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phytoplankton diversity prediction model.py
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phytoplankton diversity prediction model.py
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# 多样性建模 ---- 三个数据不足的因素，缺失值补足
from heamy.dataset import Dataset
from heamy.estimator import Regressor
from heamy.pipeline import ModelsPipeline
from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import LinearRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.ensemble import ExtraTreesRegressor
from itertools import combinations
from sklearn import metrics
import numpy as np
import pandas as pd
import itertools
import math
import random
import joblib
# create dataset
def DataCleanRA(data):
#data = data.drop('time', axis=1)
#RA anslysis
data = data.dropna() #delete all rows with NaN
data = data.drop(data[(data.times <= 5)].index)
data = data.drop(data[(data.rate <= 1.5)].index)
data = data[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T', 'diversity']]
print(data)
#data['lat'] = data['lat'].map(lambda x: math.sin(math.radians(x)))
#data['lon'] = data['lon'].map(lambda x: math.cos(math.radians(x)))
X_train, X_test, y_train, y_test = Train_Test(data)
return X_train, X_test, y_train, y_test
def Train_Test(data):
n = range(len(data))
data.index = range(len(data))
test_size = 0.1
random.seed(1)
test_n = random.sample(n,int(test_size*len(data)))
test = data.loc[test_n]
train_n = []
for i in range(len(data)):
if i in test_n:
i = i
else: train_n.append(i)
train = data.loc[train_n]
X_train = train.values[:,:-1]
y_train = train.values[:,-1]
X_test = test.values[:,:-1]
y_test = test.values[:,-1]
return X_train, X_test, y_train, y_test
def StackModel(X_train, y_train, X_test):
dataset = Dataset(X_train,y_train,X_test)
#build models
model_rf = Regressor(dataset=dataset, estimator=RandomForestRegressor, parameters={'n_estimators': 1000, 'random_state' : 1, 'n_jobs':8, 'max_depth':26},name='rf')
model_et = Regressor(dataset=dataset, estimator=ExtraTreesRegressor, parameters={'n_estimators': 1000, 'max_depth':26, 'random_state':10},name='et')
model_knn = Regressor(dataset=dataset, estimator=KNeighborsRegressor, parameters={'n_neighbors': 25},name='knn')
model_gb = Regressor(dataset=dataset, estimator=GradientBoostingRegressor, parameters={'n_estimators': 1000, 'learning_rate':0.10, 'max_depth':26,'random_state':4},name='gb')
model_bg = Regressor(dataset=dataset, estimator=BaggingRegressor, parameters={'n_estimators':1000, 'max_samples':500, 'max_features': 1.0,'random_state' : 1}, name='bg')
# Stack three models
# Returns new dataset with out-of-fold predictions
pipeline = ModelsPipeline(model_rf,model_et,model_knn, model_gb, model_bg)
stack_ds = pipeline.stack(k=10,seed=11)
#stacking
stacker = Regressor(dataset=stack_ds, estimator=BaggingRegressor, parameters={'n_estimators':500, 'max_samples':500, 'max_features': 1.0,'random_state' : 2})
results = stacker.predict()
#joblib.dump(stacker, 'stacker_total_PRED_ra.model')
return results
def Validate(results):
#results_rmse = stacker.validate(k=10,scorer=mean_absolute_error)
#results_r2 = stacker.validate(k=10,scorer=r2_score)
r2 = (pd.Series(y_test)).corr(pd.Series(results))
rmse = np.sqrt(metrics.mean_squared_error(y_test, results))
return r2, rmse
def permutation_importances(X_train, y_train, X_test, metric):
baseline = metric.copy()
X_test0 = pd.DataFrame(X_test)
save = X_test0.copy()
dd = {}
for col in X_test0.columns:
imp_rmse = []
for n in range(3):
print(n)
np.random.seed(n)
X_test0[col] = np.random.permutation(X_test0[col])
r20, rmse0 = Validate(StackModel(X_train, y_train, X_test0))
X_test0 = save.copy()
imp_rmse.append((rmse0-baseline)/baseline)
print(imp_rmse)
dd[col] = imp_rmse
dd = pd.DataFrame(dd)
return dd
def ICE_caculate_save(X_train, X_test, save_name):
col_names = ('silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T')
X_test = pd.DataFrame(X_test)
X_train = pd.DataFrame(X_train)
X_data = pd.concat([X_train, X_test], axis=0)
X_data.columns = col_names
#X_data.to_csv('D:/%s_ice_1112_data_%s.csv'%(save_name, 'X_data'))
col_names = list(col_names)
for col_i in col_names:
save = X_data.copy()
col_du = X_data[col_i]
col_du = pd.DataFrame(col_du)
col_du = col_du.drop_duplicates()
col_du = col_du[col_i].tolist()
save_i = pd.DataFrame()
if len(col_du) < 20:
for x in col_du:
print('===============original X_data===================')
print(X_data)
print('================changed X_data==================')
X_data[col_i].loc[X_data[col_i] > -1000] =x
print(X_data)
print('==================================')
X_data = X_data.values
pred = StackModel(X_train, y_train, X_data)
pred = pd.DataFrame(pred)
save_i = pd.concat([save_i, pred], axis=1)
X_data = save.copy()
save_i.columns = col_du
else:
col_du = np.array(col_du)
col_du_perc = []
for i in [5,15,25,35,45,65,75,85,95,100]:
col_du_perc.append(np.percentile(col_du, i))
for m in col_du_perc:
print('===============original X_data===================')
print(X_data)
print('================changed X_data==================')
X_data[col_i].loc[X_data[col_i] > -1000] = m
print(X_data)
X_data = X_data.values
pred = StackModel(X_train, y_train, X_data)
pred = pd.DataFrame(pred)
save_i = pd.concat([save_i, pred], axis=1)
X_data = save.copy()
save_i.columns = col_du_perc
print(save_i)
save_i.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/%s_ice_%s.csv'%(save_name, col_i))
X_data = save.copy()
def ICE_caculate(X_train, X_test, save_name):
col_names = ('silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T')
X_test = pd.DataFrame(X_test)
X_train = pd.DataFrame(X_train)
X_data = pd.concat([X_train, X_test], axis=0)
X_data.columns = col_names
X_data.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/%s.ice.Xdata_%s.csv'%(save_name, 'X_data'))
col_names = list(col_names)
ana_v = ['CO2','T']
for col_i in list(combinations(ana_v,2)):
save = X_data.copy()
col_1 = X_data[col_i[0]]
col_2 = X_data[col_i[1]]
col_1 = pd.DataFrame(col_1)
col_2 = pd.DataFrame(col_2)
col_1 = col_1.drop_duplicates()
col_2 = col_2.drop_duplicates()
col_1 = col_1[col_i[0]].tolist()
col_2 = col_2[col_i[1]].tolist()
col_1 = np.array(col_1)
col_2 = np.array(col_2)
col_1_perc = []
col_2_perc = []
for i in range(20):
col_1_perc.append(np.percentile(col_1, (i+1)*5))
col_2_perc.append(np.percentile(col_2, (i+1)*5))
m_save = {}
for m in col_1_perc:
save_i = pd.DataFrame()
n_save = []
for n in col_2_perc:
print('===============original X_data===================')
print(X_data)
print('================changed X_data==================')
X_data[col_i[0]].loc[X_data[col_i[0]] > -1000] = m
X_data[col_i[1]].loc[X_data[col_i[1]] > -1000] = n
print(X_data)
print("====CO2, T====")
print(X_data[['CO2','T']])
X_data = X_data.values
pred = StackModel(X_train, y_train, X_data)
pred = pd.DataFrame(pred)
pred.columns = [n]
save_i = pd.concat([save_i, pred], axis=1)
n_save.append(n)
X_data = save.copy()
m_save[m] = n_save
save_i.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/%s_%s_ice_0112_%s.csv'%(save_name,col_i,m))
print(save_i)
m_save.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/%s_ice_mn0112_%s.csv'%(save_name, col_i))
def shap(data,num):
cols = ['silicate', 'nitrate', 'phosphate','NPP', 'salinity']
total_col = ['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T']
for i in itertools.permutations(cols, 5):
print(i)
p1 = data[total_col].copy()
save1 = 0
shap_v = []
shap_get = pd.DataFrame()
for ii in i:
save2 = save1
col_ii = ii+num
print('original',p1[ii])
print('changed',data[col_ii])
p1[ii] = data[col_ii]
print(p1)
pv = p1.values
p_result = StackModel(X_train, y_train, pv)
save1 = ave_div(p_result,data)
shap_v.append(save1-save2)
print(shap_v)
p1 = data[total_col].copy()
shap_get[i]=shap_v
shap_get = pd.DataFrame(shap_get)
return shap_get
def ave_div(p_result,data):
ave_d = pd.DataFrame()
ave_d['latlon'] = data['latitude'].map(str)+'|'+data['longitude'].map(str)
ave_d['pred'] = p_result
a = ave_d.groupby('latlon')['pred'].mean()
get = pd.DataFrame(a)
return get['pred'].mean()
print("- - - - - - - - - - - - - - - - - - - - - - - - - - -")
print("=======================div_data_class==============================")
print("- - - - - - - - - - - - - - - - - - - - - - - - - - -")
#data = pd.read_csv("E:/project/div_all/div_all.csv", encoding='unicode_escape')
#data = pd.read_csv("E:/project/div_multi/div_class_model.csv", encoding='unicode_escape')
data = pd.read_csv("E:/project/div_all/div_all_times_rate/new_div_all/div_all_new.csv", encoding='unicode_escape')
data = data[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T', 'diversity', 'times','rate']]
print(data)
X_train, X_test, y_train, y_test = DataCleanRA(data)
results = StackModel(X_train, y_train, X_test)
#data_pt = pd.DataFrame({'y_test':y_test, 'results':results}) #save the prediction with y test
#data_pt.to_csv('C:/python/total_PRED_pt_ra.csv')
r2, rmse = Validate(results)
print("====diversity R2====")
print(r2)
print("====diversity RMSE====")
print(rmse)
#ICE_caculate_save(X_train, X_test, 'div_all_new')
#imp_test = permutation_importances(X_train, y_train, X_test, rmse)
#imp_test.to_csv('E:/project/div_all/div_all_time_rate_importance_test.csv')
'''
p = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/TCO2_others_1222.csv", encoding='unicode_escape')
p1 = p.dropna()
print(p1.columns)
shap_85 = shap(p1,'8.5')
shap_85.to_csv('E:/project/div_all/div_all_times_rate/div_time_rate_shap_8.5.csv')
shap_26 = shap(p1,'2.6')
shap_26.to_csv('E:/project/div_all/div_all_times_rate/div_time_rate_shap_2.6.csv')
'''
#ICE_caculate_save(X_train, X_test, 'div_all_times_rate')
'''
p0 = pd.read_csv("E:/project/div_all/div_all_times_rate/new_div_all/div_new_data_creating_for_2D_plot.csv", encoding='unicode_escape')
p = p0[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T']]
pred = p.values
results_p = StackModel(X_train, y_train, pred)
print(results_p)
p0['diversity'] = results_p
p0.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/多因素/div_代表数据生成_for_2D.csv')
#单因素多因素RCP2.6 RCP8.5
p = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/TCO2_others_1222.csv", encoding='unicode_escape')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity8.5', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_salinity8.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity2.6', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_salinity2.6.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_T8.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T2.6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_T2.6.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO28.5', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO28.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO22.6', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO22.6.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO28.5', 'NPP', 'PAR', 'salinity8.5', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO2Tsaliniy8.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO22.6', 'NPP', 'PAR', 'salinity2.6', 'T2.6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO2Tsalinity2.6.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO28.5', 'NPP8.5', 'PAR', 'salinity', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO2TNPP8.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO22.6', 'NPP2.6', 'PAR', 'salinity', 'T2.6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO2TNPP2.6.csv')
p1 = p[['silicate', 'nitrate', 'phosphate8.5', 'CHL', 'CO28.5', 'NPP', 'PAR', 'salinity', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO2Tphosphate8.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate2.6', 'CHL', 'CO22.6', 'NPP', 'PAR', 'salinity', 'T2.6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO2Tphosphate2.6.csv')
p1 = p[['silicate', 'nitrate8.5', 'phosphate', 'CHL', 'CO28.5', 'NPP', 'PAR', 'salinity', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO2Tnitrate8.5.csv')
p1 = p[['silicate', 'nitrate2.6', 'phosphate', 'CHL', 'CO22.6', 'NPP', 'PAR', 'salinity', 'T2.6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO2Tnitrate2.6.csv')
p1 = p[['silicate8.5', 'nitrate', 'phosphate', 'CHL', 'CO28.5', 'NPP', 'PAR', 'salinity', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO2Tsilicate8.5.csv')
p1 = p[['silicate2.6', 'nitrate', 'phosphate', 'CHL', 'CO22.6', 'NPP', 'PAR', 'salinity', 'T2.6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_CO2Tsilicate2.6.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity8.5', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_silicate8.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity2.6', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_silicate2.6.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP8.5', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_NPP8.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP2.6', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_NPP2.6.csv')
p1 = p[['silicate', 'nitrate', 'phosphate8.5', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_phosphate8.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate2.6', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_phosphate2.6.csv')
p1 = p[['silicate', 'nitrate8.5', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_nitrate8.5.csv')
p1 = p[['silicate', 'nitrate2.6', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_nitrate2.6.csv')
p1 = p[['silicate8.5', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_silicate8.5.csv')
p1 = p[['silicate2.6', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_0821_silicate2.6.csv')
'''
#SHAP
'''
#T, CO2, OTHER
p = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/TCO2_others_1222.csv", encoding='unicode_escape')
p1 = p[['silicate2.6', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO28.5', 'NPP8.5', 'PAR', 'salinity8.5', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_onlysili2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate8.5', 'CHL', 'CO28.5', 'NPP8.5', 'PAR', 'salinity8.5', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_onlysili_ni2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO28.5', 'NPP8.5', 'PAR', 'salinity8.5', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_onlysili_ni_pho2.6.csv')
p1 = p[['silicate8.5', 'nitrate2.6', 'phosphate8.5', 'CHL', 'CO28.5', 'NPP8.5', 'PAR', 'salinity8.5', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_onlyni2.6.csv')
p1 = p[['silicate8.5', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO28.5', 'NPP8.5', 'PAR', 'salinity8.5', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_onlyni_pho2.6.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate2.6', 'CHL', 'CO28.5', 'NPP8.5', 'PAR', 'salinity8.5', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_onlypho2.6.csv')
p1 = p[['silicate2.6', 'nitrate8.5', 'phosphate2.6', 'CHL', 'CO28.5', 'NPP8.5', 'PAR', 'salinity8.5', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_onlypho_sili2.6.csv')
'''
'''
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_shap_T8.5.csv')
#T, OTHER, CO2
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO2', 'NPP2.6', 'PAR', 'salinity2.6', 'T2.6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_shap_Tother2.6.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO2', 'NPP8.5', 'PAR', 'salinity8.5', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_shap_Tother8.5.csv')
#CO2, T, OTHER
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO22.6', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_shap_CO22.6.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO28.5', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_shap_CO28.5.csv')
#CO2, OTHER, T
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO22.6', 'NPP2.6', 'PAR', 'salinity2.6', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_shap_CO2other2.6.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO28.5', 'NPP8.5', 'PAR', 'salinity8.5', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_shap_CO2other8.5.csv')
#OTHER, T, CO2
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO2', 'NPP2.6', 'PAR', 'salinity2.6', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_shap_other2.6.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO2', 'NPP8.5', 'PAR', 'salinity8.5', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_shap_other8.5.csv')
'''
'''
p000 = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/CO2T_change_CO2.csv",encoding="unicode_escape")
p00 = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/TCO2_others_1222.csv", encoding='unicode_escape')
p01 = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/CO2Tother_两端10.csv", encoding='unicode_escape')
p02 = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/CO2Tother_两端10_1.csv", encoding='unicode_escape')
p03 = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/CO2Tother_两端10_2.csv", encoding='unicode_escape')
p04 = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/CO2Tother_两端10_3.csv", encoding='unicode_escape')
p100 = p000[['silicate','nitrate','phosphate','NPP','salinity']]
p10 = p00[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CO28.5', 'NPP8.5', 'salinity8.5', 'T8.5','CO22.6','NPP2.6','salinity2.6','T2.6']]
p11 = p01[['CHL','PAR','nitrate02','nitrate04','nitrate06','nitrate08','nitrate10','nitrate15','nitrate20','nitrate30','nitrate40']]
p12 = p02[['NPP02','NPP04','NPP06','NPP08','NPP10','NPP15','NPP20','NPP30','NPP40','phosphate02','phosphate04','phosphate06','phosphate08','phosphate10','phosphate15','phosphate20','phosphate30','phosphate40']]
p13 = p03[['silicate02','silicate04','silicate06','silicate08','silicate10','silicate15','silicate20','silicate30','silicate40','salinity02','salinity04','salinity06','salinity08','salinity10','salinity15','salinity20','salinity30','salinity40']]
p14 = p04[['CO202','CO204','CO206','CO208','CO210','CO215','CO220','CO230','CO240','T02','T04','T06','T08','T10','T15','T20','T30','T40']]
p = pd.concat([p100,p10,p11,p12,p13,p14],axis=1)
print("CO2Tother_10")
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO202', 'NPP', 'PAR', 'salinity', 'T02']]
print(p1)
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T02other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO204', 'NPP', 'PAR', 'salinity', 'T04']]
p1 = p1.dropna() #delete all rows with NaN
print(p1)
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T04other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO206', 'NPP', 'PAR', 'salinity', 'T06']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T06other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO208', 'NPP', 'PAR', 'salinity', 'T08']]
print(p1)
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T08other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO210', 'NPP', 'PAR', 'salinity', 'T10']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T10other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO215', 'NPP', 'PAR', 'salinity', 'T15']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T15other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO220', 'NPP', 'PAR', 'salinity', 'T20']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T20other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO230', 'NPP', 'PAR', 'salinity', 'T30']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T30other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO240', 'NPP', 'PAR', 'salinity', 'T40']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T40other2018.csv')
'''
'''
p01 = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/CO2T_change_CO2.csv", encoding='unicode_escape')
p02 = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/change_T.csv", encoding='unicode_escape')
p03 = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/TCO2_others_1222.csv", encoding='unicode_escape')
p11 = p01[['CO21','CO22','CO23','CO24','CO25','CO2_6','CO27','CO28','CO29','T2.6','T4.5','T6','T8.5']]
p12 = p02[['T1','T2','T3','T4','T5','T_6','T7','T8','T9','CO22.6','CO24.5','CO26','CO28.5']]
p13 = p03[['CHL','NPP','nitrate','PAR','phosphate','salinity','silicate','nitrate2.6','nitrate8.5','NPP2.6','NPP8.5','phosphate2.6','phosphate8.5','salinity2.6','salinity8.5','silicate2.6','silicate8.5']]
p = pd.concat([p11,p12,p13],axis=1)
print("CO2Tother2018")
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO22.6', 'NPP', 'PAR', 'salinity', 'T2.6']]
print(p1)
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T2.6other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO21', 'NPP', 'PAR', 'salinity', 'T1']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T1other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO22', 'NPP', 'PAR', 'salinity', 'T2']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T2other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO23', 'NPP', 'PAR', 'salinity', 'T3']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T3other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO24', 'NPP', 'PAR', 'salinity', 'T4']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T4other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO25', 'NPP', 'PAR', 'salinity', 'T5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T5other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2_6', 'NPP', 'PAR', 'salinity', 'T_6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T_6other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO27', 'NPP', 'PAR', 'salinity', 'T7']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T7other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO28', 'NPP', 'PAR', 'salinity', 'T8']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T8other2018.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO29', 'NPP', 'PAR', 'salinity', 'T9']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T9other2018.csv')
print("CO2Tother2.6")
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO21', 'NPP2.6', 'PAR', 'salinity2.6', 'T1']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T1other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO22', 'NPP2.6', 'PAR', 'salinity2.6', 'T2']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T2other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO23', 'NPP2.6', 'PAR', 'salinity2.6', 'T3']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T3other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO24', 'NPP2.6', 'PAR', 'salinity2.6', 'T4']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T4other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO25', 'NPP2.6', 'PAR', 'salinity2.6', 'T5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T5other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO2_6', 'NPP2.6', 'PAR', 'salinity2.6', 'T_6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T_6other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO27', 'NPP2.6', 'PAR', 'salinity2.6', 'T7']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T7other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO28', 'NPP2.6', 'PAR', 'salinity2.6', 'T8']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T8other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO29', 'NPP2.6', 'PAR', 'salinity2.6', 'T9']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T9other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO24.5', 'NPP2.6', 'PAR', 'salinity2.6', 'T4.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T4.5other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO26', 'NPP2.6', 'PAR', 'salinity2.6', 'T6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T6other2.6.csv')
p1 = p[['silicate2.6', 'nitrate2.6', 'phosphate2.6', 'CHL', 'CO28.5', 'NPP2.6', 'PAR', 'salinity2.6', 'T8.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T8.5other2.6.csv')
print("CO2Tother8.5")
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO22.6', 'NPP8.5', 'PAR', 'salinity8.5', 'T2.6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T2.6other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO21', 'NPP8.5', 'PAR', 'salinity8.5', 'T1']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_CO2T1other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO22', 'NPP8.5', 'PAR', 'salinity8.5', 'T2']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T2other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO23', 'NPP8.5', 'PAR', 'salinity8.5', 'T3']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T3other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO24', 'NPP8.5', 'PAR', 'salinity8.5', 'T4']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T4other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO25', 'NPP8.5', 'PAR', 'salinity8.5', 'T5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T5other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO2_6', 'NPP8.5', 'PAR', 'salinity8.5', 'T_6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T_6other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO27', 'NPP8.5', 'PAR', 'salinity8.5', 'T7']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T7other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO28', 'NPP8.5', 'PAR', 'salinity8.5', 'T8']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T8other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO29', 'NPP8.5', 'PAR', 'salinity8.5', 'T9']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T9other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO24.5', 'NPP8.5', 'PAR', 'salinity8.5', 'T4.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T4.5other8.5.csv')
p1 = p[['silicate8.5', 'nitrate8.5', 'phosphate8.5', 'CHL', 'CO26', 'NPP8.5', 'PAR', 'salinity8.5', 'T6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO2T6other8.5.csv')
'''
'''
p = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/CO2T_1222.csv", encoding='unicode_escape')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_2018.csv')
'''
'''
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T4.5']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_T4.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO2', 'NPP', 'PAR', 'salinity', 'T6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_T6.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO24.5', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO24.5.csv')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO26', 'NPP', 'PAR', 'salinity', 'T']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)
pred_result = pd.DataFrame(pred_result)
pred_result.to_csv('E:/project/div_all/div_all_times_rate/new_div_all/div_all_CO26.csv')
'''
'''
p = pd.read_csv("E:/project/ext-总结/巴黎协定_温度变化/CO2T_1222.csv", encoding='unicode_escape')
p1 = p[['silicate', 'nitrate', 'phosphate', 'CHL', 'CO22.6', 'NPP', 'PAR', 'salinity', 'T2.6']]
p1 = p1.dropna() #delete all rows with NaN
p_v = p1.values
pred_result = StackModel(X_train, y_train, p_v)