107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
import itertools
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import os
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from joblib import dump, load
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import pandas as pd
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model_dict = {
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"adb_ssa" : "model/SSA_ADB.joblib",
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"dtr_ssa": "model/SSA_DTR.joblib",
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"en_ssa":"model/SSA_ElasticNet.joblib",
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"gp_ssa":"model/SSA_GaussianProcessRegressor.joblib",
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"kn_ssa":"model/SSA_KNeighborsRegressor.joblib",
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"lasso_ssa":"model/SSA_Lasso.joblib",
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"lr_ssa":"model/SSA_LinearRegression.joblib",
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"rfr_ssa":"model/SSA_RFR.joblib",
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"ridge_ssa":"model/SSA_Ridge.joblib",
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"svr_ssa":"model/SSA_SVR.joblib",
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"xgb_ssa":"model/SSA_XGB.joblib",
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"adb_tpv" : "model/TPV_ADB.joblib",
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"gdbt_tpv": "model/TPV_GDBT.joblib",
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"en_tpv":"model/TPV_ElasticNet.joblib",
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"gp_tpv":"model/TPV_GaussianProcessRegressor.joblib",
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"kn_tpv":"model/TPV_KNeighborsRegressor.joblib",
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"lasso_tpv":"model/TPV_Lasso.joblib",
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"lr_tpv":"model/TPV_LinearRegression.joblib",
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"rfr_tpv":"model/TPV_RFR.joblib",
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"ridge_tpv":"model/TPV_Ridge.joblib",
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"svr_tpv":"model/TPV_SVR.joblib",
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"xgb_tpv":"model/TPV_XGB.joblib"
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}
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model_list_ssa = ['xgb_ssa','lr_ssa','ridge_ssa','gp_ssa','en_ssa','kn_ssa','svr_ssa','dtr_ssa','rfr_ssa','adb_ssa']
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model_list_tpv = ['xgb_tpv','lr_tpv','ridge_tpv','gp_tpv','en_tpv','kn_tpv','svr_tpv','dtr_tpv','rfr_tpv','adb_tpv']
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# xgb, lr, ridge,gp,en,kn,svr,dtr,rfr, adb
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ssa_mae = [308,407,407,282,411,389,322,288,192,329]
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ssa_r2 = [0.92,0.82,0.82,0.89,0.81,0.82,0.87,0.88,0.95,0.88]
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# xgb, lr, ridge,gp,en,kn,svr，gdbt,,rfr, adb
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tpv_mae = [0.2,0.2,0.2,0.2,0.2,0.3,0.23,0.23,0.16,0.21]
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tpv_r2 = [0.81,0.81,0.81,0.8,0.82,0.72,0.78,0.8,0.85,0.84]
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test_content = {
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'A': 21.01,
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'VM' : 8.17,
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'K/C': 4,
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'MM': 0,
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'AT': 700,
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'At': 2,
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'Rt': 5
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}
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test_content = pd.DataFrame([test_content])
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current_script_directory = os.path.dirname(os.path.abspath(__file__))
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def pred_single_ssa(test_content):
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ssa_pred = []
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for name in model_list_ssa:
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model_path = os.path.join(current_script_directory,model_dict[name])
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ssa_model = load(model_path)
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pred = ssa_model.predict(test_content)
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ssa_pred.append(pred[0])
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return ssa_pred
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def pred_single_tpv(test_content):
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tpv_pred = []
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for name in model_list_tpv:
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model_path = os.path.join(current_script_directory,model_dict[name])
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tpv_model = load(model_path)
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pred = tpv_model.predict(test_content)
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tpv_pred.append(pred[0])
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return tpv_pred
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def choose_model(name,data):
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model_path = os.path.join(current_script_directory,model_dict[name])
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model = load(model_path)
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pred = model.predict(data)
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return pred
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def get_excel():
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current_script_directory = os.path.dirname(os.path.abspath(__file__))
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data_path = os.path.join(current_script_directory,'data/TPV.xlsx')
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test_data = pd.read_excel(data_path,sheet_name='sheet2')
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test_data = test_data.drop('TPV', axis=1)
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print(test_data.shape)
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pred = choose_model('xgb_tpv',test_data)
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test_data['TPV'] = pred
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print(test_data)
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if __name__ == "__main__":
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# ssa_result = pred_single_ssa(test_content)
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# result = [ssa_mae, ssa_r2,ssa_result]
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# print(result)
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# print(pred_single_tpv(test_content))
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get_excel()
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