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How to use test_add method in Nose
Best Python code snippet using nose
ADAS13_RNN.py
Source:ADAS13_RNN.py 
1import pandas as pd2import conf3import src.RNN.X_Y_H as ref4from keras.models import Model5from keras.layers import Dense, Input6from keras.models import Sequential7from keras.layers import LSTM8from keras.callbacks import EarlyStopping9import src.preprocess.csv_utils as utils10import numpy as np11from src.evaluate import evaluateRegression12import sklearn13Target='ADAS13'14loss_train=[]15loss_test=[]16loss_validation=[]17loss_true_train=[]18def predict_state(Encoder,Model,target_df):19 target_dict = utils.build_ptid_split_dic(target_df)20 for i in range(len(target_df.index)):21 #print(i, 'in', len(target_df.index))22 id = target_df['PTID_Key'][i]23 start_in_train = target_dict[id][0]24 end_in_train = target_dict[id][1]25 if i!=start_in_train:26 X_test=target_df.loc[i-1,ref.h_index+ref.x_index].as_matrix(columns=None)27 temp=Encoder.predict(np.expand_dims(X_test, axis=0))28 for j in range(len(ref.h_index)):29 target_df.set_value(i,ref.h_index[j],temp[0,j])30 return target_df31def predict_result(Encoder,Model,target_df):32 target_dict = utils.build_ptid_split_dic(target_df)33 for i in range(len(target_df.index)):34 #print(i, 'in', len(target_df.index))35 id = target_df['PTID_Key'][i]36 start_in_train = target_dict[id][0]37 end_in_train = target_dict[id][1]38 if i != start_in_train:39 X_test = target_df.loc[i - 1, ref.h_index + ref.x_index].as_matrix(columns=None)40 temp = Encoder.predict(np.expand_dims(X_test, axis=0))41 for j in range(len(ref.h_index)):42 target_df.set_value(i, ref.h_index[j], temp[0, j])43 X_test_t = target_df.loc[i, ref.h_index + ref.x_index].as_matrix(columns=None)44 result=Model.predict(np.expand_dims(X_test_t, axis=0))45 target_df.set_value(i,Target,result)46 return target_df47def getTrain(input_df,train_df):48 train_df=pd.concat((input_df,train_df))49 #train=train.sort_values(by=['PTID_Key','M'])50 #train.index = pd.RangeIndex(len(train.index))51 #train=utils.dataCompen(train,Target)52 train = train_df.dropna(axis=0, subset=[Target])53 return train54epochs=1055input_shift=pd.read_csv(conf.intermediate_dir+'input_shift.csv')56train_add=pd.read_csv(conf.intermediate_dir+'train_add.csv')57train=getTrain(input_shift,train_add)58train=input_shift59X_train=train[ref.h_index+ref.x_index].values60Y_train=train[Target].values61input=Input(shape=(X_train.shape[1],))62hidden1=Dense(30, activation='relu')(input)63hidden1_1=Dense(20, activation='relu')(hidden1)64hidden1_2=Dense(20,activation='relu')(hidden1_1)65hidden2=Dense(len(ref.h_index),activation='relu')(hidden1_2)66hidden2_1=Dense(20,activation='relu')(hidden2)67output=Dense(1, activation='relu')(hidden2_1)68encoder=Model(input,hidden2)69model = Model(input,output)70model.compile(loss='mean_squared_error', optimizer='adam')71val=100000072preval=val*273callback = [74 EarlyStopping(monitor='val_loss', patience=2, min_delta=0.001, verbose=0)75 ]76X_train_1=X_train[0:6870,:]77Y_train_1=Y_train[0:6870]78model.fit(X_train, Y_train, epochs=20, verbose=1,callbacks=callback,validation_split=0.1)79train_add=predict_state(encoder,model,train_add)80train = getTrain(input_shift, train_add)81X_train = train[ref.h_index + ref.x_index].values82Y_train=train[Target]83for i in range(40):84 #if(abs(preval-val)<1):85 # break86 his=model.fit(X_train, Y_train, epochs=1, verbose=2)87 val_loss=his.history['loss']88 preval=val89 val=val_loss[-1]90 train_add=predict_state(encoder,model,train_add)91 train = getTrain(input_shift, train_add)92 X_train = train[ref.h_index + ref.x_index].values93 ############get every time loss######################94 loss_train.append(val)95 test_add = pd.read_csv(conf.intermediate_dir + 'test_add.csv')96 test_result = predict_result(encoder, model, test_add)97 test_add = pd.read_csv(conf.intermediate_dir + 'test_add.csv')98 loss=evaluateRegression(test_add, test_result)[0][1]99 loss_test.append(loss)100 ##validation101 test_add = pd.read_csv(conf.intermediate_dir + 'val_add.csv')102 test_result = predict_result(encoder, model, test_add)103 test_add = pd.read_csv(conf.intermediate_dir + 'val_add.csv')104 loss = evaluateRegression(test_add, test_result)[0][1]105 loss_validation.append(loss)106 test_add = pd.read_csv(conf.intermediate_dir + 'train_add.csv')107 test_result = predict_result(encoder, model, test_add)108 test_add = pd.read_csv(conf.intermediate_dir + 'train_add.csv')109 loss = evaluateRegression(test_add, test_result)[0][1]110 loss_true_train.append(loss)111print(loss_train)112print(loss_test)113print(loss_validation)114print(loss_true_train)115test_add=pd.read_csv(conf.intermediate_dir+'test_add.csv')116test_result=predict_result(encoder,model,test_add)117test_add=pd.read_csv(conf.intermediate_dir+'test_add.csv')118evaluateRegression(test_add,test_result)119test_result.to_csv(conf.result_dir+'test_predict.csv')120val_add=pd.read_csv(conf.intermediate_dir+'val_add.csv')121val_result=predict_result(encoder,model,val_add)122try:123 val_predict=pd.read_csv(conf.intermediate_dir+'val_predict.csv')124 val_predict[Target]=val_result[Target]125 val_predict.to_csv(conf.result_dir+'val_predict.csv',index=False)126except:...
RNN.py
Source:RNN.py
1import pandas as pd2import src.RNN.X_Y_H as ref3from keras.models import Model4from keras.layers import Dense, Input,Dropout5from keras.models import Sequential6from keras.layers import LSTM7from keras.callbacks import EarlyStopping8import src.preprocess.csv_utils as utils9import numpy as np10from src.evaluate import evaluateRegression11import sklearn12import conf13Target='MMSE'14loss_train=[]15loss_test=[]16loss_validation=[]17loss_true_train=[]18def predict_state(Encoder,Model,target_df):19 target_dict = utils.build_ptid_split_dic(target_df)20 for i in range(len(target_df.index)):21 #print(i, 'in', len(target_df.index))22 id = target_df['PTID_Key'][i]23 start_in_train = target_dict[id][0]24 end_in_train = target_dict[id][1]25 if i!=start_in_train:26 X_test=target_df.loc[i-1,ref.h_index+ref.x_index].as_matrix(columns=None)27 temp=Encoder.predict(np.expand_dims(X_test, axis=0))28 for j in range(len(ref.h_index)):29 target_df.set_value(i,ref.h_index[j],temp[0,j])30 return target_df31def predict_result(Encoder,Model,target_df):32 target_dict = utils.build_ptid_split_dic(target_df)33 for i in range(len(target_df.index)):34 #print(i, 'in', len(target_df.index))35 id = target_df['PTID_Key'][i]36 start_in_train = target_dict[id][0]37 end_in_train = target_dict[id][1]38 if i != start_in_train:39 X_test = target_df.loc[i - 1, ref.h_index + ref.x_index].as_matrix(columns=None)40 temp = Encoder.predict(np.expand_dims(X_test, axis=0))41 for j in range(len(ref.h_index)):42 target_df.set_value(i, ref.h_index[j], temp[0, j])43 X_test_t = target_df.loc[i, ref.h_index + ref.x_index].as_matrix(columns=None)44 result=Model.predict(np.expand_dims(X_test_t, axis=0))45 target_df.set_value(i,Target,result)46 return target_df47def getTrain(input_df,train_df):48 #train_df=pd.concat((input_df,train_df))49 #train=train.sort_values(by=['PTID_Key','M'])50 #train.index = pd.RangeIndex(len(train.index))51 #train=utils.dataCompen(train,Target)52 train = train_df.dropna(axis=0, subset=[Target])53 return train54epochs=1055input_shift=pd.read_csv(conf.intermediate_dir+'input_shift.csv')56train_add=pd.read_csv(conf.intermediate_dir+'train_add.csv')57train=getTrain(input_shift,train_add)58train=input_shift59X_train=train[ref.h_index+ref.x_index].values60Y_train=train[Target].values61input=Input(shape=(X_train.shape[1],))62hidden1=Dense(100, activation='relu')(input)63hidden1_1=Dense(200, activation='relu')(hidden1)64hidden2=Dense(len(ref.h_index),activation='relu')(hidden1_1)65hidden2_1=Dense(200,activation='relu')(hidden2)66hidden2_2=Dropout(0.1)(hidden2_1)67output=Dense(1, activation='relu')(hidden2_2)68encoder=Model(input,hidden2)69model = Model(input,output)70model.compile(loss='mean_squared_error', optimizer='adam')71val=100000072preval=val*273callback = [74 EarlyStopping(monitor='val_loss', patience=2, min_delta=0.01, verbose=0)75 ]76model.fit(X_train, Y_train, epochs=20, verbose=1,callbacks=callback,validation_split=0.1)77train_add=predict_state(encoder,model,train_add)78train = getTrain(input_shift, train_add)79X_train = train[ref.h_index + ref.x_index].values80Y_train=train[Target]81for i in range(40):82 #if(abs(preval-val)<1):83 # break84 his=model.fit(X_train, Y_train, epochs=1, verbose=2)85 val_loss=his.history['loss']86 preval=val87 val=val_loss[-1]88 train_add=predict_state(encoder,model,train_add)89 train = getTrain(input_shift, train_add)90 X_train = train[ref.h_index + ref.x_index].values91 ############get every time loss######################92 loss_train.append(val)93 test_add = pd.read_csv(conf.intermediate_dir + 'test_add.csv')94 test_result = predict_result(encoder, model, test_add)95 test_add = pd.read_csv(conf.intermediate_dir + 'test_add.csv')96 loss=evaluateRegression(test_add, test_result)[0][1]97 loss_test.append(loss)98 ##validation99 test_add = pd.read_csv(conf.intermediate_dir + 'val_add.csv')100 test_result = predict_result(encoder, model, test_add)101 test_add = pd.read_csv(conf.intermediate_dir + 'val_add.csv')102 loss = evaluateRegression(test_add, test_result)[0][1]103 loss_validation.append(loss)104 '''105 test_add = pd.read_csv(conf.intermediate_dir + 'train_add.csv')106 test_result = predict_result(encoder, model, test_add)107 test_add = pd.read_csv(conf.intermediate_dir + 'train_add.csv')108 loss = evaluateRegression(test_add, test_result)[0][1]109 loss_true_train.append(loss)110 '''111print(loss_train)112print(loss_test)113print(loss_validation)114print(loss_true_train)115test_add=pd.read_csv(conf.intermediate_dir+'test_add.csv')116test_result=predict_result(encoder,model,test_add)117test_add=pd.read_csv(conf.intermediate_dir+'test_add.csv')118evaluateRegression(test_add,test_result)119test_result.to_csv(conf.result_dir+'test_predict.csv')120val_add=pd.read_csv(conf.intermediate_dir+'val_add.csv')121val_result=predict_result(encoder,model,val_add)122try:123 val_predict=pd.read_csv(conf.intermediate_dir+'val_predict.csv')124 val_predict[Target]=val_result[Target]125 val_predict.to_csv(conf.result_dir+'val_predict.csv',index=False)126except:...
VN_RNN.py
Source:VN_RNN.py
1import pandas as pd2import src.RNN.X_Y_H as ref3from keras.models import Model,optimizers4from keras.layers import Dense, Input,Dropout5from keras.models import Sequential6from keras.layers import LSTM7from keras.callbacks import EarlyStopping8import src.preprocess.csv_utils as utils9import numpy as np10from src.evaluate import evaluateRegression11import conf12Target='Ventricles_Norm'13loss_train=[]14loss_test=[]15loss_validation=[]16loss_true_train=[]17def predict_state(Encoder,Model,target_df):18 target_dict = utils.build_ptid_split_dic(target_df)19 for i in range(len(target_df.index)):20 #print(i, 'in', len(target_df.index))21 id = target_df['PTID_Key'][i]22 start_in_train = target_dict[id][0]23 end_in_train = target_dict[id][1]24 if i!=start_in_train:25 X_test=target_df.loc[i-1,ref.h_index+ref.x_index].as_matrix(columns=None)26 temp=Encoder.predict(np.expand_dims(X_test, axis=0))27 for j in range(len(ref.h_index)):28 target_df.set_value(i,ref.h_index[j],temp[0,j])29 return target_df30def predict_result(Encoder,Model,target_df):31 target_dict = utils.build_ptid_split_dic(target_df)32 for i in range(len(target_df.index)):33 #print(i, 'in', len(target_df.index))34 id = target_df['PTID_Key'][i]35 start_in_train = target_dict[id][0]36 end_in_train = target_dict[id][1]37 if i != start_in_train:38 X_test = target_df.loc[i - 1, ref.h_index + ref.x_index].as_matrix(columns=None)39 temp = Encoder.predict(np.expand_dims(X_test, axis=0))40 for j in range(len(ref.h_index)):41 target_df.set_value(i, ref.h_index[j], temp[0, j])42 X_test_t = target_df.loc[i, ref.h_index + ref.x_index].as_matrix(columns=None)43 result=Model.predict(np.expand_dims(X_test_t, axis=0))44 target_df.set_value(i,Target,result)45 return target_df46def getTrain(input_df,train_df):47 #train_df=pd.concat((input_df,train_df))48 #train=train.sort_values(by=['PTID_Key','M'])49 #train.index = pd.RangeIndex(len(train.index))50 #train=utils.dataCompen(train,Target)51 train = train_df.dropna(axis=0, subset=[Target])52 return train53epochs=1054input_shift=pd.read_csv(conf.intermediate_dir+'input_shift.csv')55train_add=pd.read_csv(conf.intermediate_dir+'train_add.csv')56train=getTrain(input_shift,train_add)57train=input_shift58X_train=train[ref.h_index+ref.x_index].values59Y_train=train[Target].values60input=Input(shape=(X_train.shape[1],))61hidden1=Dense(100, activation='relu')(input)62hidden1_1=Dense(100, activation='relu')(hidden1)63hidden2=Dense(len(ref.h_index),activation='relu')(hidden1_1)64hidden2_1=Dense(100,activation='relu')(hidden2)65output=Dense(1, activation='relu')(hidden2_1)66encoder=Model(input,hidden2)67model = Model(input,output)68my_opti=optimizers.rmsprop(decay=0.0000001)69model.compile(loss='mean_squared_error', optimizer='rmsprop')70val=100000071preval=val*272callback = [73 EarlyStopping(monitor='val_loss', patience=2, min_delta=0.0000001, verbose=0)74 ]75model.fit(X_train, Y_train, epochs=20)76train_add=predict_state(encoder,model,train_add)77train = getTrain(input_shift, train_add)78X_train = train[ref.h_index + ref.x_index].values79Y_train=train[Target]80for i in range(40):81 #if(abs(preval-val)<1):82 # break83 his=model.fit(X_train, Y_train, epochs=1, verbose=2)84 val_loss=his.history['loss']85 preval=val86 val=val_loss[-1]87 train_add=predict_state(encoder,model,train_add)88 train = getTrain(input_shift, train_add)89 X_train = train[ref.h_index + ref.x_index].values90 ############get every time loss######################91 loss_train.append(val)92 test_add = pd.read_csv(conf.intermediate_dir + 'test_add.csv')93 test_result = predict_result(encoder, model, test_add)94 test_add = pd.read_csv(conf.intermediate_dir + 'test_add.csv')95 loss=evaluateRegression(test_add, test_result)[0][1]96 loss_test.append(loss)97 ##validation98 test_add = pd.read_csv(conf.intermediate_dir + 'val_add.csv')99 test_result = predict_result(encoder, model, test_add)100 test_add = pd.read_csv(conf.intermediate_dir + 'val_add.csv')101 loss = evaluateRegression(test_add, test_result)[0][1]102 loss_validation.append(loss)103 '''104 test_add = pd.read_csv(conf.intermediate_dir + 'train_add.csv')105 test_result = predict_result(encoder, model, test_add)106 test_add = pd.read_csv(conf.intermediate_dir + 'train_add.csv')107 loss = evaluateRegression(test_add, test_result)[0][1]108 loss_true_train.append(loss)109 '''110print(loss_train)111print(loss_test)112print(loss_validation)113print(loss_true_train)114test_add=pd.read_csv(conf.intermediate_dir+'test_add.csv')115test_result=predict_result(encoder,model,test_add)116test_add=pd.read_csv(conf.intermediate_dir+'test_add.csv')117evaluateRegression(test_add,test_result)118test_result.to_csv(conf.result_dir+'test_predict.csv')119val_add=pd.read_csv(conf.intermediate_dir+'val_add.csv')120val_result=predict_result(encoder,model,val_add)121try:122 val_predict=pd.read_csv(conf.intermediate_dir+'val_predict.csv')123 val_predict[Target]=val_result[Target]124 val_predict.to_csv(conf.result_dir+'val_predict.csv',index=False)125except:...
test_signals.py
Source:test_signals.py
...28 def getStockHigh(self):29 result = signals.getCurrentPrice('AIB')30 self.assertEquals(result, 15)31 self.assertEquals(signals.add(10, 5), 15)32 def test_add(self):33 result = signals.getCurrentPrice('AIB')34 self.assertEquals(result, 15)35 self.assertEquals(signals.add(10, 5), 15)36class TestStockLow(unittest.TestCase):37 def test_add(self):38 result = signals.getCurrentPrice('AIB')39 self.assertEquals(result, 15)40 self.assertEquals(signals.add(10, 5), 15)41 def test_add(self):42 result = signals.getCurrentPrice('AIB')43 self.assertEquals(result, 15)44 self.assertEquals(signals.add(10, 5), 15)45class TestStockOpen(unittest.TestCase):46 def test_add(self):47 result = signals.getCurrentPrice('AIB')48 self.assertEquals(result, 15)49 self.assertEquals(signals.add(10, 5), 15)50 def test_add(self):51 result = signals.add(10, 5)52 self.assertEquals(result, 15)53 self.assertEquals(signals.add(10, 5), 15)54class TestStockClose(unittest.TestCase):55 def test_add(self):56 result = signals.getCurrentPrice('AIB')57 self.assertEquals(result, 15)58 self.assertEquals(signals.add(10, 5), 15)59 def test_add(self):60 result = signals.getCurrentPrice('AIB')61 self.assertEquals(result, 15)62 self.assertEquals(signals.add(10, 5), 15)63class TestStockVolume(unittest.TestCase):64 def test_add(self):65 result = signals.getCurrentPrice('AIB')66 self.assertEquals(result, 15)67 self.assertEquals(signals.add(10, 5), 15)68 def test_add(self):69 result = signals.getCurrentPrice('AIB')70 self.assertEquals(result, 15)71 self.assertEquals(signals.add(10, 5), 15)72class TestUpdateStockPrice(unittest.TestCase):73 def test_add(self):74 result = signals.getCurrentPrice('AIB')75 self.assertEquals(result, 15)76 self.assertEquals(signals.add(10, 5), 15)77 def test_add(self):78 result = signals.getCurrentPrice('AIB')79 self.assertEquals(result, 15)80 self.assertEquals(signals.add(10, 5), 15)81if __name__ == '__main__':...
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