In [15]:
utils.plot_dataset_description_with_target_distribution(cleaned_train_data,"LAP_TIME",
title="Cleaned train data")
In the heat of a Formula E race, teams need fast access to insights that can help drivers make split-second decisions and cross the finish line first. Can your data-science skills help Envision Racing, one of the founding teams in the championship, take home even more trophies?
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Genpact (NYSE: G) is a global professional services firm that makes business transformation real, driving digital-led innovation and digitally enabled intelligent operations for our clients.
| Feature | Description Provided |
|---|---|
| NUMBER | Number in sequence |
| DRIVER_NUMBER | Driver number |
| LAP_NUMBER | Lap number |
| LAP_TIME | Lap time in seconds |
| LAP_IMPROVEMENT | Number of Lap Improvement |
| CROSSING_FINISH_LINE_IN_PIT | Time |
| S1 | Sector 1 in [min sec.microseconds] |
| S1_IMPROVEMENT | Improvement in sector 1 |
| S2 | Sector 2 in [min sec.microseconds] |
| S2_IMPROVEMENT | Improvement in sector 2 |
| S3 | Sector 3 in [min sec.microseconds] |
| S3_IMPROVEMENT | Improvement in sector 3 |
| KPH | Speed in kilometer/hour |
| ELAPSED | Time elapsed in [min sec.microseconds] |
| HOUR | In [min sec.microseconds] |
| S1_LARGE | In [min sec.microseconds] |
| S2_LARGE | In [min sec.microseconds] |
| S3_LARGE | In [min sec.microseconds] |
| DRIVER_NAME | Name of the driver |
| PIT_TIME | Time taken to car stops in the pits for fuel and other consumables to be renewed or replenished |
| GROUP | Group of driver |
| TEAM | Team name |
| POWER | Brake Horsepower(bhp) |
| LOCATION | Location of the event |
| EVENT | Free practice or qualifying |
The submission will be evaluated using the RMSLE metric.\ One can use numpy.sqrt(mean_squared_log_error(actual, predicted)) to calculate the same
Downloading ML Helper :
# Downloading a package from github : https://github.com/karthikchiru12/ML_Helper
! git clone https://github.com/karthikchiru12/ML_Helper.git
Cloning into 'ML_Helper'... remote: Enumerating objects: 42, done. remote: Counting objects: 100% (42/42), done. remote: Compressing objects: 100% (32/32), done. remote: Total 42 (delta 14), reused 34 (delta 9), pack-reused 0 Unpacking objects: 100% (42/42), 7.99 KiB | 194.00 KiB/s, done.
Importing packages :
# Ignoring warnings
import warnings
warnings.filterwarnings('ignore')
import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn import tree
from sklearn.metrics import mean_squared_log_error
from sklearn.model_selection import GridSearchCV
import xgboost as xgb
import tensorflow as tf
from tensorflow.keras.layers import Dense, Flatten, Dropout, Activation
from tensorflow.keras import Model,Sequential
from tensorflow.keras import backend as K
from pycaret.regression import *
from ML_Helper.Analysis.eda import EDA
from ML_Helper.Encoding.featurize import Featurizer
from Spaghetti import utils
# Run this cell to view code for EDA function
?? EDA
# Run this cell to view code for Featurizer function
?? Featurizer
# Run this cell to view code for utils functions inside the folder
?? utils
Files under Data folder :
os.listdir('Data/')
['test.csv', 'train.csv', 'train_weather.csv', 'test_weather.csv', 'Data_DIR_2021.zip', 'submission.csv']
Importing each file :
if not os.path.isfile('Cleaned_data/cleaned_train.csv'):
train_data = pd.read_csv('Data/train.csv')
else:
cleaned_train_data = pd.read_csv('Cleaned_data/cleaned_train.csv')
if not os.path.isfile('Cleaned_data/cleaned_test.csv'):
test_data = pd.read_csv('Data/test.csv')
#test_data['LAP_TIME'] = 0
else:
cleaned_test_data = pd.read_csv('Cleaned_data/cleaned_test.csv')
if not os.path.isfile('Cleaned_data/cleaned_train_weather.csv'):
train_weather_data = pd.read_csv('Data/train_weather.csv')
else:
cleaned_train_weather_data = pd.read_csv('Cleaned_data/cleaned_train_weather.csv')
if not os.path.isfile('Cleaned_data/cleaned_test_weather.csv'):
test_weather_data = pd.read_csv('Data/test_weather.csv')
else:
cleaned_test_weather_data = pd.read_csv('Cleaned_data/cleaned_test_weather.csv')
sample_submission = pd.read_csv('Data/submission.csv')
Spending that 70% of time cleaning this data :
if not os.path.isfile('Cleaned_data/'):
path = os.getcwd()
# Creating a new directory 'data'
os.mkdir(path+'/Cleaned_data')
print("train")
utils.clean_data(train_data,'cleaned_train.csv')
print("test")
utils.clean_data(test_data,'cleaned_test.csv')
print("train_weather")
utils.clean_weather_data(train_weather_data,'cleaned_train_weather.csv')
print("test_weather")
utils.clean_weather_data(test_weather_data,'cleaned_test_weather.csv')
train CROSSING_FINISH_LINE_IN_PIT : FIXED S1 : FIXED S2 : FIXED S3 : FIXED KPH : FIXED ELAPSED : FIXED HOUR : FIXED S1 Large : FIXED S2 Large : FIXED S3 Large : FIXED PIT_TIME : FIXED GROUP : FIXED POWER : FIXED DONE : If the code works, dont touch it. test CROSSING_FINISH_LINE_IN_PIT : FIXED S1 : FIXED S2 : FIXED S3 : FIXED KPH : FIXED ELAPSED : FIXED HOUR : FIXED S1 Large : FIXED S2 Large : FIXED S3 Large : FIXED PIT_TIME : FIXED GROUP : FIXED POWER : FIXED DONE : If the code works, dont touch it. train_weather DONE : If the code works, dont touch it. test_weather DONE : If the code works, dont touch it.
# To startover the data cleaning, delete the Cleaned_data directory and rerun above cells
!rm -r Cleaned_data
os.listdir('Cleaned_data/')
['cleaned_train_weather.csv', 'cleaned_train.csv', 'cleaned_test.csv', 'cleaned_test_weather.csv']
cleaned_train_data.info(verbose=False)
<class 'pandas.core.frame.DataFrame'> RangeIndex: 10276 entries, 0 to 10275 Columns: 49 entries, NUMBER to PIT_TOTAL dtypes: float64(18), int64(17), object(14) memory usage: 3.8+ MB
utils.plot_dataset_description_with_target_distribution(cleaned_train_data,"LAP_TIME",
title="Cleaned train data")
utils.plot_missing_values_per_feature(cleaned_train_data,title="Cleaned train data")
cleaned_test_data.info(verbose=False)
<class 'pandas.core.frame.DataFrame'> RangeIndex: 420 entries, 0 to 419 Columns: 49 entries, NUMBER to PIT_TOTAL dtypes: float64(19), int64(16), object(14) memory usage: 160.9+ KB
utils.plot_dataset_description_with_target_distribution(cleaned_test_data,"NUMBER",
title="Cleaned test data")
utils.plot_missing_values_per_feature(cleaned_test_data,title="Cleaned test data")
cleaned_train_weather_data.info(verbose=False)
<class 'pandas.core.frame.DataFrame'> RangeIndex: 914 entries, 0 to 913 Columns: 11 entries, TIME_UTC_SECONDS to EVENT dtypes: float64(5), int64(3), object(3) memory usage: 78.7+ KB
utils.plot_dataset_description_with_target_distribution(cleaned_train_weather_data
,"AIR_TEMP",title="Cleaned train weather data")
cleaned_test_weather_data.info(verbose=False)
<class 'pandas.core.frame.DataFrame'> RangeIndex: 167 entries, 0 to 166 Columns: 11 entries, TIME_UTC_SECONDS to EVENTS dtypes: int64(8), object(3) memory usage: 14.5+ KB
utils.plot_dataset_description_with_target_distribution(cleaned_test_weather_data
,"AIR_TEMP",title="Cleaned test weather data")
Initializng the object :
eda = EDA(cleaned_train_data)
Plain distributions of numerical features :
eda.kde_plot(list(cleaned_train_data.describe().columns),width=8,height=30,
title="Dsitributions of train data numerical features")
Distributions of numerical features by event :
eda.kde_plot(list(cleaned_train_data.describe().columns),width=18,height=40,hue="EVENT",
title="Dsitributions of train data numerical features by event")
eda.count_plot(['LOCATION','EVENT',' CROSSING_FINISH_LINE_IN_PIT'],width=8,height=8)
plt.figure(figsize=(24,4))
sns.countplot(cleaned_train_data['TEAM'])
<AxesSubplot:xlabel='TEAM', ylabel='count'>
plt.figure(figsize=(16,4))
sns.countplot(cleaned_train_data['DRIVER_NAME'])
<AxesSubplot:xlabel='DRIVER_NAME', ylabel='count'>
eda.correlation_plot(list(cleaned_train_data.describe().columns),width=30,
title="Correlation martrix of train data")
Initializng the object :
eda = EDA(cleaned_train_weather_data)
Plain distributions of numerical features :
eda.kde_plot(list(cleaned_test_weather_data.describe().columns),width=10,height=10,
title="Dsitributions of train weather data numerical features")
Distributions of numerical features by event :
eda.kde_plot(list(cleaned_test_weather_data.describe().columns),width=20,height=20,hue="EVENT",
title="Dsitributions of train weather data numerical features by event")
eda.count_plot(['LOCATION','EVENT'],width=8,height=4)
eda.correlation_plot(list(cleaned_train_weather_data.describe().columns),width=8,height=6,
title="Correlation martrix of train weather data")
distance = velocity * time
cleaned_train_data['Distance'] = cleaned_train_data['S1_TOTAL'] + cleaned_train_data['S2_TOTAL'] + \
cleaned_train_data['S3_TOTAL']
cleaned_train_data['Distance'] = cleaned_train_data['Distance'] * (cleaned_train_data[' KPH']*5/18)
cleaned_test_data['Distance'] = cleaned_test_data['S1_TOTAL'] + cleaned_test_data['S2_TOTAL'] + \
cleaned_test_data['S3_TOTAL']
cleaned_test_data['Distance'] = cleaned_test_data['Distance'] * (cleaned_test_data[' KPH']*5/18)
dt1 = DecisionTreeRegressor()
dt1.fit(cleaned_train_data[[' DRIVER_NUMBER', ' LAP_NUMBER',
' LAP_IMPROVEMENT', ' S1_IMPROVEMENT', ' S2_IMPROVEMENT',
' S3_IMPROVEMENT', ' KPH', 'GROUP', 'POWER', 'S1 minutes', 'S1 seconds',
'S1_TOTAL', 'S2 minutes', 'S2 seconds', 'S2_TOTAL', 'S3 minutes',
'S3 seconds', 'S3_TOTAL', 'ELAPSED minutes', 'ELAPSED seconds',
'ELAPSED_TOTAL', 'minutes', 'seconds', 'TIME_TOTAL', 'S1 Large minutes',
'S1 Large seconds', 'S2 Large minutes', 'S2 Large seconds',
'S3 Large minutes', 'S3 Large seconds', 'PIT_TIME minutes',
'PIT_TIME seconds', 'PIT_TOTAL','Distance']]
,cleaned_train_data['LAP_TIME'])
DecisionTreeRegressor()
plt.figure(figsize=(10,10))
sns.barplot(y = dt1.feature_names_in_, x = dt1.feature_importances_)
<AxesSubplot:>
with open('pp.txt','w') as file:
file.write(tree.export_text(dt1,feature_names=list(dt1.feature_names_in_)))
based on event (median) :
cleaned_train_weather_data.groupby(['EVENT']).median().reset_index().drop(['TIME_UTC_SECONDS','RAIN'],axis=1)
| EVENT | AIR_TEMP | TRACK_TEMP | HUMIDITY | PRESSURE | WIND_SPEED | WIND_DIRECTION | |
|---|---|---|---|---|---|---|---|
| 0 | Free Practice 1 | 13.0000 | 18.0 | 72.0 | 1011.000 | 2.00000 | 84.0 |
| 1 | Free Practice 2 | 15.0556 | 20.9 | 76.0 | 1007.000 | 3.18280 | 160.0 |
| 2 | Free Practice 3 | 12.6667 | 18.2 | 74.5 | 965.104 | 1.06093 | 293.0 |
| 3 | Qualifying Group 1 | 15.1667 | 25.0 | 76.0 | 1006.000 | 2.12187 | 209.0 |
| 4 | Qualifying Group 2 | 14.4722 | 27.4 | 76.0 | 1012.000 | 2.12187 | 191.5 |
| 5 | Qualifying Group 3 | 18.0000 | 19.0 | 76.0 | 1008.000 | 3.18280 | 163.0 |
| 6 | Qualifying Group 4 | 18.0000 | 26.1 | 72.0 | 1008.000 | 2.12187 | 218.0 |
train_weather_data_median = \
cleaned_train_weather_data.groupby(['EVENT']).median().reset_index().drop(['TIME_UTC_SECONDS','RAIN'],axis=1)
print("Before Merging :\n")
print("train data :", cleaned_train_data.shape)
print("train weather data :", train_weather_data_median.shape)
Before Merging : train data : (10276, 50) train weather data : (7, 7)
train_data_combined = pd.merge(cleaned_train_data,train_weather_data_median,on=['EVENT'])
print("After Merging :\n")
print("train data :", train_data_combined.shape)
After Merging : train data : (10276, 56)
train_data_combined = train_data_combined.drop(['NUMBER',' S1',' S2',' S3','S1 minutes', 'S1 seconds',
'S2 minutes', 'S2 seconds','S3 minutes', 'S3 seconds',
'ELAPSED minutes', 'ELAPSED seconds','minutes','seconds',
' S1_IMPROVEMENT',' S2_IMPROVEMENT',' S3_IMPROVEMENT',
' ELAPSED',' HOUR','S1_LARGE','S2_LARGE','S3_LARGE',
'S1 Large minutes', 'S1 Large seconds','S2 Large minutes',
'S2 Large seconds', 'S3 Large minutes','S3 Large seconds',
'PIT_TIME minutes', 'PIT_TIME seconds',
'PIT_TIME','LOCATION'],axis=1)
train_data_combined.shape
(10276, 24)
x = train_data_combined.drop(['LAP_TIME'],axis=1)
y = train_data_combined['LAP_TIME']
x_train, x_cv, y_train, y_cv = train_test_split(x, y, test_size=0.2, random_state=42)
print("Item : Shape")
print("########################")
print("x_train : {0}".format(x_train.shape))
print(".........................")
print("y_train : {0}".format(y_train.shape))
print(".........................")
print("x_cv : {0}".format(x_cv.shape))
print(".........................")
print("y_cv : {0}".format(y_cv.shape))
Item : Shape ######################## x_train : (8220, 23) ......................... y_train : (8220,) ......................... x_cv : (2056, 23) ......................... y_cv : (2056,)
cleaned_test_data['EVENT'].value_counts()
Qualifying Group 2 113 Qualifying Group 3 110 Qualifying Group 4 99 Qualifying Group 1 98 Name: EVENT, dtype: int64
cleaned_test_weather_data['EVENTS'].value_counts()
Free Practice 1 75 Free Practice 2 60 Qualifying Group 1 8 Qualifying Group 2 8 Qualifying Group 4 8 Qualifying Group 3 8 Name: EVENTS, dtype: int64
Free Practice events in test data is not available
test_weather_data_median = cleaned_test_weather_data[(cleaned_test_weather_data['EVENTS']!='Free Practice 1') &
(cleaned_test_weather_data['EVENTS']!='Free Practice 2') &
(cleaned_test_weather_data['EVENTS']!='Free Practice 3')].groupby(['EVENTS']).median()\
.reset_index().drop(['TIME_UTC_SECONDS','RAIN'],axis=1)
test_weather_data_median = test_weather_data_median.rename(columns={'EVENTS':'EVENT'})
test_weather_data_median
| EVENT | AIR_TEMP | TRACK_TEMP | HUMIDITY | PRESSURE | WIND_SPEED | WIND_DIRECTION | |
|---|---|---|---|---|---|---|---|
| 0 | Qualifying Group 1 | 23.0 | 33.5 | 50.5 | 1013.0 | 4.0 | 202.0 |
| 1 | Qualifying Group 2 | 23.0 | 33.5 | 49.0 | 1013.0 | 6.0 | 210.5 |
| 2 | Qualifying Group 3 | 23.5 | 34.0 | 48.5 | 1013.0 | 5.5 | 214.5 |
| 3 | Qualifying Group 4 | 23.5 | 33.5 | 48.5 | 1013.0 | 8.0 | 205.5 |
Combining test normal and weather data
test_data_combined = pd.merge(cleaned_test_data,test_weather_data_median)
test_data_combined = test_data_combined.drop(['NUMBER',' S1',' S2',' S3','S1 minutes', 'S1 seconds',
'S2 minutes', 'S2 seconds','S3 minutes', 'S3 seconds',
'ELAPSED minutes', 'ELAPSED seconds','minutes','seconds',
' S1_IMPROVEMENT',' S2_IMPROVEMENT',' S3_IMPROVEMENT',
' ELAPSED',' HOUR','S1_LARGE','S2_LARGE','S3_LARGE',
'S1 Large minutes', 'S1 Large seconds','S2 Large minutes',
'S2 Large seconds', 'S3 Large minutes','S3 Large seconds',
'PIT_TIME minutes', 'PIT_TIME seconds',
'PIT_TIME','LOCATION','LAP_TIME'],axis=1)
test_data_combined.shape
(420, 23)
f1 = Featurizer(x_train = x_train, x_holdout = x_cv, x_test = test_data_combined)
#numerical =[' KPH','POWER','PRESSURE','HUMIDITY','AIR_TEMP','TRACK_TEMP','WIND_DIRECTION']
categorical = [' CROSSING_FINISH_LINE_IN_PIT','TEAM','EVENT','DRIVER_NAME']
x_train_final,x_test_final,x_cv_final = f1.featurize(numerical = [] , categorical = categorical)
WELL scipy.sparse.hstack() was not working, well still need a lot of work to do there.
So manually stacking them below, For this i commented out 122 line in the featurize.py
x_train_final = np.hstack(x_train_final)
x_test_final = np.hstack(x_test_final)
x_cv_final = np.hstack(x_cv_final)
print("Item : Shape")
print("########################")
print("x_train : {0}".format(x_train_final.shape))
print(".........................")
print("y_train : {0}".format(y_train.shape))
print(".........................")
print("x_cv : {0}".format(x_cv_final.shape))
print(".........................")
print("y_cv : {0}".format(y_cv.shape))
print(".........................")
print("x_test : {0}".format(x_test_final.shape))
print(".........................")
Item : Shape ######################## x_train : (8220, 72) ......................... y_train : (8220,) ......................... x_cv : (2056, 72) ......................... y_cv : (2056,) ......................... x_test : (420, 72) .........................
if not os.path.isfile('Final_data/'):
os.mkdir('Final_data')
temp = pd.DataFrame(x_train_final)
temp['target'] = y_train.values
temp.to_csv('Final_data/final_train.csv',index=False)
temp = pd.DataFrame(x_cv_final)
temp['target'] = y_cv.values
temp.to_csv('Final_data/final_cv.csv',index=False)
temp = pd.DataFrame(x_test_final)
temp.to_csv('Final_data/final_test.csv',index=False)
final_train = pd.read_csv('Final_data/final_train.csv')
final_cv = pd.read_csv('Final_data/final_cv.csv')
final_test = pd.read_csv('Final_data/final_test.csv')
def root_mean_squared_log_error(y_true, y_pred):
return np.sqrt(mean_squared_log_error(y_true,y_pred))
xgbr = xgb.XGBRegressor(n_jobs=-1,n_estimators=500,tree_method="gpu_hist",
predictor="gpu_predictor",seed=42)
evaluation = [( final_train.drop(['target'],axis=1), final_train['target']),
( final_cv.drop(['target'],axis=1), final_cv['target'])]
xgbr.fit(final_train.drop(['target'],axis=1), final_train['target'],
eval_set=evaluation, eval_metric="rmsle",
early_stopping_rounds=20)
#verbose=False)
[0] validation_0-rmsle:1.22375 validation_1-rmsle:1.23794 [1] validation_0-rmsle:0.79932 validation_1-rmsle:0.82410 [2] validation_0-rmsle:0.63997 validation_1-rmsle:0.66802 [3] validation_0-rmsle:0.57573 validation_1-rmsle:0.60850 [4] validation_0-rmsle:0.55016 validation_1-rmsle:0.57905 [5] validation_0-rmsle:0.54044 validation_1-rmsle:0.56971 [6] validation_0-rmsle:0.53725 validation_1-rmsle:0.56711 [7] validation_0-rmsle:0.53551 validation_1-rmsle:0.56669 [8] validation_0-rmsle:0.53573 validation_1-rmsle:0.56680 [9] validation_0-rmsle:0.53442 validation_1-rmsle:0.56787 [10] validation_0-rmsle:0.53502 validation_1-rmsle:0.56866 [11] validation_0-rmsle:0.53461 validation_1-rmsle:0.56922 [12] validation_0-rmsle:0.53466 validation_1-rmsle:0.56974 [13] validation_0-rmsle:0.53431 validation_1-rmsle:0.57018 [14] validation_0-rmsle:0.53408 validation_1-rmsle:0.57015 [15] validation_0-rmsle:0.53364 validation_1-rmsle:0.57057 [16] validation_0-rmsle:0.53218 validation_1-rmsle:0.57060 [17] validation_0-rmsle:0.53100 validation_1-rmsle:0.57042 [18] validation_0-rmsle:0.53052 validation_1-rmsle:0.57039 [19] validation_0-rmsle:0.52867 validation_1-rmsle:0.57044 [20] validation_0-rmsle:0.52824 validation_1-rmsle:0.57047 [21] validation_0-rmsle:0.52487 validation_1-rmsle:0.57106 [22] validation_0-rmsle:0.52432 validation_1-rmsle:0.57114 [23] validation_0-rmsle:0.52324 validation_1-rmsle:0.57096 [24] validation_0-rmsle:0.52164 validation_1-rmsle:0.57109 [25] validation_0-rmsle:0.52131 validation_1-rmsle:0.57107 [26] validation_0-rmsle:0.52047 validation_1-rmsle:0.57120
XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=0,
importance_type='gain', interaction_constraints='',
learning_rate=0.300000012, max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan, monotone_constraints='()',
n_estimators=500, n_jobs=-1, num_parallel_tree=1,
predictor='gpu_predictor', random_state=42, reg_alpha=0,
reg_lambda=1, scale_pos_weight=1, seed=42, subsample=1,
tree_method='gpu_hist', validate_parameters=1, verbosity=None)
root_mean_squared_log_error(final_train['target'],xgbr.predict(final_train.drop(['target'],axis=1)))
0.5355086715738926
root_mean_squared_log_error(final_cv['target'],xgbr.predict(final_cv.drop(['target'],axis=1)))
0.5666899785370824
xgb_submission = pd.DataFrame({"LAP_TIME" :abs(xgbr.predict(final_test))})
xgb_submission.to_csv("xgbr1.csv",index=False)
xgbr = xgb.XGBRegressor(n_jobs=-1,n_estimators=1000,tree_method="gpu_hist",
predictor="gpu_predictor",seed=42)
evaluation = [( final_train.drop(['target'],axis=1), np.log(final_train['target'] + 1).values.reshape(-1,1)),
( final_cv.drop(['target'],axis=1), np.log(final_cv['target'] + 1).values.reshape(-1,1))]
xgbr.fit(final_train.drop(['target'],axis=1), np.log(final_train['target'] + 1).values.reshape(-1,1),
eval_set=evaluation, eval_metric="rmsle",
early_stopping_rounds=30,)
#verbose=False)
[0] validation_0-rmsle:0.72347 validation_1-rmsle:0.72428 [1] validation_0-rmsle:0.46942 validation_1-rmsle:0.47083 [2] validation_0-rmsle:0.33576 validation_1-rmsle:0.33827 [3] validation_0-rmsle:0.26349 validation_1-rmsle:0.26781 [4] validation_0-rmsle:0.22443 validation_1-rmsle:0.23143 [5] validation_0-rmsle:0.20353 validation_1-rmsle:0.21364 [6] validation_0-rmsle:0.19229 validation_1-rmsle:0.20541 [7] validation_0-rmsle:0.18778 validation_1-rmsle:0.20175 [8] validation_0-rmsle:0.18499 validation_1-rmsle:0.20048 [9] validation_0-rmsle:0.18402 validation_1-rmsle:0.19994 [10] validation_0-rmsle:0.18283 validation_1-rmsle:0.19989 [11] validation_0-rmsle:0.18196 validation_1-rmsle:0.19991 [12] validation_0-rmsle:0.18096 validation_1-rmsle:0.20000 [13] validation_0-rmsle:0.17997 validation_1-rmsle:0.20006 [14] validation_0-rmsle:0.17799 validation_1-rmsle:0.19981 [15] validation_0-rmsle:0.17699 validation_1-rmsle:0.20030 [16] validation_0-rmsle:0.17596 validation_1-rmsle:0.20029 [17] validation_0-rmsle:0.17554 validation_1-rmsle:0.20036 [18] validation_0-rmsle:0.17513 validation_1-rmsle:0.20015 [19] validation_0-rmsle:0.17424 validation_1-rmsle:0.20048 [20] validation_0-rmsle:0.17025 validation_1-rmsle:0.20090 [21] validation_0-rmsle:0.16793 validation_1-rmsle:0.20120 [22] validation_0-rmsle:0.16642 validation_1-rmsle:0.20122 [23] validation_0-rmsle:0.16595 validation_1-rmsle:0.20122 [24] validation_0-rmsle:0.16456 validation_1-rmsle:0.20134 [25] validation_0-rmsle:0.16366 validation_1-rmsle:0.20136 [26] validation_0-rmsle:0.16291 validation_1-rmsle:0.20144 [27] validation_0-rmsle:0.16165 validation_1-rmsle:0.20147 [28] validation_0-rmsle:0.16006 validation_1-rmsle:0.20169 [29] validation_0-rmsle:0.15775 validation_1-rmsle:0.20209 [30] validation_0-rmsle:0.15726 validation_1-rmsle:0.20214 [31] validation_0-rmsle:0.15670 validation_1-rmsle:0.20209 [32] validation_0-rmsle:0.15635 validation_1-rmsle:0.20210 [33] validation_0-rmsle:0.15533 validation_1-rmsle:0.20208 [34] validation_0-rmsle:0.15455 validation_1-rmsle:0.20204 [35] validation_0-rmsle:0.15240 validation_1-rmsle:0.20210 [36] validation_0-rmsle:0.15130 validation_1-rmsle:0.20222 [37] validation_0-rmsle:0.15086 validation_1-rmsle:0.20221 [38] validation_0-rmsle:0.14955 validation_1-rmsle:0.20233 [39] validation_0-rmsle:0.14776 validation_1-rmsle:0.20262 [40] validation_0-rmsle:0.14581 validation_1-rmsle:0.20267 [41] validation_0-rmsle:0.14476 validation_1-rmsle:0.20269 [42] validation_0-rmsle:0.14415 validation_1-rmsle:0.20269 [43] validation_0-rmsle:0.14374 validation_1-rmsle:0.20272
XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,
colsample_bynode=1, colsample_bytree=1, gamma=0, gpu_id=0,
importance_type='gain', interaction_constraints='',
learning_rate=0.300000012, max_delta_step=0, max_depth=6,
min_child_weight=1, missing=nan, monotone_constraints='()',
n_estimators=1000, n_jobs=-1, num_parallel_tree=1,
predictor='gpu_predictor', random_state=42, reg_alpha=0,
reg_lambda=1, scale_pos_weight=1, seed=42, subsample=1,
tree_method='gpu_hist', validate_parameters=1, verbosity=None)
root_mean_squared_log_error(final_train['target'],
np.exp(xgbr.predict(final_train.drop(['target'],axis=1)))-1)
0.4741558736462166
root_mean_squared_log_error(final_cv['target'],xgbr.predict(final_cv.drop(['target'],axis=1)))
2.829691260574427
xgb_submission = pd.DataFrame({"LAP_TIME" :abs(xgbr.predict(final_test))})
xgb_submission.to_csv("xgbr2.csv",index=False)
# https://stackoverflow.com/questions/43855162/rmse-rmsle-loss-function-in-keras
def root_mean_squared_log_error_K(y_true, y_pred):
return K.sqrt(K.mean(K.square(K.log(1+y_pred) - K.log(1+y_true))))
tf.random.set_seed(42)
tf.keras.backend.clear_session()
model = Sequential()
model.add(Dense(128, activation='relu',input_dim = final_train.drop(['target'],axis=1).shape[1]))
model.add(Dropout(0.25))
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(32, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(16, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(8, activation='relu'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(1,))
model.summary()
Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 128) 9344 _________________________________________________________________ dropout (Dropout) (None, 128) 0 _________________________________________________________________ dense_1 (Dense) (None, 128) 16512 _________________________________________________________________ dropout_1 (Dropout) (None, 128) 0 _________________________________________________________________ dense_2 (Dense) (None, 64) 8256 _________________________________________________________________ dropout_2 (Dropout) (None, 64) 0 _________________________________________________________________ dense_3 (Dense) (None, 32) 2080 _________________________________________________________________ dropout_3 (Dropout) (None, 32) 0 _________________________________________________________________ dense_4 (Dense) (None, 16) 528 _________________________________________________________________ dropout_4 (Dropout) (None, 16) 0 _________________________________________________________________ dense_5 (Dense) (None, 8) 136 _________________________________________________________________ dropout_5 (Dropout) (None, 8) 0 _________________________________________________________________ flatten (Flatten) (None, 8) 0 _________________________________________________________________ dense_6 (Dense) (None, 1) 9 ================================================================= Total params: 36,865 Trainable params: 36,865 Non-trainable params: 0 _________________________________________________________________
model.compile(
loss = 'mae',
optimizer="adam",
metrics=['mse',root_mean_squared_log_error_K])
history = model.fit(
x = final_train.drop(['target'],axis=1),
y = final_train['target'], batch_size = 16,
validation_data = (final_cv.drop(['target'],axis=1),final_cv['target']),
epochs=10
)
Epoch 1/10 514/514 [==============================] - 4s 4ms/step - loss: 57.8673 - mse: 5636.3728 - root_mean_squared_log_error_K: nan - val_loss: 58.6863 - val_mse: 4047.5371 - val_root_mean_squared_log_error_K: 1.0379 Epoch 2/10 514/514 [==============================] - 2s 3ms/step - loss: 40.1773 - mse: 2507.8919 - root_mean_squared_log_error_K: nan - val_loss: 54.2365 - val_mse: 3531.8025 - val_root_mean_squared_log_error_K: 0.9321 Epoch 3/10 514/514 [==============================] - 2s 3ms/step - loss: 37.3923 - mse: 2239.1054 - root_mean_squared_log_error_K: nan - val_loss: 56.0876 - val_mse: 3742.2014 - val_root_mean_squared_log_error_K: 0.9729 Epoch 4/10 514/514 [==============================] - 2s 3ms/step - loss: 36.9055 - mse: 2196.7436 - root_mean_squared_log_error_K: 0.8392 - val_loss: 54.2637 - val_mse: 3533.6179 - val_root_mean_squared_log_error_K: 0.9309 Epoch 5/10 514/514 [==============================] - 2s 4ms/step - loss: 34.8617 - mse: 1956.7970 - root_mean_squared_log_error_K: 0.7268 - val_loss: 53.5332 - val_mse: 3452.5674 - val_root_mean_squared_log_error_K: 0.9159 Epoch 6/10 514/514 [==============================] - 2s 4ms/step - loss: 33.3721 - mse: 1811.4409 - root_mean_squared_log_error_K: 0.7035 - val_loss: 51.0884 - val_mse: 3192.3506 - val_root_mean_squared_log_error_K: 0.8665 Epoch 7/10 514/514 [==============================] - 2s 4ms/step - loss: 32.6102 - mse: 1758.3441 - root_mean_squared_log_error_K: nan - val_loss: 59.7137 - val_mse: 4162.9121 - val_root_mean_squared_log_error_K: 1.0571 Epoch 8/10 514/514 [==============================] - 2s 3ms/step - loss: 32.2366 - mse: 1718.8482 - root_mean_squared_log_error_K: 0.6644 - val_loss: 55.7472 - val_mse: 3698.4402 - val_root_mean_squared_log_error_K: 0.9620 Epoch 9/10 514/514 [==============================] - 2s 3ms/step - loss: 31.5294 - mse: 1633.3875 - root_mean_squared_log_error_K: 0.6571 - val_loss: 54.1435 - val_mse: 3516.9392 - val_root_mean_squared_log_error_K: 0.9275 Epoch 10/10 514/514 [==============================] - 2s 3ms/step - loss: 30.8109 - mse: 1594.9158 - root_mean_squared_log_error_K: 0.6495 - val_loss: 58.8877 - val_mse: 4066.0483 - val_root_mean_squared_log_error_K: 1.0366
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('Loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()
findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans. findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans. findfont: Font family ['sans-serif'] not found. Falling back to DejaVu Sans.
plt.plot(history.history['root_mean_squared_log_error_K'])
plt.plot(history.history['val_root_mean_squared_log_error_K'])
plt.title('model rmsle')
plt.ylabel('rmse')
plt.xlabel('epoch')
plt.legend(['train', 'val'], loc='upper left')
plt.show()
reg1 = setup(data = final_train, target = 'target' , preprocess = False,
use_gpu = True , test_data = final_cv , session_id = 42)
best = compare_models(sort = 'RMSLE')
| Model | MAE | MSE | RMSE | R2 | RMSLE | MAPE | TT (Sec) | |
|---|---|---|---|---|---|---|---|---|
| gbr | Gradient Boosting Regressor | 17.1305 | 612.9593 | 24.7304 | 0.0905 | 0.5570 | 0.1775 | 2.1760 |
| rf | Random Forest Regressor | 18.1150 | 656.1216 | 25.5887 | 0.0259 | 0.5606 | 0.1894 | 3.4750 |
| ada | AdaBoost Regressor | 18.0983 | 628.0002 | 25.0380 | 0.0669 | 0.5607 | 0.1954 | 1.9630 |
| huber | Huber Regressor | 17.8223 | 673.3141 | 25.9154 | 0.0017 | 0.5686 | 0.1796 | 0.5700 |
| et | Extra Trees Regressor | 19.2618 | 727.3399 | 26.9484 | -0.0811 | 0.5688 | 0.2018 | 3.0200 |
| knn | K Neighbors Regressor | 19.4121 | 731.4306 | 27.0167 | -0.0857 | 0.5702 | 0.2043 | 0.3370 |
| lr | Linear Regression | 18.0884 | 660.6728 | 25.6774 | 0.0195 | 0.5720 | 0.1890 | 0.1220 |
| ridge | Ridge Regression | 18.0882 | 660.6619 | 25.6771 | 0.0195 | 0.5720 | 0.1890 | 0.1430 |
| omp | Orthogonal Matching Pursuit | 18.0771 | 660.5472 | 25.6723 | 0.0201 | 0.5721 | 0.1890 | 0.0910 |
| lasso | Lasso Regression | 18.0597 | 659.9518 | 25.6603 | 0.0211 | 0.5722 | 0.1889 | 0.1330 |
| en | Elastic Net | 18.0578 | 659.7975 | 25.6575 | 0.0213 | 0.5722 | 0.1888 | 0.1670 |
| br | Bayesian Ridge | 18.0728 | 660.7816 | 25.6763 | 0.0199 | 0.5725 | 0.1890 | 0.1780 |
| llar | Lasso Least Angle Regression | 18.3334 | 675.4443 | 25.9595 | -0.0018 | 0.5767 | 0.1923 | 0.1000 |
| dummy | Dummy Regressor | 18.3334 | 675.4443 | 25.9595 | -0.0018 | 0.5767 | 0.1923 | 0.0150 |
| par | Passive Aggressive Regressor | 20.7461 | 811.2826 | 28.4066 | -0.2056 | 0.5852 | 0.2175 | 0.1160 |
| dt | Decision Tree Regressor | 25.4227 | 1249.4717 | 35.3226 | -0.8599 | 0.7948 | 0.2716 | 0.2500 |
| lar | Least Angle Regression | 3573515.2507 | 793565330707716.1250 | 8983250.0830 | -1382241738217.0886 | 4.5618 | 41290.3543 | 0.1150 |
best_model = tune_model(best ,optimize = 'RMSLE',n_iter = 50)
| MAE | MSE | RMSE | R2 | RMSLE | MAPE | |
|---|---|---|---|---|---|---|
| 0 | 17.5164 | 639.4019 | 25.2864 | 0.1053 | 0.5325 | 0.1730 |
| 1 | 17.5700 | 648.3123 | 25.4620 | 0.0906 | 0.6856 | 0.1705 |
| 2 | 17.3576 | 628.5803 | 25.0715 | 0.1160 | 0.5634 | 0.1832 |
| 3 | 18.0875 | 667.5394 | 25.8368 | 0.0358 | 0.4738 | 0.1867 |
| 4 | 16.6387 | 578.2183 | 24.0462 | 0.0785 | 0.5034 | 0.2008 |
| 5 | 18.3148 | 687.1918 | 26.2143 | 0.1076 | 0.6536 | 0.1786 |
| 6 | 15.8349 | 525.6983 | 22.9281 | 0.0843 | 0.4848 | 0.1677 |
| 7 | 16.3627 | 554.2783 | 23.5431 | 0.1088 | 0.5877 | 0.1615 |
| 8 | 16.2807 | 533.9828 | 23.1081 | 0.0862 | 0.4663 | 0.1807 |
| 9 | 17.6739 | 670.7220 | 25.8983 | 0.0845 | 0.6197 | 0.1768 |
| Mean | 17.1637 | 613.3925 | 24.7395 | 0.0898 | 0.5571 | 0.1780 |
| SD | 0.7892 | 57.0257 | 1.1622 | 0.0217 | 0.0739 | 0.0104 |
best_model
GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',
init=None, learning_rate=0.4, loss='ls', max_depth=1,
max_features=1.0, max_leaf_nodes=None,
min_impurity_decrease=0.4, min_impurity_split=None,
min_samples_leaf=4, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=130,
n_iter_no_change=None, presort='deprecated',
random_state=42, subsample=0.6, tol=0.0001,
validation_fraction=0.1, verbose=0, warm_start=False)
grb_submission = pd.DataFrame({"LAP_TIME" :best_model.predict(final_test)})
grb_submission.to_csv("grb.csv",index=False)
# top5 = compare_models(n_select = 5 , optimize = 'RMSLE', meta_model = 'gbr')
# stacker = stack_models(top5)
gbr = create_model(estimator = 'gbr')
rf = create_model(estimator = 'rf')
et = create_model(estimator = 'et')
ada = create_model(estimator = 'ada')
huber = create_model(estimator = 'huber')
top3 = compare_models(n_select = 3 , sort= 'RMSLE')
| Model | MAE | MSE | RMSE | R2 | RMSLE | MAPE | TT (Sec) | |
|---|---|---|---|---|---|---|---|---|
| gbr | Gradient Boosting Regressor | 17.1305 | 612.9593 | 24.7304 | 0.0905 | 0.5570 | 0.1775 | 2.1800 |
| rf | Random Forest Regressor | 18.1150 | 656.1216 | 25.5887 | 0.0259 | 0.5606 | 0.1894 | 3.5550 |
| ada | AdaBoost Regressor | 18.0983 | 628.0002 | 25.0380 | 0.0669 | 0.5607 | 0.1954 | 1.9890 |
| huber | Huber Regressor | 17.8223 | 673.3141 | 25.9154 | 0.0017 | 0.5686 | 0.1796 | 0.5800 |
| et | Extra Trees Regressor | 19.2618 | 727.3399 | 26.9484 | -0.0811 | 0.5688 | 0.2018 | 3.0620 |
| knn | K Neighbors Regressor | 19.4121 | 731.4306 | 27.0167 | -0.0857 | 0.5702 | 0.2043 | 0.2150 |
| lr | Linear Regression | 18.0884 | 660.6728 | 25.6774 | 0.0195 | 0.5720 | 0.1890 | 0.1220 |
| ridge | Ridge Regression | 18.0882 | 660.6619 | 25.6771 | 0.0195 | 0.5720 | 0.1890 | 0.0920 |
| omp | Orthogonal Matching Pursuit | 18.0771 | 660.5472 | 25.6723 | 0.0201 | 0.5721 | 0.1890 | 0.1020 |
| lasso | Lasso Regression | 18.0597 | 659.9518 | 25.6603 | 0.0211 | 0.5722 | 0.1889 | 0.1330 |
| en | Elastic Net | 18.0578 | 659.7975 | 25.6575 | 0.0213 | 0.5722 | 0.1888 | 0.1570 |
| br | Bayesian Ridge | 18.0728 | 660.7816 | 25.6763 | 0.0199 | 0.5725 | 0.1890 | 0.1570 |
| llar | Lasso Least Angle Regression | 18.3334 | 675.4443 | 25.9595 | -0.0018 | 0.5767 | 0.1923 | 0.0890 |
| dummy | Dummy Regressor | 18.3334 | 675.4443 | 25.9595 | -0.0018 | 0.5767 | 0.1923 | 0.0150 |
| par | Passive Aggressive Regressor | 20.7461 | 811.2826 | 28.4066 | -0.2056 | 0.5852 | 0.2175 | 0.1100 |
| dt | Decision Tree Regressor | 25.4227 | 1249.4717 | 35.3226 | -0.8599 | 0.7948 | 0.2716 | 0.2490 |
| lar | Least Angle Regression | 3573515.2507 | 793565330707716.1250 | 8983250.0830 | -1382241738217.0886 | 4.5618 | 41290.3543 | 0.1180 |
top3
[GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',
init=None, learning_rate=0.1, loss='ls', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=42, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0, warm_start=False),
RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
max_depth=None, max_features='auto', max_leaf_nodes=None,
max_samples=None, min_impurity_decrease=0.0,
min_impurity_split=None, min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=100, n_jobs=-1, oob_score=False,
random_state=42, verbose=0, warm_start=False),
AdaBoostRegressor(base_estimator=None, learning_rate=1.0, loss='linear',
n_estimators=50, random_state=42)]
tuned_top3 = []
for i in top3:
tuned_top3.append(tune_model(i ,optimize = 'RMSLE',n_iter = 5))
| MAE | MSE | RMSE | R2 | RMSLE | MAPE | |
|---|---|---|---|---|---|---|
| 0 | 18.2669 | 636.6738 | 25.2324 | 0.1091 | 0.5332 | 0.1880 |
| 1 | 18.9894 | 689.8706 | 26.2654 | 0.0323 | 0.6971 | 0.1932 |
| 2 | 18.0645 | 638.6464 | 25.2715 | 0.1018 | 0.5673 | 0.1984 |
| 3 | 19.0738 | 680.2577 | 26.0817 | 0.0174 | 0.4781 | 0.2048 |
| 4 | 17.6260 | 601.0620 | 24.5166 | 0.0421 | 0.5087 | 0.2180 |
| 5 | 18.6431 | 674.8036 | 25.9770 | 0.1237 | 0.6532 | 0.1890 |
| 6 | 16.8490 | 543.7153 | 23.3177 | 0.0529 | 0.4902 | 0.1862 |
| 7 | 17.5048 | 576.2179 | 24.0045 | 0.0736 | 0.5916 | 0.1805 |
| 8 | 17.2847 | 555.3886 | 23.5667 | 0.0496 | 0.4688 | 0.1994 |
| 9 | 18.2150 | 671.8698 | 25.9205 | 0.0829 | 0.6206 | 0.1880 |
| Mean | 18.0517 | 626.8506 | 25.0154 | 0.0686 | 0.5609 | 0.1946 |
| SD | 0.6973 | 51.6069 | 1.0396 | 0.0336 | 0.0745 | 0.0104 |
tuned_top3
[GradientBoostingRegressor(alpha=0.9, ccp_alpha=0.0, criterion='friedman_mse',
init=None, learning_rate=0.05, loss='ls', max_depth=6,
max_features='sqrt', max_leaf_nodes=None,
min_impurity_decrease=0.0002, min_impurity_split=None,
min_samples_leaf=3, min_samples_split=5,
min_weight_fraction_leaf=0.0, n_estimators=120,
n_iter_no_change=None, presort='deprecated',
random_state=42, subsample=0.65, tol=0.0001,
validation_fraction=0.1, verbose=0, warm_start=False),
RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',
max_depth=8, max_features='log2', max_leaf_nodes=None,
max_samples=None, min_impurity_decrease=0,
min_impurity_split=None, min_samples_leaf=4,
min_samples_split=10, min_weight_fraction_leaf=0.0,
n_estimators=90, n_jobs=-1, oob_score=False,
random_state=42, verbose=0, warm_start=False),
AdaBoostRegressor(base_estimator=None, learning_rate=0.4, loss='linear',
n_estimators=160, random_state=42)]
stacker = stack_models(tuned_top3 , optimize = 'RMSLE', meta_model = best_model)
| MAE | MSE | RMSE | R2 | RMSLE | MAPE | |
|---|---|---|---|---|---|---|
| 0 | 17.4243 | 638.5719 | 25.2700 | 0.1065 | 0.5314 | 0.1721 |
| 1 | 17.5472 | 654.1302 | 25.5760 | 0.0824 | 0.6880 | 0.1698 |
| 2 | 17.3718 | 629.6373 | 25.0926 | 0.1145 | 0.5619 | 0.1823 |
| 3 | 18.1342 | 656.5803 | 25.6238 | 0.0516 | 0.4731 | 0.1881 |
| 4 | 16.6183 | 579.2394 | 24.0674 | 0.0769 | 0.5041 | 0.1999 |
| 5 | 18.2630 | 685.9886 | 26.1914 | 0.1092 | 0.6531 | 0.1780 |
| 6 | 15.6861 | 523.2954 | 22.8757 | 0.0885 | 0.4848 | 0.1664 |
| 7 | 16.2649 | 551.2834 | 23.4794 | 0.1137 | 0.5855 | 0.1603 |
| 8 | 16.1148 | 527.5120 | 22.9676 | 0.0973 | 0.4667 | 0.1791 |
| 9 | 17.6350 | 674.7490 | 25.9759 | 0.0790 | 0.6209 | 0.1764 |
| Mean | 17.1060 | 612.0988 | 24.7120 | 0.0920 | 0.5570 | 0.1772 |
| SD | 0.8358 | 58.2732 | 1.1904 | 0.0191 | 0.0743 | 0.0107 |
grb_submission = pd.DataFrame({"LAP_TIME" :stacker.predict(final_test)})
grb_submission.to_csv("stacker.csv",index=False)