Update app/modelling.py

app/modelling.py

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