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update object to use a relative path root #9

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update object to use a relative path root #9

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23 changes: 11 additions & 12 deletions XGB_Model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -63,7 +63,7 @@ def fit(self,param, X,y, M_save_filepath):
print('Calibration time', round(c_time), 's')
print('Saving Model')
#adjust this to match changing models
pickle.dump(model, open(self.cwd + M_save_filepath, "wb"))
pickle.dump(model, open(self.cwd.joinpath(M_save_filepath), "wb"))

self.model_=model

Expand All @@ -78,9 +78,8 @@ def predict(self,X, model):


def XGB_Predict(self, test_feat, test_targ):

#Make predictions with the model
model = pickle.load(open(self.cwd+"/Model_History/V2/XGBoost_"+self.Target+".dat", "rb"))
model = pickle.load(open(self.cwd.joinpath("/Model_History/V2/XGBoost_"+self.Target+".dat"), "rb"))
start_time = time.time()
#since the previous timestep is being used, we need to predict this value
predict = []
Expand Down Expand Up @@ -439,7 +438,7 @@ def Feature_Optimization(self):
self.Final_FeaturesDF = self.Col_Check_feat[self.Final_Features]

#save features list
pickle.dump(self.Final_Features, open(self.cwd + "/Model_History/V2/"+self.targ+"_features.pkl", "wb"))
pickle.dump(self.Final_Features, open(self.cwd.joinpath("/Model_History/V2/"+self.targ+"_features.pkl"), "wb"))

print('The final features for ', self.targ, 'are: ')
print(self.Final_FeaturesDF.columns)
Expand Down Expand Up @@ -530,10 +529,10 @@ def ProcessData(self, Sim, scenario, test_yr):

print('Processing data into features/targets for ', self.scenario, ' scenario')
#Input optimial features from XGBoost_WSM_Tuning.
LittleDell_Percent_Full = pickle.load(open("Models/V2/LittleDell_Percent_Full_features.pkl", "rb"))
Mtn_Dell_Percent_Full = pickle.load(open("Models/V2/Mtn_Dell_Percent_Full_features.pkl", "rb"))
SLCDPU_GW = pickle.load(open("Models/V2/SLCDPU_GW_features.pkl", "rb"))
SLCDPU_DC_Water_Use = pickle.load(open("Models/V2/SLCDPU_DC_Water_Use_features.pkl", "rb"))
LittleDell_Percent_Full = pickle.load(open(self.cwd.joinpath("BoostModels/LittleDell_Percent_Full_features.pkl"), "rb"))
Mtn_Dell_Percent_Full = pickle.load(open(self.cwd.joinpath("BoostModels/Mtn_Dell_Percent_Full_features.pkl"), "rb"))
SLCDPU_GW = pickle.load(open(self.cwd.joinpath("BoostModels/SLCDPU_GW_features.pkl"), "rb"))
SLCDPU_DC_Water_Use = pickle.load(open(self.cwd.joinpath("BoostModels/SLCDPU_DC_Water_Use_features.pkl"), "rb"))



Expand Down Expand Up @@ -647,10 +646,10 @@ def WSM_Predict(self):
DC_targ = copy.deepcopy(self.targets[self.DC])

#Make predictions with the model, load model from XGBoost_WSM_Tuning
MDell_model = pickle.load(open("Models/V1/XGBoost_"+self.MDell+".dat", "rb"))
LDell_model = pickle.load(open("Models/V2/XGBoost_"+self.LDell+".dat", "rb"))
GW_model = pickle.load(open("Models/V2/XGBoost_"+self.GW+".dat", "rb"))
DC_model = pickle.load(open("Models/V2/XGBoost_"+self.DC+".dat", "rb"))
MDell_model = pickle.load(open(self.cwd.joinpath("BoostModels/XGBoost_"+self.MDell+".dat"), "rb"))
LDell_model = pickle.load(open(self.cwd.joinpath("BoostModels/XGBoost_"+self.LDell+".dat"), "rb"))
GW_model = pickle.load(open(self.cwd.joinpath("BoostModels/XGBoost_"+self.GW+".dat"), "rb"))
DC_model = pickle.load(open(self.cwd.joinpath("BoostModels/XGBoost_"+self.DC+".dat"), "rb"))



Expand Down
28 changes: 22 additions & 6 deletions XGBoost_WSM_Tuning.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -8,13 +8,30 @@
"outputs": [],
"source": [
"#my python module\n",
"import XGB_Model\n",
"!pip install collinearity jenkspy \n",
"from pathlib import Path\n",
"import pandas as pd\n",
"import os\n",
"%matplotlib inline\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"import sys\n",
"import subprocess\n",
"\n",
"try:\n",
" import google.colab\n",
"\n",
" ENV_IS_CL = True\n",
" cwd = r\"/content/ml_WSM\"\n",
" sys.path.insert(0, cwd)\n",
" subprocess.run([\"git\", \"clone\", \"https://github.com/whitelightning450/Machine-Learning-Water-Systems-Model\", \"ml_WSM\"])\n",
"except:\n",
" cwd = os.getcwd()\n",
" \n",
"cwd = Path(cwd)\n",
"\n",
"import XGB_Model"
]
},
{
Expand All @@ -26,11 +43,10 @@
"source": [
"\n",
"#This is the final dataset to make predictions on\n",
"p = Path('Training_Simulations')\n",
"p = cwd.joinpath('Training_Simulations')\n",
"\n",
"Sims = {i.stem[0:8] : pd.read_excel(i, skiprows = 5) for i in p.glob('**/*.xlsx')}\n",
"\n",
"cwd = os.getcwd()"
"\n"
]
},
{
Expand Down Expand Up @@ -97,7 +113,7 @@
"DataProcc = XGB_Model.XGB_Tuning(cwd)\n",
"\n",
"#Load in data and process accordingly\n",
"DataProcc.ProcessData(Sims, 'Obs_Ave', feat, targ, 2021, False, allData = True)\n",
"DataProcc.ProcessData(Sims, 'Obs_Ave', feat, targ, 2021, False, allData = True, cwd=cwd)\n",
"\n",
"'''\n",
"This step check to collinearity among features.\n",
Expand Down