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import os
import numpy as np
import pandas as pd
from sklearn.metrics import accuracy_score
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import PowerTransformer
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import f1_score
import argparse
import joblib
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"]
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)
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)
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":f"{accuracy_score(y_test,predict):4f}",
"F1_Score":f"{f1_score(y_test,predict):4f}"}
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