Upload driverpospredictionfinal.py

driverpospredictionfinal.py

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+# -*- coding: utf-8 -*-
+"""DriverPosPredictionFinal
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1DGTfO4HEZDof1phuficJD_J8v38JnF3C
+"""
+
+
+
+
+
+
+
+from sklearn import tree, linear_model
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.model_selection import train_test_split, GridSearchCV
+!pip install bayesian-optimization
+from bayes_opt import BayesianOptimization
+from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score, mean_squared_log_error, median_absolute_error
+import pandas as pd
+import joblib
+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+import xgboost as xgb
+import pickle
+
+def read_data(file_name):
+  df = pd.read_csv(file_name)
+  x= df.drop(['RaceID', "Pos", "formating" ], axis=1)
+  y =df["Pos"]
+  return x,y
+
+#returns the object for model being used
+def decision_tree_regressor_method():
+  dtree = DecisionTreeRegressor(random_state=42)
+  return dtree
+def decsison_tree_classifier_method(X_train,y_train):
+  dtree = DecisionTreeClassifier()
+  dtree = dtree.fit(X_train,y_train)
+  return dtree
+def linear_reg(X_train,y_train):
+  regr= linear_model.LinearRegression()
+  regr.fit(X_train,y_train)
+  return regr
+def hyper_paramter_tuning(model, xtrain,ytrain,**kwargs): #doesnt work for linear regression
+  #dtree = DecisionTreeRegressor(random_state=42)
+#  grid_search_object = GridSearchCV(estimator=model, param_grid=kwargs, cv=5, n_jobs=-1, verbose=2, scoring='neg_mean_squared_error')
+  grid_search_object = GridSearchCV(estimator=model, param_grid=kwargs, cv=3, n_jobs=-1, verbose=2, scoring='neg_mean_squared_error')
+  grid_search_object.fit(xtrain,ytrain)
+  best_estimator = grid_search_object.best_estimator_
+  return best_estimator
+
+
+def predict(model, xtest,ytest):
+    # Predict the target for the test set
+    y_pred = model.predict(xtest)
+    # Calculate various evaluation metrics
+    mse = mean_squared_error(ytest, y_pred)
+    rmse = mse ** 0.5
+    mae = mean_absolute_error(ytest, y_pred)
+    r2 = r2_score(ytest, y_pred)
+    msle = mean_squared_log_error(ytest, y_pred)
+    medae = median_absolute_error(ytest, y_pred)
+    # Print the metrics
+
+    print(f"Test MSE: {mse}") # smaller is better - mean squared error
+    print(f"Test RMSE: {rmse}")#
+    print(f"Test MAE: {mae}")
+    print(f"Test R²: {r2}")
+    print(f"Test MSLE: {msle}")
+    print(f"Test Median Absolute Error: {medae}")
+    #rmse measures abg magnitude of errors betwen actual and predicted values
+    return y_pred
+
+
+
+def hyperamter_tuning_paramter_grid():
+  parameter_grid = {'max_depth': [1,2,3,4,5,None],
+     "min_samples_split":[2,3,5,6] ,
+     'min_samples_leaf': [1,2,4,5],
+     "min_weight_fraction_leaf": [0.0,0.01, 0.05,0.1, 0.2],# add more hypermater tuning criteria
+    }#'criterion': ['gini', 'entropy']
+  return parameter_grid
+
+
+
+
+def ask_user(model, X_test=None):
+    """
+    Collect user input and format it for prediction.
+    Returns a DataFrame with a single row formatted like the training data.
+    """
+    try:
+        # Create a dictionary to store inputs
+        input_data = {}
+
+        # Collect basic race information
+        input_data['LapNumber'] = float(input("Enter the number of total laps in race: "))
+        input_data['LapTimes'] = float(input("Avg lap time from prev year (or estimate 98): "))
+        input_data['PitStopTimes'] = float(input("Enter pit stop time (0 if no pit stop): "))
+        input_data['PrevLap'] = float(input("Enter avg lap time differece (0-2) (0 if first lap): "))
+
+        # Collect race conditions
+        input_data['AvgSpeed'] = float(input("Enter average speed in km/h from prev year: "))
+        input_data['AirTemp_Cel'] = float(input("estimate air temperature in Celsius on race day: "))
+        input_data['TrackTemp_Cel'] = float(input("estimate track temperature in Celsius on race day: "))
+        input_data['Humidity'] = float(input("estimate the humidity percentage (0-100) on race day: "))
+        input_data['WindSpeed_km/h'] = float(input("estimate wind speed in km/h: "))
+
+        # Create a DataFrame with the input data
+        input_df = pd.DataFrame([input_data])
+
+        # If we have a test set, ensure our columns match the training data
+        if X_test is not None:
+            missing_cols = set(X_test.columns) - set(input_df.columns)
+            # Add any missing columns with 0s
+            for col in missing_cols:
+                input_df[col] = 0
+            # Ensure column order matches training data
+            input_df = input_df[X_test.columns]
+
+        # Make prediction
+        prediction = model.predict(input_df)
+        print(f"\nPredicted position: {int(round(prediction[0]))}")
+
+        return prediction[0]
+
+    except ValueError as e:
+        print(f"Error: Please enter valid numeric values. Details: {e}")
+        return None
+    except Exception as e:
+        print(f"An error occurred: {e}")
+        return None
+
+
+
+
+
+#-----linear regresssion------------
+x , y= read_data("b.csv")
+xtrain, xtest , ytrain, ytest = train_test_split(x,y,test_size = 0.3)
+
+model_1 = linear_reg(xtrain,ytrain)
+
+ypred = predict(model_1,xtest,ytest)
+#visvualize(ytest,ypred)
+
+#-----descision tree classifier-----
+x , y= read_data("b.csv")
+xtrain, xtest , ytrain, ytest = train_test_split(x,y,test_size = 0.3)
+
+model_2 = decsison_tree_classifier_method(xtrain,ytrain)
+
+parameter_grid= hyperamter_tuning_paramter_grid()
+tuned_model_2 = hyper_paramter_tuning(model_2,xtrain,ytrain,**parameter_grid)
+y_pred = predict(tuned_model_2,xtest,ytest)
+
+#visvualize(ytest,ypred)
+
+#----descision tree regressor-------
+x , y= read_data("b.csv")
+xtrain, xtest , ytrain, ytest = train_test_split(x,y,test_size = 0.3)
+
+model_3 = decision_tree_regressor_method()
+
+parameter_grid= hyperamter_tuning_paramter_grid()
+tuned_model_3 = hyper_paramter_tuning(model_3,xtrain,ytrain,**parameter_grid)
+y_pred= predict(tuned_model_3,xtest,ytest)
+
+
+# Ask user for input and predict position
+#predicted_position = ask_user(tuned_model_3, x_columns)
+
+
+
+
+predicted_position = ask_user(tuned_model_3, xtest)
+
+#pickle.dump(tuned_model_3.open('model.pkl','mb'))
+
+!pip install streamlit
+import streamlit as st
+import pickle
+import numpy as np
+
+model = pickle.load(open('model.pkl','rb'))
+
+def predictPos(TLaps,Laptime,Pit,Prev,AvgSpeed,AirTemp,TrackTemp,Humid,Wind):
+  input = np.array([ [TLaps,Laptime,Pit,Prev,AvgSpeed,AirTemp,TrackTemp,Humid,Wind]]).astype(np.float64)
+  prediction=model.predict(input)
+  return prediction[0]
+
+def main():
+  st.title("title")
+  html_temp="""
+  <div style = "background-color:#025244; padding :10px">
+  <h2 style = "color:white; text-align:center;"> Driver Position Prediction </h2>
+  </div>
+  """
+  st.markdown(html_temp, unsafe_allow_html=True)
+  TLaps = st.text_input("Enter the number of total laps in race: ","Type Here")
+  Laptime = st.text_input("Avg lap time from prev year (or estimate 98): ","Type Here")
+  Pit = st.text_input("Enter pit stop time (0 if no pit stop): ","Type Here")
+  Prev= st.text_input("Enter avg lap time differece (0-2) (0 if first lap): ","Type Here")
+
+
+  AvgSpeed = st.text_input("Enter average speed in km/h from prev year: ","Type Here")
+  AirTemp= st.text_input("estimate air temperature in Celsius on race day: ","Type Here")
+  TrackTemp = st.text_input("estimate track temperature in Celsius on race day: ","Type Here")
+  Humid = st.text_input("estimate the humidity percentage (0-100) on race day: ","Type Here")
+  Wind = st.text_input("estimate wind speed in km/h: ","Type Here")
+  safe_html="""
+    <div style="background-color:#F4D03F;padding:10px >
+    <h2 style="color:white;text-align:center;"> Your forest is safe</h2>
+    </div>
+    """
+  danger_html="""
+  <div style="background-color:#F08080;padding:10px >
+    <h2 style="color:black ;text-align:center;"> Your forest is in danger</h2>
+   </div>
+    """
+
+  if st.button("Predict"):
+    output=predictPos(TLaps,Laptime,Pit,Prev,AvgSpeed,AirTemp,TrackTemp,Humid,Wind)
+    st.success('The postion of driver is {}'.format(output))
+
+    # if output > 0.5:
+    #   st.markdown(danger_html,unsafe_allow_html=True)
+    # else:
+    #   st.markdown(safe_html,unsafe_allow_html=True)
+
+if __name__=='__main__':
+    main()
+
+
+