
server = HTTPServer(('', PORT_NUMBER), MyHandler)
|
||||||
|
print('Started http server on port ', PORT_NUMBER)
|
||||||
|
|
||||||
|
# Wait forever for incoming http requests
|
||||||
|
server.serve_forever()
|
||||||
|
|
||||||
|
except KeyboardInterrupt:
|
||||||
|
print('^C received, shutting down the web server')
|
||||||
|
|
||||||
|
server.socket.close()
|
||||||
Loading…
Reference in New Issue
Block a user