# sel.append(('ANOVA', SelectKBest(f_classif, k=num_fea)))
# sel.append(('TSCR', SelectKBest(t_score.t_score, k=num_fea)))
# sel.append(('GINI', SelectKBest(gini_index.gini_index, k=5)))
sel.append(('FSCR', SelectKBest(fisher_score.fisher_score, k=num_fea)))
# sel.append(('RELF', SelectKBest(reliefF.reliefF, k=num_fea)))
output = open("namesFeat.txt", "w")
scoring = 'roc_auc'
# UNIVARIATE FEATURE SELECTION X CLASSIFICATION (10 fold CV)
for name, model in models:
for kind, selection in sel:
print(kind)
pipe = make_pipeline(MinMaxScaler(), selection, model)
kfold = model_selection.KFold(n_splits=10, random_state=seed)
cv_results = model_selection.cross_val_score(pipe,
X_train,
Y_train,
cv=kfold)
# msg = "%s %s: %f (%f)\n" % (kind, name, cv_results.mean(), cv_results.std())
#output.write(msg)
pipe.fit(X_train, Y_train)
feat = pipe.named_steps['selectkbest'].get_support()
featNum = 0
for val in feat:
featNum += 1
if val == True:
def start():
device = 0
frame_rate_ratio = 7
process_speed = 1
mirror = True
# Video reader
cam = cv2.VideoCapture(device)
# CV_CAP_PROP_FPS
input_fps = cam.get(cv2.CAP_PROP_FPS)
print("Running at {} fps.".format(input_fps))
ret_val, orig_image = cam.read()
width = orig_image.shape[1]
height = orig_image.shape[0]
factor = 0.3
i = 0 # default is 0
resize_fac = 1
while True:
cv2.waitKey(10)
if cam.isOpened() is False or ret_val is False:
break
if mirror:
orig_image = cv2.flip(orig_image, 1)
tic = time.time()
cropped = crop(orig_image, width, factor)
input_image = cv2.resize(cropped, (0, 0),
fx=1 / resize_fac,
fy=1 / resize_fac,
interpolation=cv2.INTER_CUBIC)
print('Processing frame: ', i)
toc = time.time()
print('processing time is %.5f' % (toc - tic))
toc = time.time()
print('processing time is %.5f' % (toc - tic))
canvas = cv2.resize(input_image, (0, 0),
fx=4,
fy=2,
interpolation=cv2.INTER_CUBIC)
imgForText = cv2.resize(canvas, (700, 700),
interpolation=cv2.INTER_CUBIC)
img = cv2.cvtColor(canvas, cv2.COLOR_BGR2HSV).astype("float32")
(h, s, v) = cv2.split(img)
s = s * 0
s = np.clip(s, 0, 255)
img = cv2.merge([h, s, v])
img = cv2.cvtColor(img.astype("uint8"), cv2.COLOR_HSV2BGR)
image = cv2.resize(img, (700, 700), interpolation=cv2.INTER_CUBIC)
x_list, y_list = [], []
for x in np.arange(0, 700, 1):
for y in np.arange(0, 700, 1):
# if np.all(image[y][x] == (0, 0, 0)):
if np.all(image[y][x] == (255, 255, 255)):
x_list.append(x)
y_list.append(700 - y)
peaks, _ = scipy.signal.find_peaks(y_list, height=400)
if (len(peaks) != 0):
try:
extrac = extract_feat(image, peaks[0] - 90, peaks[0] + 96)
json_file = open('best_model.json', 'r')
loaded_model_json = json_file.read()
json_file.close()
loaded_model = model_from_json(loaded_model_json)
loaded_model.load_weights("best-model.h5")
print("Loaded model from disk")
loaded_model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['accuracy'])
train_new = np.reshape(extrac, (186, 1))
scaler = MinMaxScaler(feature_range=(0, 1))
train_new1 = scaler.fit_transform(train_new)
p = np.reshape(train_new1, (1, 186))
predictions = loaded_model.predict(np.reshape(p, (1, 186, 1)))
if str(np.argmax(predictions, axis=1)[0]) == '0':
output = 'N'
elif str(np.argmax(predictions, axis=1)[0]) == '1':
output = 'S'
elif str(np.argmax(predictions, axis=1)[0]) == '2':
output = 'V'
elif str(np.argmax(predictions, axis=1)[0]) == '3':
output = 'F'
elif str(np.argmax(predictions, axis=1)[0]) == '4':
output = 'U'
cv2.putText(imgForText, output, (x_list[peaks[0]], 100),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, 0)
cv2.putText(
imgForText, 'prob: ' + str(
round(
predictions[0][np.argmax(predictions, axis=1)[0]],
2)), (x_list[peaks[0]], 120),
cv2.FONT_HERSHEY_SIMPLEX, 0.4, 0)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
except IndexError:
print('immagine non posizionata correttamente')
cv2.imshow('frame', imgForText)
ret_val, orig_image = cam.read()
i += 1
@author: PRAJWAL
"""
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from tensorflow import random
dataset_training = pd.read_csv('../GOOG_train.csv')
dataset_training.head()
training_data = dataset_training[['Open']].values
from sklearn.preprocessing import MinMaxScaler
sc = MinMaxScaler(feature_range=(0, 1))
training_data_scaled = sc.fit_transform(training_data)
X_train = []
y_train = []
for i in range(60, 1258):
X_train.append(training_data_scaled[i - 60:i, 0])
y_train.append(training_data_scaled[i, 0])
X_train, y_train = np.array(X_train), np.array(y_train)
X_train = np.reshape(X_train, (X_train.shape[0], X_train.shape[1], 1))
from keras.models import Sequential
from keras.layers import Dense, LSTM, Dropout
seed = 1
# -*- coding: utf-8 -*-
"""
Created on Wed May 30 11:03:07 2018
@author: user
"""
import pandas as pd
df = pd.read_csv(
'https://raw.githubusercontent.com/rasbt/pattern_classification/master/data/wine_data.csv',
header=None,
usecols=[0, 1, 2])
df.columns = ['Class label', 'Alcohol', 'Malic acid']
from sklearn.preprocessing import StandardScaler, MinMaxScaler
sc = StandardScaler()
df.iloc[:, [1, 2]] = sc.fit_transform(df.iloc[:, [1, 2]])
mms = MinMaxScaler()
df1 = mms.fit_transform(df)
plt.scatter(range(len(results)),results,c='r')
plt.title('Validate')
plt.show()
"""
"""
Crear pronostico de ventas
"""
ultimosDias = df['2018-11-16':'2018-11-30']
values = ultimosDias.values
values = values.astype('float32')
#normalize features
values = values.reshape(-1,1)
scaler = MinMaxScaler(feature_range=(-1,1))
scaled = scaler.fit_transform(values)
reframed = series_to_supervised(scaled, pasos, 1)
reframed.drop(reframed.columns[[7]], axis=1, inplace=True)
values = reframed.values
x_test = values[6:, :]
x_test = x_test.reshape((x_test.shape[0],1,x_test.shape[1]))
def aggValue(x_test, nVal):
for i in range(x_test.shape[2]-1):
x_test[0][0][i]=x_test[0][0][i+1]
x_test[0][0][x_test.shape[2]-1]=nVal
return x_test
import pickle
import numpy as np
import os
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
b = os.path.exists(r"D:\ZSNJAP01\flight\prediction\linedlyt")
if b:
print("path exist")
else:
os.makedirs(r'D:\ZSNJAP01\flight\prediction\linedlyt')
with open(r"D:\ZSNJAP01\flight\delaydata\30min\lineavgt.pkl", 'rb') as f:
featime = pickle.load(f) #ndarray dim = [3623,4,5]
featime2d = np.reshape(featime, [-1, featime.shape[1] * featime.shape[2]])
scaler1 = MinMaxScaler(feature_range=(0, 1))
featime2d = scaler1.fit_transform(featime2d).squeeze()
featime = np.reshape(featime2d, [-1, featime.shape[1], featime.shape[2]])
featime = featime[:, :, :, np.newaxis]
time_step = 2
batch_size = 30
filters = 6
kernel_size = (2, 2)
strides = (1, 1)
padding = 'same'
data_format = 'channels_last'
input_shape = (batch_size, time_step, featime[0].shape[0], featime[0].shape[1],
featime[0].shape[2])
dilation_rate = (1, 1)
activation = 'a(x) = x'
recurrent_activation = 'a(x) = x'
# Any results you write to the current directory are saved as output.
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.head()
train_data.describe()
train_labels = train_data.Cover_Type.values
test_id = test_data.Id.values
train_data.drop(['Soil_Type7', 'Soil_Type15', 'Id', 'Cover_Type'],
axis=1,
inplace=True)
test_data.drop(['Soil_Type7', 'Soil_Type15', 'Id'], axis=1, inplace=True)
print(train_data.shape, test_data.shape)
min_max_scaler = MinMaxScaler(
) # If you did not use the scaler, you will get higher accuracy
train_data = min_max_scaler.fit_transform(train_data)
test_data = min_max_scaler.fit_transform(test_data)
distance_matrix = pairwise_distances(train_data, metric='euclidean')
print(distance_matrix.shape)
sorted_distance_index = np.argsort(distance_matrix, axis=1).astype(np.uint16)
print(sorted_distance_index)
sorted_distance_labels = train_labels[sorted_distance_index].astype(np.uint8)
print(sorted_distance_labels)
max_k = 100
k_matrix = np.empty((len(sorted_distance_labels), 0), dtype=np.uint8)
for k in range(1, max_k + 1):
k_along_rows = np.apply_along_axis(
lambda x: np.bincount(x).argmax(),
axis=1,
def index():
import numpy as np
df = pdr.get_data_yahoo(request.args.get('stockname'))
df1 = df.reset_index()['Close']
scaler = MinMaxScaler(feature_range=(0, 1))
df1 = scaler.fit_transform(np.array(df1).reshape(-1, 1))
training_size = int(len(df1) * 0.65)
test_size = len(df1) - training_size
train_data, test_data = df1[0:training_size, :], df1[
training_size:len(df1), :1]
# import numpy
# convert an array of values into a dataset matrix
def create_dataset(dataset, time_step=1):
dataX, dataY = [], []
for i in range(len(dataset) - time_step - 1):
a = dataset[i:(i + time_step), 0] ###i=0, 0,1,2,3-----99 100
dataX.append(a)
dataY.append(dataset[i + time_step, 0])
return np.array(dataX), np.array(dataY)
time_step = 100
X_train, y_train = create_dataset(train_data, time_step)
X_test, ytest = create_dataset(test_data, time_step)
# reshape input to be [samples, time steps, features] which is required for LSTM
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1)
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1)
### Create the Stacked LSTM model
import tensorflow as tf
import numpy as np
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.layers import LSTM
model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1])])
model = Sequential()
if (request.args.get('stockname')
== 'IDEA.NS') or (request.args.get('stockname')
== 'AAPL') or (request.args.get('stockname')
== 'TSLA'):
model.add(LSTM(50, return_sequences=True, input_shape=(100, 1)))
model.add(LSTM(50, return_sequences=True))
else:
model.add(LSTM(50, return_sequences=True, input_shape=(100, 1)))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50, return_sequences=True))
model.add(LSTM(50))
model.add(Dense(1))
if (request.args.get('stockname')
== 'IDEA.NS') or (request.args.get('stockname')
== 'AAPL') or (request.args.get('stockname')
== 'TSLA'):
model.compile(loss='mean_squared_error', optimizer='adam')
else:
model.compile(loss='mean_squared_error',
optimizer='adam',
metrics=['accuracy'])
if (request.args.get('stockname')
== 'IDEA.NS') or (request.args.get('stockname')
== 'AAPL') or (request.args.get('stockname')
== 'TSLA'):
model.fit(X_train,
y_train,
validation_data=(X_test, ytest),
epochs=10,
batch_size=64,
verbose=1)
else:
model.fit(
X_train,
y_train,
validation_data=(X_test, ytest),
epochs=10,
batch_size=10,
verbose=2) # Actual Epoch 50. Changing to 10 for current demo
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)
##Transformback to original form --- rescaling
train_predict = scaler.inverse_transform(train_predict)
test_predict = scaler.inverse_transform(test_predict)
### Plotting
# shift train predictions for plotting
look_back = 100
trainPredictPlot = np.empty_like(df1)
trainPredictPlot[:, :] = np.nan
trainPredictPlot[look_back:len(train_predict) +
look_back, :] = train_predict
# shift test predictions for plotting
testPredictPlot = np.empty_like(df1)
testPredictPlot[:, :] = np.nan
testPredictPlot[len(train_predict) + (look_back * 2) + 1:len(df1) -
1, :] = test_predict
if request.args.get('stockname') == 'AAPL' or request.args.get(
'stockname') == 'TSLA':
x_input = test_data[341:].reshape(1, -1)
# elif request.args.get('stockname') == 'TSLA':
# x_input = test_data[341:].reshape(1,-1)
else:
x_input = test_data[331:].reshape(1, -1)
temp_input = list(x_input)
temp_input = temp_input[0].tolist()
# demonstrate prediction for next 10 days
from numpy import array
lst_output = []
n_steps = 100
i = 0
if (request.args.get('stockname')
== 'IDEA.NS') or (request.args.get('stockname')
== 'AAPL') or (request.args.get('stockname')
== 'TSLA'):
while (i < 3):
if (len(temp_input) > 100):
x_input = np.array(temp_input[1:])
x_input = x_input.reshape(1, -1)
x_input = x_input.reshape((1, n_steps, 1))
yhat = model.predict(x_input, verbose=0)
temp_input.extend(yhat[0].tolist())
temp_input = temp_input[1:]
lst_output.extend(yhat.tolist())
i = i + 1
else:
x_input = x_input.reshape((1, n_steps, 1))
yhat = model.predict(x_input, verbose=0)
temp_input.extend(yhat[0].tolist())
lst_output.extend(yhat.tolist())
i = i + 1
else:
while (i < 10):
if (len(temp_input) > 100):
# print(temp_input)
x_input = np.array(temp_input[1:])
x_input = x_input.reshape(1, -1)
x_input = x_input.reshape((1, n_steps, 1))
# print(x_input)
yhat = model.predict(x_input, verbose=0)
temp_input.extend(yhat[0].tolist())
temp_input = temp_input[1:]
# print(temp_input)
lst_output.extend(yhat.tolist())
i = i + 1
else:
x_input = x_input.reshape((1, n_steps, 1))
yhat = model.predict(x_input, verbose=0)
temp_input.extend(yhat[0].tolist())
lst_output.extend(yhat.tolist())
i = i + 1
if (request.args.get('stockname')
== 'IDEA.NS') or (request.args.get('stockname')
== 'AAPL') or (request.args.get('stockname')
== 'TSLA'):
day_new = np.arange(1, 101) # testdata 100indexes
day_pred = np.arange(101, 104) # 101-131-predicted
# FOR RELIANCE
else:
day_new = np.arange(1, 101) # testdata 100indexes
day_pred = np.arange(101, 111) # 101-131-predicted
if (request.args.get('stockname')
== 'AAPL') or (request.args.get('stockname') == 'TSLA'):
numpyData3 = scaler.inverse_transform(df1[1158:])
# FOR TSLA
# elif (request.args.get('stockname') =='TSLA'):
# numpyData3 = scaler.inverse_transform(df1[1158:])
# FOR RELIANCE
else:
numpyData3 = scaler.inverse_transform(df1[1131:])
numpyData4 = scaler.inverse_transform(lst_output)
daynew = {"x": day_new.tolist(), "y": day_pred.tolist()}
daypred = {"x": numpyData3.tolist(), "y": numpyData4.tolist()}
data = []
data.append(daynew)
data.append(daypred)
final_data = {"data": data}
print(final_data)
return final_data
img_test = pd.read_csv(
'/Users/deangao/Desktop/CSM226/covidProject_data/radiomics/test_covid_normal_pn.csv'
)
all_data = pd.concat([img_train, img_test], axis=0)
img_train = img_train.drop(columns=['id', 'Entropy', 'Uniformity', 'Energy'])
img_test = img_test.drop(columns=['id', 'Entropy', 'Uniformity', 'Energy'])
all_data = all_data.drop(columns=['id', 'Entropy', 'Uniformity', 'Energy'])
# In[5]:
train_X, train_y = img_train.iloc[:, :-1], img_train.iloc[:, -1]
test_X, test_y = img_test.iloc[:, :-1], img_test.iloc[:, -1]
labels = {0: 'normal lung', 1: 'pneumonia lung', 2: 'covid lung'}
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(train_X)
scaler2 = MinMaxScaler()
scaled_all = scaler2.fit_transform(all_data.iloc[:, :-1])
scaled_all_data = pd.concat([
pd.DataFrame(all_data.iloc[:, -1].reset_index()),
pd.DataFrame(scaled_all, columns=train_X.columns)
],
axis=1)
scaled_all_data = scaled_all_data.drop(['index'], axis=1)
# In[6]:
all_data.shape
all_data.iloc[:, -1]
return df_training, df_test
# Import data sets, split into training and test sets
dataset_all = pd.read_csv('./datasets/' + dataset_csv)
dataset_training, dataset_test = create_training_test(dataset_all,
testing_records)
training_set = dataset_training.iloc[:, 1:7].values
###training_set = dataset_training.iloc[:, 4:5].values
test_set = dataset_test.iloc[:, 1:7].values
###test_set = dataset_test.iloc[:, 4:5].values
# Apply scaling
from sklearn.preprocessing import MinMaxScaler
scale = MinMaxScaler(feature_range=(0, 1))
# Apply scale object on data
training_set_scaled = scale.fit_transform(training_set)
X_train = []
y_train = []
for i in range(timesteps, training_set_scaled.shape[0]):
###X_train.append(training_set_scaled[i-timesteps:i, 0])
###y_train.append(training_set_scaled[i, 0])
X_train.append(training_set_scaled[i - timesteps:i, 0:6])
y_train.append(training_set_scaled[
i,
3:4]) #y_train.append(i, 3:4 ) or y_train.append(i, 0:4 ) ?, #79, 9:53
# Livro Ciência de Dados e Aprendizado de Máquina - https://www.amazon.com.br/dp/B07X1TVLKW
# Livro Inteligência Artificial com Python - Redes Neurais Intuitivas - https://www.amazon.com.br/dp/B087YSVVXW
# Livro Redes Neurais Artificiais - https://www.amazon.com.br/dp/B0881ZYYCJ
import numpy as np
import pandas as pd
from keras.models import Sequential
from keras.layers import Dense, Dropout
from sklearn.preprocessing import MinMaxScaler
from keras.layers import LSTM
import matplotlib.pyplot as plt
base = pd.read_csv('petr4-treinamento.csv')
base = base.dropna()
base_treino = base.iloc[:, 1:2].values
normalizador = MinMaxScaler(feature_range = (0,1))
base_treino_normalizada = normalizador.fit_transform(base_treino)
previsores = []
preco_real = []
for i in range(90,1242):
previsores.append(base_treino_normalizada[i-90:i,0])
preco_real.append(base_treino_normalizada[i,0])
previsores, preco_real = np.array(previsores), np.array(preco_real)
previsores = np.reshape(previsores,(previsores.shape[0], previsores.shape[1],1))
regressor = Sequential()
regressor.add(LSTM(units = 100,
return_sequences = True,
a4 = Rain_test1[3 * 31 * 24:4 * 31 * 24].resample('3H').sum()
a5 = Rain_test1[4 * 31 * 24:5 * 31 * 24].resample('3H').sum()
Qi_test1['Rain_sum'] = pd.concat([a1, a2, a3, a4, a5],
axis=0,
ignore_index=True)
#%%
# 环境表数据
# T气温、w风向、wd风速 每天
Environmentdata = pd.read_excel('./data/环境表.xlsx')
# 填充缺失
Environmentdata['T'].fillna(method='ffill', inplace=True)
Environmentdata['w'].fillna(method='ffill', inplace=True)
# wd归一化
from sklearn.preprocessing import MinMaxScaler
ss = MinMaxScaler()
Environmentdata['wd'] = ss.fit_transform(Environmentdata['wd'].values.reshape(
-1, 1))
Environmentdata['TimeStample'] = pd.to_datetime(Environmentdata['TimeStample'],
format='%Y-%m-%d')
Environmentdata = dataframe_cut(Environmentdata,
begin_time='{}-01-01'.format(y_start),
end_time='{}-12-31'.format(y_end))
#%%
# 测试数据
Environmentdata_test = pd.read_excel('./final_data/环境表.xlsx')
# 填充缺失
Environmentdata_test['T'].fillna(method='ffill', inplace=True)
Environmentdata_test['w'].fillna(method='ffill', inplace=True)
# wd归一化
y, sr = librosa.load(wav_path, sr=None)
D = np.abs(
librosa.core.stft(y,
n_fft=640,
hop_length=160,
win_length=640,
window='hann'))
noise_txt_path = noise_path + "/{}".format("noise.txt")
n_D = np.loadtxt(noise_txt_path)
b = int((D.shape[1] - n_D.shape[1]) / 2)
a = D.shape[1] - n_D.shape[1] - b
n_D = np.pad(n_D, ((0, 0), (a, b)), 'constant')
D_T = (librosa.amplitude_to_db(D, ref=np.max)).T
minMax = MinMaxScaler()
audio_feature_std = minMax.fit_transform(D_T)
print("original audio_feature shape", audio_feature_std.shape)
length = int((audio_feature_std.shape)[0])
original_feature = audio_feature_std
if length == 4 * int(length / 4):
loop_num = int(length / 4)
else:
loop_num = 1 + int(length / 4)
audio_feature = np.zeros((loop_num, 1284), dtype=np.float32)
PAD = np.zeros((1, 321), dtype=np.float32)
# lr.fit(x_dtrain, y_dtrain)
# predictions = lr.predict(x_test)
# print('AUC',roc_auc_score(y_test, predictions))
# print('准确率',accuracy_score(y_test,predictions))
# 拟合模型
# lr = LogisticRegression(C=1.0, penalty='l2', solver='newton-cg', class_weight='balanced')
# lr.fit(dataset12_x, dataset12_y)
# joblib.dump(lr, 'lr.model') # 保存训练的RF模型
# predict test set
lr = joblib.load('lr.model')
result = lr.predict_proba(dataset3_x)
result = pd.DataFrame(result)
result.index = dataset3.index
result.columns = ['0', 'probability']
result.drop('0',
axis=1,
inplace=True)
dataset3_preds['label'] = result.copy()
dataset3_preds.label = MinMaxScaler().fit_transform(dataset3_preds.label.reshape(-1, 1))
dataset3_preds.sort_values(by=['Coupon_id', 'label'], inplace=True)
dataset3_preds.to_csv("lr_preds.csv", index=None, header=None)
print(dataset3_preds.describe())
cost_time = time.time() - start_time
print('cost_time', cost_time)
else:
Len = len(real_list)
total = 0
for i in range(Len):
total = total + abs(pred_list[i] / real_list[i] - 1)
return (total / Len)
## 创建model
look_back = 1
model = Sequential()
model.add(LSTM(32, input_shape=(24, 1)))
model.add(Dense(1))
model.compile(loss='mean_squared_error', optimizer='adam')
scaler = MinMaxScaler(feature_range=(0, 1)) # 正则化函数
MAPE_list = [0 for i in range(120)]
for i in range(120):
train_data_norm = scaler.fit_transform(pd.DataFrame(data_list[i][1:145]))
train_data_norm = np.array(train_data_norm).tolist()
train_data_norm = list(chain.from_iterable(train_data_norm))
# del(train_data_norm[0])
X, y = split_sequence(train_data_norm, 24)
n_features = 1
X = X.reshape((X.shape[0], X.shape[1], n_features))
# reshape input to be [samples, time steps, features]
#trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))
model.fit(X, y, epochs=50, batch_size=1, verbose=2)
pred_val = []
pred_data = train_data_norm[120:145]
print(df.shape)
print(df.columns)
print(df.head())
X = df.drop('benign_0__mal_1', axis=1).values
y = df['benign_0__mal_1'].values
print('X shape: ', X.shape, 'y shape: ', y.shape)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=101)
scaler = MinMaxScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
print('train shape(Data points, Feature): ', X_train)
early_stop = EarlyStopping(monitor='val_loss',
mode='min',
verbose=1,
patience=25)
model = Sequential()
#X_train feature = 30
model.add(Dense(30, activation='relu'))
def train_predict(pipe_data):
# lgb
param_grid = [{
'num_leaves': [20],
'min_data_in_leaf': [2, 3],
'objective': ['regression'],
'max_depth': [3, 4, 5],
'learning_rate': [0.06, 0.12, 0.24],
"min_child_samples": [3],
"boosting": ["gbdt"],
"feature_fraction": [0.7],
"bagging_freq": [1],
"bagging_fraction": [1],
"bagging_seed": [11],
"metric": ['mse'],
"lambda_l2": [0.0003, 0.001, 0.003],
"verbosity": [-1]
}]
lgb_best_params = find_best_params(pipe_data, lgb_predict, param_grid)
X_train, y_train, X_test, test_idx = split_data(pipe_data,
target_name='target')
min_max_scaler = MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train)
X_test = min_max_scaler.transform(X_test)
oof_lgb, predictions_lgb = lgb_predict(X_train,
y_train,
X_test,
params=lgb_best_params,
verbose_eval=200) #
# xgb
param_grid = [{
'silent': [1],
'nthread': [4],
'eval_metric': ['rmse'],
'eta': [0.03],
'objective': ['reg:linear'],
'max_depth': [4, 5, 6],
'num_round': [1000],
'subsample': [0.4, 0.6, 0.8, 1],
'colsample_bytree': [0.7, 0.9, 1],
}]
xgb_best_params = find_best_params(pipe_data, xgb_predict, param_grid)
X_train, y_train, X_test, test_idx = split_data(pipe_data,
target_name='target')
min_max_scaler = MinMaxScaler()
X_train = min_max_scaler.fit_transform(X_train)
X_test = min_max_scaler.transform(X_test)
oof_xgb, predictions_xgb = xgb_predict(X_train,
y_train,
X_test,
params=xgb_best_params,
verbose_eval=200) #
# 模型融合 stacking
train_stack = np.vstack([oof_lgb, oof_xgb]).transpose()
test_stack = np.vstack([predictions_lgb, predictions_xgb]).transpose()
folds_stack = RepeatedKFold(n_splits=5, n_repeats=2, random_state=4590)
oof_stack = np.zeros(train_stack.shape[0])
predictions = np.zeros(test_stack.shape[0])
for fold_, (trn_idx,
val_idx) in enumerate(folds_stack.split(train_stack, y_train)):
print("fold {}".format(fold_))
trn_data, trn_y = train_stack[trn_idx], y_train[trn_idx]
val_data, val_y = train_stack[val_idx], y_train[val_idx]
clf_3 = BayesianRidge()
clf_3.fit(trn_data, trn_y)
oof_stack[val_idx] = clf_3.predict(val_data)
predictions += clf_3.predict(test_stack) / 10
final_score = mean_squared_error(y_train, oof_stack)
print(final_score)
return predictions
