Update app/modelling.py
Browse files- app/modelling.py +26 -35
app/modelling.py
CHANGED
@@ -10,49 +10,40 @@ from sklearn.metrics import f1_score
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import argparse
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import joblib
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def train(
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return {"Accuracy":accuracy_score(y_test,predict),
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"F1_Score":f1_score(y_test,predict)}
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if __name__=="__main__":
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parser=argparse.ArgumentParser()
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parser.add_argument("--dataset_pth",default="/home/sudhanshu/manufacturing_defect/Manufacturing_Downtime_Dataset.csv")
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args=parser.parse_args()
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results=train(args.dataset_pth)
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print(f"Accuracy: {results['Accuracy']}\n")
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print(f"F1_Score: {results['F1_Score']}")
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import argparse
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import joblib
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def train(dataset):
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df=dataset.copy()
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features=["Torque(Nm)","Hydraulic_Pressure(bar)","Cutting(kN)","Coolant_Pressure(bar)","Spindle_Speed(RPM)","Coolant_Temperature","Downtime"]
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df=df[features]
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df.dropna(inplace=True,ignore_index=True)
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X=df.drop("Downtime",axis=1)
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y=df["Downtime"]
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X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.20,random_state=42,stratify=y)
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transform=PowerTransformer()
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X_train=transform.fit_transform(X_train)
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X_test=transform.transform(X_test)
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encoder=LabelEncoder()
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y_train=encoder.fit_transform(y_train)
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y_test=encoder.transform(y_test)
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model=RandomForestClassifier(random_state=42)
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model.fit(X_train,y_train)
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predict=model.predict(X_test)
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cwd=os.getcwd()
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transform_pth=os.path.join(cwd,"app","transform.pkl")
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encoder_pth=os.path.join(cwd,"app","encoder.pkl")
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model_pth=os.path.join(cwd,"app","model.pkl")
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joblib.dump(transform,transform_pth)
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joblib.dump(encoder,encoder_pth)
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joblib.dump(model,model_pth)
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return {"Accuracy":f"{accuracy_score(y_test,predict):4f}",
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"F1_Score":f"{f1_score(y_test,predict):4f}"}
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