driverpospredictionfinal.py

# -*- coding: utf-8 -*-
"""DriverPosPredictionFinal

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1DGTfO4HEZDof1phuficJD_J8v38JnF3C
"""

from flask import Flask,jsonify
import json
app = Flask(__name__)


from sklearn import tree, linear_model
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.model_selection import train_test_split, GridSearchCV
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)

    # Convert 'LapTime' column to numeric (removing 'days' and 'seconds' part)
    df['LapTime'] = pd.to_timedelta(df['LapTime']).dt.total_seconds()

    # Encoding categorical columns (like 'Compound' and 'Compound2')
    label_encoder = LabelEncoder()
    df['Compound'] = label_encoder.fit_transform(df['Compound'])
    df['Compound2'] = label_encoder.fit_transform(df['Compound2'])

    # Drop non-predictive columns and set target variable
    X = df.drop(['Race', 'Driver', 'Position', 'Rainfall', 'RainFall2'], axis=1)
    y = df['Position']
    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

#@app.route('/predict',methods=['POST']) # api endpoint -any url(requests) with /predict will route to the method 
def prediction(req):
  data = json.loads(req)
  input_data = {}

  input_data['LapNumber'] = float(input("Enter the number of total laps in race: "))
  input_data['LapTime'] = float(input("Avg lap time from prev year (in seconds): "))
  input_data['TyreLife'] = float(input("Enter tyre life (in laps): "))
  input_data['Speedl1'] = float(input("Enter Speedl1 (in km/h): "))
  input_data['Speedl2'] = float(input("Enter Speedl2 (in km/h): "))
  input_data['SpeedFL'] = float(input("Enter SpeedFL (in km/h): "))
  input_data['SpeedST'] = float(input("Enter SpeedST (in km/h): "))
  input_data['AirTemp'] = float(input("Enter air temperature in Celsius: "))
  input_data['TrackTemp'] = float(input("Enter track temperature in Celsius: "))
  input_data['Humidity'] = float(input("Enter humidity percentage (0-100): "))
  input_data['WindSpeed'] = float(input("Enter wind speed in km/h: "))
  input_data['Compound'] = float(input("Enter compound (numeric): "))
  input_data['Compound2'] = float(input("Enter compound2 (numeric): "))
  return jsonify({'message':'Hello world'})


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



X, y = read_data("b.csv")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)

# Train and evaluate models
# model_1 = linear_reg(X_train, y_train)
# y_pred_1 = predict(model_1, X_test, y_test)

#model_2 = decision_tree_classifier_method(X_train, y_train)
parameter_grid = hyperparameter_tuning_paramter_grid()
# tuned_model_2 = hyper_paramter_tuning(model_2, X_train, y_train, **parameter_grid)
# y_pred_2 = predict(tuned_model_2, X_test, y_test)

model_3 = decision_tree_regressor_method()
tuned_model_3 = hyper_paramter_tuning(model_3, X_train, y_train, **parameter_grid)
y_pred_3 = predict(tuned_model_3, X_test, y_test)

# Ask user for input and predict position
predicted_position = ask_user(tuned_model_3, X_test)

# Save model
with open('tuned_model.pkl', 'wb') as file:
    pickle.dump(tuned_model_3, file)