import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib .pyplot as plt
import math
import copy
import pickle
import gc
from sklearn .model_selection import StratifiedKFold
from xgboost import XGBClassifier
from sklearn .metrics import roc_auc_score
from sklearn .base import clone
from sklearn .preprocessing import StandardScaler Step 1. Load and explore the data TARGET = 'Response'
SEED = 94 print ('Loading Data...' )
train = pd .read_csv ('input/train.csv' )
test = pd .read_csv ('input/test.csv' )
submission_data = pd .read_csv ('input/sample_submission.csv' )
print ('Data Load Successfully.' )Loading Data...
Data Load Successfully.
((11504798, 12), (7669866, 11))
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11504798 entries, 0 to 11504797
Data columns (total 12 columns):
# Column Dtype
--- ------ -----
0 id int64
1 Gender object
2 Age int64
3 Driving_License int64
4 Region_Code float64
5 Previously_Insured int64
6 Vehicle_Age object
7 Vehicle_Damage object
8 Annual_Premium float64
9 Policy_Sales_Channel float64
10 Vintage int64
11 Response int64
dtypes: float64(3), int64(6), object(3)
memory usage: 1.0+ GB
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id
Age
Driving_License
Region_Code
Previously_Insured
Annual_Premium
Policy_Sales_Channel
Vintage
Response
count
1.150480e+07
1.150480e+07
1.150480e+07
1.150480e+07
1.150480e+07
1.150480e+07
1.150480e+07
1.150480e+07
1.150480e+07
mean
5.752398e+06
3.838356e+01
9.980220e-01
2.641869e+01
4.629966e-01
3.046137e+04
1.124254e+02
1.638977e+02
1.229973e-01
std
3.321149e+06
1.499346e+01
4.443120e-02
1.299159e+01
4.986289e-01
1.645475e+04
5.403571e+01
7.997953e+01
3.284341e-01
min
0.000000e+00
2.000000e+01
0.000000e+00
0.000000e+00
0.000000e+00
2.630000e+03
1.000000e+00
1.000000e+01
0.000000e+00
25%
2.876199e+06
2.400000e+01
1.000000e+00
1.500000e+01
0.000000e+00
2.527700e+04
2.900000e+01
9.900000e+01
0.000000e+00
50%
5.752398e+06
3.600000e+01
1.000000e+00
2.800000e+01
0.000000e+00
3.182400e+04
1.510000e+02
1.660000e+02
0.000000e+00
75%
8.628598e+06
4.900000e+01
1.000000e+00
3.500000e+01
1.000000e+00
3.945100e+04
1.520000e+02
2.320000e+02
0.000000e+00
max
1.150480e+07
8.500000e+01
1.000000e+00
5.200000e+01
1.000000e+00
5.401650e+05
1.630000e+02
2.990000e+02
1.000000e+00
#Combine daataset for processing
train ['is_train' ] = 1
test ['is_train' ] = 0
df = pd .concat ([train , test ])
df .head ()
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id
Gender
Age
Driving_License
Region_Code
Previously_Insured
Vehicle_Age
Vehicle_Damage
Annual_Premium
Policy_Sales_Channel
Vintage
Response
is_train
0
0
Male
21
1
35.0
0
1-2 Year
Yes
65101.0
124.0
187
0.0
1
1
1
Male
43
1
28.0
0
> 2 Years
Yes
58911.0
26.0
288
1.0
1
2
2
Female
25
1
14.0
1
< 1 Year
No
38043.0
152.0
254
0.0
1
3
3
Female
35
1
1.0
0
1-2 Year
Yes
2630.0
156.0
76
0.0
1
4
4
Female
36
1
15.0
1
1-2 Year
No
31951.0
152.0
294
0.0
1
Step 2. Data preprocessing # Check missing values
df .isnull ().sum ()id 0
Gender 0
Age 0
Driving_License 0
Region_Code 0
Previously_Insured 0
Vehicle_Age 0
Vehicle_Damage 0
Annual_Premium 0
Policy_Sales_Channel 0
Vintage 0
Response 7669866
is_train 0
dtype: int64
Age and Vehicle_Age (0.77) :
Strong positive correlation. Older individuals tend to have older vehicles.Previously_Insured and Vehicle_Damage (-0.84) :
Strong negative correlation. If someone is previously insured, their vehicle is less likely to be damaged.Policy_Sales_Channel and Age (-0.60) :
Moderate negative correlation. Younger individuals are more likely to be reached through certain sales channels.
def transform_categorical_features (df ):
print ('Transforming categorical features..' )
gender_map = {'Male' : 0 , 'Female' : 1 }
vehicle_age = {'< 1 Year' : 0 , '1-2 Year' : 1 , '> 2 Years' : 2 }
vehicle_damage = {'No' :0 , 'Yes' :1 }
df ['Gender' ] = df ['Gender' ].map (gender_map )
df ['Vehicle_Age' ] = df ['Vehicle_Age' ].map (vehicle_age )
df ['Vehicle_Damage' ] = df ['Vehicle_Damage' ].map (vehicle_damage )
print ("Transformed successfully." )
return df def create_additional_features (df ):
print ('Creating additional features..' )
df ['Vehicle_Age_Policy_Sales_Channel' ] = pd .factorize (df ['Vehicle_Age' ].astype (str ) + df ['Policy_Sales_Channel' ].astype (str ))[0 ]
df ['Age_Vehicle_Age' ] = pd .factorize (df ['Age' ].astype (str ) + df ['Vehicle_Age' ].astype (str ))[0 ]
df ['Prev_Insured_Vehicle_Damage' ] = pd .factorize (df ['Previously_Insured' ].astype (str ) + df ['Vehicle_Damage' ].astype (str ))[0 ]
df ['Prev_Insured_Vintage' ] = pd .factorize (df ['Previously_Insured' ].astype (str ) + df ['Vintage' ].astype (str ))[0 ]
df ['Policy_Sales_Channel_Age' ] = pd .factorize (df ['Policy_Sales_Channel' ].astype (str ) + df ['Age' ].astype (str ))[0 ]
return df def adjust_data_types (df ):
print ('Adjusting data types' )
df ['Region_Code' ] = df ['Region_Code' ].astype (int )
df ['Annual_Premium' ] = df ['Annual_Premium' ].astype (int )
df ['Policy_Sales_Channel' ] = df ['Policy_Sales_Channel' ].astype (int )
return df def optimize_memory_usage (df ):
print ('Optimizing memory usage' )
start_mem_usage = df .memory_usage ().sum () / 1024 ** 2
for col in df .columns :
col_type = df [col ].dtype
if col_type .name in ['category' , 'object' ]:
raise ValueError (f"Column '{ col } { col_type .name } )
c_min = df [col ].min ()
c_max = df [col ].max ()
if str (col_type )[:3 ] == 'int' :
if c_min > np .iinfo (np .int8 ).min and c_max < np .iinfo (np .int8 ).max :
df [col ] = df [col ].astype (np .int8 )
elif c_min > np .iinfo (np .int16 ).min and c_max < np .iinfo (np .int16 ).max :
df [col ] = df [col ].astype (np .int16 )
elif c_min > np .iinfo (np .int32 ).min and c_max < np .iinfo (np .int32 ).max :
df [col ] = df [col ].astype (np .int32 )
elif c_min > np .iinfo (np .int64 ).min and c_max < np .iinfo (np .int64 ).max :
df [col ] = df [col ].astype (np .int64 )
else :
if c_min > np .finfo (np .float16 ).min and c_max < np .finfo (np .float16 ).max :
df [col ] = df [col ].astype (np .float16 )
elif c_min > np .finfo (np .float32 ).min and c_max < np .finfo (np .float32 ).max :
df [col ] = df [col ].astype (np .float32 )
else :
df [col ] = df [col ].astype (np .float64 )
end_mem_usage = df .memory_usage ().sum () / 1024 ** 2
print (f'------ Memory usage before: { start_mem_usage :.2f} )
print (f'------ Memory usage after: { end_mem_usage :.2f} )
print (f'------ Reduced memory usage by { (100 * (start_mem_usage - end_mem_usage ) / start_mem_usage ):.1f} )
return df
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id
Gender
Age
Driving_License
Region_Code
Previously_Insured
Vehicle_Age
Vehicle_Damage
Annual_Premium
Policy_Sales_Channel
Vintage
Response
is_train
0
0
Male
21
1
35.0
0
1-2 Year
Yes
65101.0
124.0
187
0.0
1
1
1
Male
43
1
28.0
0
> 2 Years
Yes
58911.0
26.0
288
1.0
1
2
2
Female
25
1
14.0
1
< 1 Year
No
38043.0
152.0
254
0.0
1
3
3
Female
35
1
1.0
0
1-2 Year
Yes
2630.0
156.0
76
0.0
1
4
4
Female
36
1
15.0
1
1-2 Year
No
31951.0
152.0
294
0.0
1
df = transform_categorical_features (df )
df = adjust_data_types (df )
df = create_additional_features (df )
df = optimize_memory_usage (df )
df .head () Transforming categorical features..
Transformed successfully.
Adjusting data types
Creating additional features..
Optimizing memory usage
------ Memory usage before: 2560.09 MB
------ Memory usage after: 713.17 MB
------ Reduced memory usage by 72.1%
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id
Gender
Age
Driving_License
Region_Code
Previously_Insured
Vehicle_Age
Vehicle_Damage
Annual_Premium
Policy_Sales_Channel
Vintage
Response
is_train
Vehicle_Age_Policy_Sales_Channel
Age_Vehicle_Age
Prev_Insured_Vehicle_Damage
Prev_Insured_Vintage
Policy_Sales_Channel_Age
0
0
0
21
1
35
0
1
1
65101
124
187
0.0
1
0
0
0
0
0
1
1
0
43
1
28
0
2
1
58911
26
288
1.0
1
1
1
0
1
1
2
2
1
25
1
14
1
0
0
38043
152
254
0.0
1
2
2
1
2
2
3
3
1
35
1
1
0
1
1
2630
156
76
0.0
1
3
3
0
3
3
4
4
1
36
1
15
1
1
0
31951
152
294
0.0
1
4
4
1
4
4
# Compute the correlation matrix
corr = df .corr ()
# Create a heatmap of the correlation matrix
plt .figure (figsize = (12 , 8 ))
sns .heatmap (corr , annot = True , cmap = 'coolwarm' , fmt = '.2f' , vmin = - 1 , vmax = 1 )
plt .title ('Correlation Matrix' )
plt .show ()from sklearn .preprocessing import MinMaxScaler
# Initialize MinMaxScaler
min_max_scaler = MinMaxScaler ()
# Select features to scale
features_to_scale = ['Annual_Premium' , 'Vintage' , 'Policy_Sales_Channel' ]
# Fit and transform the selected features
df [features_to_scale ] = min_max_scaler .fit_transform (df [features_to_scale ])# Split the data back into train and test sets
train = df [df ['is_train' ] == 1 ].drop (columns = ['is_train' ])
test = df [df ['is_train' ] == 0 ].drop (columns = ['is_train' ])
X_train = train .drop (columns = [TARGET ])
y_train = train [TARGET ]
X_test = test .drop (columns = [TARGET ])
y_test = submission_data Subsample the data to speed up training process X_train_subsample = X_train .sample (frac = 0.01 , random_state = 42 )
y_train_subsample = y_train .sample (frac = 0.01 , random_state = 42 )X_test_subsample = X_test .sample (frac = 0.01 , random_state = 42 )X_train_subsample .shape
X_test_subsample .shape import tensorflow as tf
from tensorflow .keras .models import Sequential
from tensorflow .keras .layers import Dense , Input
from tensorflow .keras .optimizers import Adam
from tensorflow .keras .callbacks import EarlyStopping , Callback
# Custom callback to print additional training information
class CustomCallback (Callback ):
def on_epoch_end (self , epoch , logs = None ):
print (f"Epoch { epoch + 1 } { self .params ['epochs' ]} )
print (f" - loss: { logs ['loss' ]:.4f} { logs ['auc' ]:.4f} { logs ['val_loss' ]:.4f} { logs ['val_auc' ]:.4f} )
# Build the model
model = Sequential ()
model .add (Input (shape = (X_train .shape [1 ],)))
model .add (Dense (128 , activation = 'relu' ))
model .add (Dense (64 , activation = 'relu' ))
model .add (Dense (32 , activation = 'relu' ))
model .add (Dense (1 , activation = 'sigmoid' ))
# Compile the model with AUC as a metric
model .compile (optimizer = Adam (learning_rate = 0.001 ), loss = 'binary_crossentropy' , metrics = [tf .keras .metrics .AUC ()])
# Define early stopping callback
early_stopping = EarlyStopping (monitor = 'val_loss' , patience = 10 , restore_best_weights = True )
# Train the model
history = model .fit (X_train , y_train , epochs = 100 , validation_split = 0.2 , callbacks = [early_stopping ])
# Evaluate the model
loss , auc = model .evaluate (X_test , y_test )
print (f"Test AUC: { auc :.4f} )
# Predict probabilities
y_pred_proba = model .predict (X_test ).ravel ()
# Calculate the AUC score
roc_auc = roc_auc_score (y_test , y_pred_proba )
print (f"ROC AUC: { roc_auc :.4f} )Epoch 1/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m301s�[0m 1ms/step - auc_2: 0.5000 - loss: 132.1440 - val_auc_2: 0.5000 - val_loss: 0.3724
Epoch 2/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m250s�[0m 868us/step - auc_2: 0.5003 - loss: 0.3755 - val_auc_2: 0.5000 - val_loss: 0.3724
Epoch 3/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m251s�[0m 872us/step - auc_2: 0.4995 - loss: 0.3732 - val_auc_2: 0.5000 - val_loss: 0.3723
Epoch 4/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m258s�[0m 898us/step - auc_2: 0.4995 - loss: 0.3730 - val_auc_2: 0.5000 - val_loss: 0.3725
Epoch 5/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m264s�[0m 917us/step - auc_2: 0.4997 - loss: 0.3728 - val_auc_2: 0.5000 - val_loss: 0.3723
Epoch 6/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m271s�[0m 940us/step - auc_2: 0.4992 - loss: 0.3731 - val_auc_2: 0.5000 - val_loss: 0.3723
Epoch 7/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m270s�[0m 939us/step - auc_2: 0.4998 - loss: 0.3727 - val_auc_2: 0.5000 - val_loss: 0.3724
Epoch 8/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m264s�[0m 918us/step - auc_2: 0.4998 - loss: 0.3733 - val_auc_2: 0.5000 - val_loss: 0.3724
Epoch 9/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m261s�[0m 906us/step - auc_2: 0.4993 - loss: 0.3727 - val_auc_2: 0.5000 - val_loss: 0.3722
Epoch 10/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m265s�[0m 920us/step - auc_2: 0.4994 - loss: 0.3725 - val_auc_2: 0.5000 - val_loss: 0.3724
Epoch 11/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m270s�[0m 936us/step - auc_2: 0.4999 - loss: 0.3727 - val_auc_2: 0.5000 - val_loss: 0.3723
Epoch 12/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m257s�[0m 894us/step - auc_2: 0.4999 - loss: 0.3728 - val_auc_2: 0.5000 - val_loss: 0.3724
Epoch 13/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m256s�[0m 888us/step - auc_2: 0.4999 - loss: 0.3726 - val_auc_2: 0.5000 - val_loss: 0.3723
Epoch 14/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m248s�[0m 862us/step - auc_2: 0.4998 - loss: 0.3729 - val_auc_2: 0.5000 - val_loss: 0.3723
Epoch 15/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m250s�[0m 867us/step - auc_2: 0.5002 - loss: 0.3728 - val_auc_2: 0.5000 - val_loss: 0.3724
Epoch 16/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m251s�[0m 872us/step - auc_2: 0.4998 - loss: 0.3729 - val_auc_2: 0.5000 - val_loss: 0.3723
Epoch 17/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m251s�[0m 871us/step - auc_2: 0.4995 - loss: 0.3727 - val_auc_2: 0.5000 - val_loss: 0.3724
Epoch 18/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m253s�[0m 878us/step - auc_2: 0.4999 - loss: 0.3730 - val_auc_2: 0.5000 - val_loss: 0.3723
Epoch 19/100
�[1m287620/287620�[0m �[32m━━━━━━━━━━━━━━━━━━━━�[0m�[37m�[0m �[1m253s�[0m 879us/step - auc_2: 0.5002 - loss: 0.3729 - val_auc_2: 0.5000 - val_loss: 0.3723
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
Cell In[22], line 32
29 history = model.fit(X_train, y_train, epochs=100, validation_split=0.2, callbacks=[early_stopping])
31 # Evaluate the model
---> 32 loss, auc = model.evaluate(X_test, y_test)
33 print(f"Test AUC: {auc:.4f}")
35 # Predict probabilities
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\utils\traceback_utils.py:122, in filter_traceback.<locals>.error_handler(*args, **kwargs)
119 filtered_tb = _process_traceback_frames(e.__traceback__)
120 # To get the full stack trace, call:
121 # `keras.config.disable_traceback_filtering()`
--> 122 raise e.with_traceback(filtered_tb) from None
123 finally:
124 del filtered_tb
File ~\AppData\Local\Programs\Python\Python312\Lib\site-packages\tensorflow\python\eager\execute.py:53, in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
51 try:
52 ctx.ensure_initialized()
---> 53 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
54 inputs, attrs, num_outputs)
55 except core._NotOkStatusException as e:
56 if name is not None:
InvalidArgumentError: Graph execution error:
Detected at node UnsortedSegmentSum_1 defined at (most recent call last):
File "<frozen runpy>", line 198, in _run_module_as_main
File "<frozen runpy>", line 88, in _run_code
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\ipykernel_launcher.py", line 18, in <module>
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\traitlets\config\application.py", line 1075, in launch_instance
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\ipykernel\kernelapp.py", line 739, in start
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\tornado\platform\asyncio.py", line 205, in start
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\asyncio\base_events.py", line 641, in run_forever
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\asyncio\base_events.py", line 1987, in _run_once
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\asyncio\events.py", line 88, in _run
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\ipykernel\kernelbase.py", line 545, in dispatch_queue
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\ipykernel\kernelbase.py", line 534, in process_one
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\ipykernel\kernelbase.py", line 437, in dispatch_shell
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\ipykernel\ipkernel.py", line 362, in execute_request
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\ipykernel\kernelbase.py", line 778, in execute_request
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\ipykernel\ipkernel.py", line 449, in do_execute
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\ipykernel\zmqshell.py", line 549, in run_cell
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\interactiveshell.py", line 3075, in run_cell
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\interactiveshell.py", line 3130, in _run_cell
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\async_helpers.py", line 129, in _pseudo_sync_runner
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\interactiveshell.py", line 3334, in run_cell_async
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\interactiveshell.py", line 3517, in run_ast_nodes
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\IPython\core\interactiveshell.py", line 3577, in run_code
File "C:\Users\user\AppData\Local\Temp\ipykernel_14524\278436547.py", line 32, in <module>
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\utils\traceback_utils.py", line 117, in error_handler
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\backend\tensorflow\trainer.py", line 425, in evaluate
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\backend\tensorflow\trainer.py", line 161, in one_step_on_iterator
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\backend\tensorflow\trainer.py", line 150, in one_step_on_data
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\backend\tensorflow\trainer.py", line 87, in test_step
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\trainers\trainer.py", line 412, in compute_metrics
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\trainers\compile_utils.py", line 330, in update_state
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\trainers\compile_utils.py", line 17, in update_state
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\metrics\confusion_metrics.py", line 1379, in update_state
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\metrics\metrics_utils.py", line 481, in update_confusion_matrix_variables
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\metrics\metrics_utils.py", line 277, in _update_confusion_matrix_variables_optimized
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\ops\math.py", line 59, in segment_sum
File "C:\Users\user\AppData\Local\Programs\Python\Python312\Lib\site-packages\keras\src\backend\tensorflow\math.py", line 17, in segment_sum
data.shape = [64] does not start with segment_ids.shape = [32]
[[{{node UnsortedSegmentSum_1}}]] [Op:__inference_one_step_on_iterator_26047228]
Step 5. Train and evaluate the model def train_and_evaluate (model , X , y , X_test , folds = 10 , random_state = None ):
print (f'Training { model .__class__ .__name__ } \n ' )
scores = []
feature_importances = np .zeros (X .shape [1 ])
evaluation_history = []
oof_pred_probs = np .zeros (X .shape [0 ])
test_pred_probs = np .zeros (X_test .shape [0 ])
skf = StratifiedKFold (n_splits = 10 , random_state = 94 , shuffle = True )
for fold_index , (train_index , val_index ) in enumerate (skf .split (X , y )):
X_train , X_val = X .iloc [train_index ], X .iloc [val_index ]
y_train , y_val = y .iloc [train_index ], y .iloc [val_index ]
model_clone = copy .deepcopy (model )
model_clone .fit (
X_train ,
y_train ,
eval_set = [(X_val , y_val )],
verbose = 500 )
feature_importances += model_clone .feature_importances_ / folds
evaluation_history .append (model_clone .evals_result ())
y_pred_probs = model_clone .predict_proba (X_val )[:, 1 ]
oof_pred_probs [val_index ] = y_pred_probs
temp_test_pred_probs = model_clone .predict_proba (X_test )[:, 1 ]
test_pred_probs += temp_test_pred_probs / folds
auc_score = roc_auc_score (y_val , y_pred_probs )
scores .append (auc_score )
print (f'\n --- Fold { fold_index + 1 } { auc_score :.5f} \n \n ' )
del model_clone
gc .collect ()
print (f'------ Average AUC: { np .mean (scores ):.5f} { np .std (scores ):.5f} \n \n ' )
return oof_pred_probs , test_pred_probs best_params = {
'alpha' : 1.302348865795227e-06 ,
'max_depth' : 15 ,
'learning_rate' : 0.061800451723613786 ,
'subsample' : 0.7098803046786328 ,
'colsample_bytree' : 0.2590672912533101 ,
'min_child_weight' : 10 ,
'gamma' : 0.8399887056014855 ,
'reg_alpha' : 0.0016943548302122801 ,
'max_bin' : 71284 ,
'early_stopping_rounds' : 50
}
best_xgb_model = XGBClassifier (** best_params , n_estimators = 12000 , random_state = 94 , eval_metric = "auc" )
# Call train_and_evaluate function with XGBClassifier model
oof_pred_probs , predictions = train_and_evaluate (best_xgb_model , X_train , y_train , X_test , folds = 10 , random_state = SEED )submission = pd .DataFrame ({
'id' : X_test ['id' ],
'Response' : predictions
})
submission .to_csv ('submission.csv' , index = False )
submission .head ()##Binning some features
from sklearn .preprocessing import LabelEncoder , StandardScaler
# Binning Vintage
bins_vintage = [0 , 200 , 400 , 600 , 800 , float ('inf' )]
labels_vintage = ['Very New' , 'New' , 'Moderately New' , 'Experienced' , 'Very Experienced' ]
df ['Vintage_Binned' ] = pd .cut (df ['Vintage' ], bins = bins_vintage , labels = labels_vintage )
# Binning Annual_Premium
bins_premium = [0 , 10000 , 30000 , 50000 , 100000 , float ('inf' )]
labels_premium = ['Very Low' , 'Low' , 'Moderate' , 'High' , 'Very High' ]
df ['Annual_Premium_Binned' ] = pd .cut (df ['Annual_Premium' ], bins = bins_premium , labels = labels_premium )
# Encoding Policy_Sales_Channel
le = LabelEncoder ()
df ['Policy_Sales_Channel_Encoded' ] = le .fit_transform (df ['Policy_Sales_Channel' ])
# Dropping original columns
df = df .drop (['Vintage' , 'Annual_Premium' , 'Policy_Sales_Channel' ], axis = 1 )
df ['Annual_Premium_Binned_Numeric' ], _ = pd .factorize (df ['Annual_Premium_Binned' ])
df ['Vintage_Binned_Numeric' ],_ = pd .factorize (df ['Vintage_Binned' ])##Using RandomizedSearch - hyperparamiters tunning
from sklearn .model_selection import RandomizedSearchCV
from xgboost import XGBClassifier
xgb_params = {
'colsample_bylevel' : [0.1 , 0.2 , 0.3 , 0.5 , 0.7 , 1.0 ],
'colsample_bynode' : [0.1 , 0.2 , 0.3 , 0.5 , 0.7 , 1.0 ],
'colsample_bytree' : [0.1 , 0.2 , 0.3 , 0.5 , 0.7 , 1.0 ],
'gamma' : [0 , 0.1 , 0.5 , 0.6051 , 1 ],
'max_bin' : [256 , 512 , 682 , 1024 ],
'max_delta_step' : [0 , 1 , 5 , 7 , 10 ],
'max_depth' : [3 , 5 , 10 , 20 , 50 , 68 , 100 ],
'min_child_weight' : [1 , 3 , 5 , 7 , 10 ],
'n_estimators' : [100 , 500 , 1000 , 5000 , 10000 ],
'reg_alpha' : [0 , 0.1 , 0.4651 , 0.5 ],
'reg_lambda' : [0 , 0.1 , 0.5 , 1 ],
'subsample' : [0.5 , 0.6 , 0.7 , 0.8 , 0.9 , 1.0 ]
}
# Set up cross-validation strategy
cv = StratifiedKFold (n_splits = FOLDS , shuffle = True , random_state = SEED )
xgb_model = XGBClassifier (objective = "binary:logistic" , n_jobs = - 1 , random_state = SEED , eval_metric = "auc" , verbosity = 0 , tree_method = 'hist' )
random_search = RandomizedSearchCV (estimator = xgb_model , param_distributions = xgb_params , n_iter = 5 , scoring = 'roc_auc' , cv = cv , verbose = 1 , random_state = SEED )
print (random_search )
random_search .fit (X_train_subsample , y_train_subsample )
print ("Best parameters found: " , random_search .best_params_ )
print ("Best AUC score: " , random_search .best_score_ )