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Update risk_model.py
Browse files- risk_model.py +10 -2
risk_model.py
CHANGED
@@ -2,11 +2,17 @@ import os
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import pandas as pd
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from sklearn.ensemble import RandomForestClassifier
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from joblib import dump, load
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MODEL_PATH = "heating_model.pkl"
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DATA_PATH = "mantle_training.csv"
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HISTORY = []
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def train_and_save_model():
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data = pd.read_csv(DATA_PATH)
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X = data[["temperature", "duration"]]
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@@ -27,13 +33,15 @@ def predict_risk(temp, duration):
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global model
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pred = model.predict([[temp, duration]])[0]
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score = max(model.predict_proba([[temp, duration]])[0]) * 100
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HISTORY.append({
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"Temperature": temp,
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"Duration": duration,
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"Risk": pred,
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"Confidence": round(score, 2)
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})
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return pred, round(score, 2)
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def retrain_model():
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try:
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import pandas as pd
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from sklearn.ensemble import RandomForestClassifier
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from joblib import dump, load
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from datetime import datetime
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import pytz
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MODEL_PATH = "heating_model.pkl"
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DATA_PATH = "mantle_training.csv"
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HISTORY = []
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def get_ist_time():
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ist = pytz.timezone('Asia/Kolkata')
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return datetime.now(ist).strftime("%Y-%m-%d %H:%M:%S %Z")
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def train_and_save_model():
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data = pd.read_csv(DATA_PATH)
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X = data[["temperature", "duration"]]
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global model
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pred = model.predict([[temp, duration]])[0]
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score = max(model.predict_proba([[temp, duration]])[0]) * 100
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timestamp = get_ist_time()
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HISTORY.append({
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"Temperature": temp,
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"Duration": duration,
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"Risk": pred,
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"Confidence": round(score, 2),
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"Timestamp": timestamp
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})
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return pred, round(score, 2), timestamp
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def retrain_model():
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try:
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