In [1]:
%load_ext autoreload
%autoreload 2
%matplotlib inline
In [2]:
from fastai.imports import *
from fastai.structured import *
from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from IPython.display import display
from sklearn import metrics
In [3]:
PATH = os.getcwd();
PATH
Out[3]:
'D:\\Github\\fastai\\courses\\ml1'
In [4]:
df_raw = pd.read_csv(f'{PATH}\\av_train_DaEJRFg.csv', low_memory=False, parse_dates=['incident_date'])
In [5]:
df_raw.head(1)
Out[5]:
victim_id
incident_time
incident_date
incident_location
incident_tehsil
cause_of_emergency
base_to_scene_distance
scene_to_hospital_distance
roadway_feature
road_type
surrounding_area
criticality
0
VIC20120001987267
22:23:19
2012-07-11
Subhash Nagar
Raipur
Multiple Vehicular Incident
13
12
CUR
HIW
FOR
0
In [6]:
df_raw.criticality.value_counts()
Out[6]:
0 11255
1 522
Name: criticality, dtype: int64
In [7]:
df_raw.head(3)
Out[7]:
victim_id
incident_time
incident_date
incident_location
incident_tehsil
cause_of_emergency
base_to_scene_distance
scene_to_hospital_distance
roadway_feature
road_type
surrounding_area
criticality
0
VIC20120001987267
22:23:19
2012-07-11
Subhash Nagar
Raipur
Multiple Vehicular Incident
13
12
CUR
HIW
FOR
0
1
VIC20110002147887
20:23:09
2011-02-11
Coro Nation
Raipur
2 Wheeler accidents
1
5
CUR
HIW
AGL
0
2
VIC20090001391483
20:37:15
2009-07-25
Kewal Vihar
Raipur
Non motorised vehicle accidents(Bullock cart,B...
2
2
INT
LOC
HOSP
0
In [24]:
add_datepart(test, 'incident_date')
test.head()
Out[24]:
victim_id
incident_time
incident_location
incident_tehsil
cause_of_emergency
base_to_scene_distance
scene_to_hospital_distance
roadway_feature
road_type
surrounding_area
...
incident_Day
incident_Dayofweek
incident_Dayofyear
incident_Is_month_end
incident_Is_month_start
incident_Is_quarter_end
incident_Is_quarter_start
incident_Is_year_end
incident_Is_year_start
incident_Elapsed
0
VIC20100001032706
15:46:12
Chc Sahaspur
Sahaspur
MVC - Pedestrian (Run Over/Hit & Run)
23
27
INT
LOC
MAR
...
19
2
139
False
False
False
False
False
False
1274227200
1
VIC20120000441519
16:55:28
Doiwala Ambulance
Doiwala
Multiple Vehicular Incident
5
21
CUR
HIW
AGL
...
18
6
78
False
False
False
False
False
False
1332028800
2
VIC20130000014119
18:49:35
Race Course Chowk
Raipur
Multiple Vehicular Incident
3
1
CUR
HIW
AGL
...
1
0
91
False
True
False
True
False
False
1364774400
3
VIC20140000614145
16:22:55
Selaqui
Sahaspur
Multiple Vehicular Incident
18
14
UNK
LOC
AGL
...
31
5
151
True
False
False
False
False
False
1401494400
4
VIC20090002298916
15:35:15
Ambari Mode,Dakpather
Vikasnagar
2 Wheeler accidents
6
10
INT
LOC
MAR
...
12
2
224
False
False
False
False
False
False
1250035200
5 rows × 23 columns
In [25]:
test.drop('incident_Elapsed', axis=1, inplace=True)
In [26]:
test['incident_location'].fillna('Dehradun',inplace=True)
In [27]:
train_cats(test)
In [115]:
os.makedirs('tmp', exist_ok=True)
df_raw.to_feather('tmp/av_rookree_raw')
In [187]:
test.head(2)
Out[187]:
victim_id
incident_time
incident_location
incident_tehsil
cause_of_emergency
base_to_scene_distance
scene_to_hospital_distance
roadway_feature
road_type
surrounding_area
...
incident_Week
incident_Day
incident_Dayofweek
incident_Dayofyear
incident_Is_month_end
incident_Is_month_start
incident_Is_quarter_end
incident_Is_quarter_start
incident_Is_year_end
incident_Is_year_start
0
VIC20100001032706
15:46:12
Chc Sahaspur
Sahaspur
MVC - Pedestrian (Run Over/Hit & Run)
23
27
INT
LOC
MAR
...
20
19
2
139
False
False
False
False
False
False
1
VIC20120000441519
16:55:28
Doiwala Ambulance
Doiwala
Multiple Vehicular Incident
5
21
CUR
HIW
AGL
...
11
18
6
78
False
False
False
False
False
False
2 rows × 22 columns
In [188]:
test.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5048 entries, 0 to 5047
Data columns (total 22 columns):
victim_id 5048 non-null category
incident_time 5048 non-null category
incident_location 5048 non-null category
incident_tehsil 5048 non-null category
cause_of_emergency 5048 non-null category
base_to_scene_distance 5048 non-null int64
scene_to_hospital_distance 5048 non-null int64
roadway_feature 5048 non-null category
road_type 5048 non-null category
surrounding_area 5048 non-null category
incident_Year 5048 non-null int64
incident_Month 5048 non-null int64
incident_Week 5048 non-null int64
incident_Day 5048 non-null int64
incident_Dayofweek 5048 non-null int64
incident_Dayofyear 5048 non-null int64
incident_Is_month_end 5048 non-null bool
incident_Is_month_start 5048 non-null bool
incident_Is_quarter_end 5048 non-null bool
incident_Is_quarter_start 5048 non-null bool
incident_Is_year_end 5048 non-null bool
incident_Is_year_start 5048 non-null bool
dtypes: bool(6), category(8), int64(8)
memory usage: 899.9 KB
In [15]:
y = df_raw.criticality
In [16]:
df_raw.drop('criticality',axis=1,inplace=True)
In [17]:
from sklearn.model_selection import train_test_split
X_train, X_validation, y_train, y_validation = train_test_split(df_raw, y, train_size=0.8, random_state=1234)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\model_selection\_split.py:2026: FutureWarning: From version 0.21, test_size will always complement train_size unless both are specified.
FutureWarning)
In [18]:
df_raw.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 11777 entries, 0 to 11776
Data columns (total 22 columns):
victim_id 11777 non-null category
incident_time 11777 non-null category
incident_location 11777 non-null category
incident_tehsil 11777 non-null category
cause_of_emergency 11777 non-null category
base_to_scene_distance 11777 non-null int64
scene_to_hospital_distance 11777 non-null int64
roadway_feature 11777 non-null category
road_type 11777 non-null category
surrounding_area 11777 non-null category
incident_Year 11777 non-null int64
incident_Month 11777 non-null int64
incident_Week 11777 non-null int64
incident_Day 11777 non-null int64
incident_Dayofweek 11777 non-null int64
incident_Dayofyear 11777 non-null int64
incident_Is_month_end 11777 non-null bool
incident_Is_month_start 11777 non-null bool
incident_Is_quarter_end 11777 non-null bool
incident_Is_quarter_start 11777 non-null bool
incident_Is_year_end 11777 non-null bool
incident_Is_year_start 11777 non-null bool
dtypes: bool(6), category(8), int64(8)
memory usage: 1.9 MB
In [19]:
categorical_features_indices = np.where(df_raw.dtypes != (np.int64 or np.bool))[0]
In [20]:
categorical_features_indices
Out[20]:
array([ 0, 1, 2, 3, 4, 7, 8, 9, 16, 17, 18, 19, 20, 21], dtype=int64)
In [21]:
#importing library and building model
from catboost import CatBoostClassifier
model=CatBoostClassifier(iterations=1000, depth=13, learning_rate=0.01, loss_function='CrossEntropy',\
)
model.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_validation, y_validation))
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bestTest = 0.1533056093
bestIteration = 977
Out[21]:
<catboost.core.CatBoostClassifier at 0x1419bcafba8>
In [22]:
test = pd.read_csv(f'{PATH}\\av_test_TQDFDgg.csv', low_memory=False, parse_dates=['incident_date'])
In [23]:
test.head(3)
Out[23]:
victim_id
incident_time
incident_date
incident_location
incident_tehsil
cause_of_emergency
base_to_scene_distance
scene_to_hospital_distance
roadway_feature
road_type
surrounding_area
0
VIC20100001032706
15:46:12
2010-05-19
Chc Sahaspur
Sahaspur
MVC - Pedestrian (Run Over/Hit & Run)
23
27
INT
LOC
MAR
1
VIC20120000441519
16:55:28
2012-03-18
Doiwala Ambulance
Doiwala
Multiple Vehicular Incident
5
21
CUR
HIW
AGL
2
VIC20130000014119
18:49:35
2013-04-01
Race Course Chowk
Raipur
Multiple Vehicular Incident
3
1
CUR
HIW
AGL
In [28]:
prediction_proba = model.predict_proba(test)
In [29]:
model.get_feature_importance(X_train,y_train,cat_features=categorical_features_indices)
Out[29]:
[0.0,
1.1129709561865988,
4.4016411556994965,
6.1345647175531255,
6.998560673937555,
5.972310613916887,
37.423568482713634,
3.0380239859691693,
1.622082743095675,
3.1978542547884197,
10.258152030191379,
1.7966032786782968,
2.3300737146483876,
3.7255273601526704,
3.442680584435092,
3.327519385745386,
0.8214413430669426,
1.12442581884976,
0.9372256309116047,
0.9277285753608542,
0.796284940893562,
0.6107597532055076]
In [30]:
prediction_proba[:,1]
Out[30]:
array([ 0.06831, 0.05915, 0.01654, ..., 0.03902, 0.0543 , 0.04739])
In [31]:
def make_submission(probs):
sample = pd.read_csv(f'{PATH}//av_sample_submission_n2Tyn0h.csv')
submit = sample.copy()
submit['criticality'] = probs
return submit
In [33]:
submit = make_submission(prediction_proba[:,1])
In [34]:
submit.head(2)
Out[34]:
victim_id
criticality
0
VIC20100001032706
0.068305
1
VIC20120000441519
0.059148
In [35]:
submit.to_csv(PATH + 'av_cat_2.csv', index=False)
In [5]:
sub1 = pd.read_csv(PATH + 'av_cat_.csv')
In [6]:
sub2 = pd.read_csv(PATH + 'av_cat_2.csv')
In [ ]:
sub3 = pd.read_csv(PATH)
In [7]:
sub1.head(1)
Out[7]:
victim_id
criticality
0
VIC20100001032706
0.069681
In [8]:
sub2.head(2)
Out[8]:
victim_id
criticality
0
VIC20100001032706
0.068305
1
VIC20120000441519
0.059148
In [9]:
pred1 = sub1['criticality'];
pred2 = sub2['criticality'];
In [11]:
pred3 = (.4*pred1 + .6*pred2)/2.
In [12]:
sub1['criticality'] = pred3
In [13]:
sub1.to_csv(PATH + 'av_cat_1_2_.csv', index=False)
In [204]:
from sklearn.ensemble import GradientBoostingClassifier #GBM algorithm
from sklearn import cross_validation, metrics #Additional scklearn functions
from sklearn.grid_search import GridSearchCV #Perforing grid search
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
"This module will be removed in 0.20.", DeprecationWarning)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\grid_search.py:42: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
DeprecationWarning)
In [209]:
def modelfit(alg, dtrain, predictors, performCV=True, printFeatureImportance=True, cv_folds=5):
#Fit the algorithm on the data
alg.fit(dtrain[predictors], y)
#Predict training set:
dtrain_predictions = alg.predict(dtrain[predictors])
dtrain_predprob = alg.predict_proba(dtrain[predictors])[:,1]
#Perform cross-validation:
if performCV:
cv_score = cross_validation.cross_val_score(alg, dtrain[predictors], y, cv=cv_folds, scoring='roc_auc')
#Print model report:
print ("\nModel Report")
print ("Accuracy : %.4g" % metrics.accuracy_score(y , dtrain_predictions))
print ("AUC Score (Train): %f" % metrics.roc_auc_score(y , dtrain_predprob))
if performCV:
print ("CV Score : Mean - %.7g | Std - %.7g | Min - %.7g | Max - %.7g" % (np.mean(cv_score),np.std(cv_score),np.min(cv_score),np.max(cv_score)))
#Print Feature Importance:
if printFeatureImportance:
feat_imp = pd.Series(alg.feature_importances_, predictors).sort_values(ascending=False)
plt.figure(figsize=(20,20))
feat_imp.plot(kind='bar', title='Feature Importances')
plt.ylabel('Feature Importance Score')
In [210]:
#Choose all predictors except target & IDcols
predictors = df.columns
gbm0 = GradientBoostingClassifier(random_state=10)
modelfit(gbm0, df, predictors)
Model Report
Accuracy : 0.9603
AUC Score (Train): 0.870501
CV Score : Mean - 0.7950892 | Std - 0.02467951 | Min - 0.7703814 | Max - 0.8424375
In [212]:
param_test1 = {'n_estimators':[20, 30, 40, 50, 60, 70, 80, 90]}
gsearch1 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.05, min_samples_split=500,
min_samples_leaf=50,max_depth=8,max_features='sqrt',subsample=0.8,random_state=10),
param_grid = param_test1, scoring='roc_auc',n_jobs=4,iid=False, cv=5)
gsearch1.fit(df[predictors], y)
Out[212]:
GridSearchCV(cv=5, error_score='raise',
estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,
learning_rate=0.05, loss='deviance', max_depth=8,
max_features='sqrt', max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=50, min_samples_split=500,
min_weight_fraction_leaf=0.0, n_estimators=100,
presort='auto', random_state=10, subsample=0.8, verbose=0,
warm_start=False),
fit_params={}, iid=False, n_jobs=4,
param_grid={'n_estimators': [20, 30, 40, 50, 60, 70, 80, 90]},
pre_dispatch='2*n_jobs', refit=True, scoring='roc_auc', verbose=0)
In [213]:
gsearch1.grid_scores_, gsearch1.best_params_, gsearch1.best_score_
Out[213]:
([mean: 0.76812, std: 0.02706, params: {'n_estimators': 20},
mean: 0.77004, std: 0.02740, params: {'n_estimators': 30},
mean: 0.77381, std: 0.02709, params: {'n_estimators': 40},
mean: 0.77702, std: 0.02671, params: {'n_estimators': 50},
mean: 0.78037, std: 0.02678, params: {'n_estimators': 60},
mean: 0.78425, std: 0.02559, params: {'n_estimators': 70},
mean: 0.78591, std: 0.02616, params: {'n_estimators': 80},
mean: 0.78726, std: 0.02711, params: {'n_estimators': 90}],
{'n_estimators': 90},
0.7872580277711331)
In [215]:
## Test 2
param_test2 = {'max_depth':[5, 7, 9, 11, 13, 15] ,'min_samples_split': [200, 400, 600, 800, 1000]}
gsearch2 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.05, n_estimators=90, max_features='sqrt', subsample=0.8, random_state=10),
param_grid = param_test2, scoring='roc_auc',n_jobs=4,iid=False, cv=5)
In [216]:
gsearch2.fit(df[predictors], y)
gsearch2.grid_scores_, gsearch2.best_params_, gsearch2.best_score_
Out[216]:
([mean: 0.78051, std: 0.02758, params: {'max_depth': 5, 'min_samples_split': 200},
mean: 0.78206, std: 0.02766, params: {'max_depth': 5, 'min_samples_split': 400},
mean: 0.78349, std: 0.02935, params: {'max_depth': 5, 'min_samples_split': 600},
mean: 0.78399, std: 0.03035, params: {'max_depth': 5, 'min_samples_split': 800},
mean: 0.78113, std: 0.02870, params: {'max_depth': 5, 'min_samples_split': 1000},
mean: 0.78132, std: 0.03128, params: {'max_depth': 7, 'min_samples_split': 200},
mean: 0.78252, std: 0.02891, params: {'max_depth': 7, 'min_samples_split': 400},
mean: 0.78477, std: 0.02503, params: {'max_depth': 7, 'min_samples_split': 600},
mean: 0.78607, std: 0.02890, params: {'max_depth': 7, 'min_samples_split': 800},
mean: 0.78567, std: 0.02563, params: {'max_depth': 7, 'min_samples_split': 1000},
mean: 0.78175, std: 0.03052, params: {'max_depth': 9, 'min_samples_split': 200},
mean: 0.78102, std: 0.02997, params: {'max_depth': 9, 'min_samples_split': 400},
mean: 0.77856, std: 0.02791, params: {'max_depth': 9, 'min_samples_split': 600},
mean: 0.78273, std: 0.02939, params: {'max_depth': 9, 'min_samples_split': 800},
mean: 0.78581, std: 0.02958, params: {'max_depth': 9, 'min_samples_split': 1000},
mean: 0.77360, std: 0.02857, params: {'max_depth': 11, 'min_samples_split': 200},
mean: 0.77661, std: 0.02433, params: {'max_depth': 11, 'min_samples_split': 400},
mean: 0.78004, std: 0.02795, params: {'max_depth': 11, 'min_samples_split': 600},
mean: 0.77862, std: 0.02773, params: {'max_depth': 11, 'min_samples_split': 800},
mean: 0.78378, std: 0.02755, params: {'max_depth': 11, 'min_samples_split': 1000},
mean: 0.77162, std: 0.02400, params: {'max_depth': 13, 'min_samples_split': 200},
mean: 0.77339, std: 0.02792, params: {'max_depth': 13, 'min_samples_split': 400},
mean: 0.78074, std: 0.02463, params: {'max_depth': 13, 'min_samples_split': 600},
mean: 0.77830, std: 0.02768, params: {'max_depth': 13, 'min_samples_split': 800},
mean: 0.78393, std: 0.02380, params: {'max_depth': 13, 'min_samples_split': 1000},
mean: 0.77288, std: 0.02501, params: {'max_depth': 15, 'min_samples_split': 200},
mean: 0.77532, std: 0.02042, params: {'max_depth': 15, 'min_samples_split': 400},
mean: 0.77981, std: 0.02797, params: {'max_depth': 15, 'min_samples_split': 600},
mean: 0.78247, std: 0.02535, params: {'max_depth': 15, 'min_samples_split': 800},
mean: 0.78440, std: 0.02115, params: {'max_depth': 15, 'min_samples_split': 1000}],
{'max_depth': 7, 'min_samples_split': 800},
0.7860716441858158)
In [217]:
#test 3
param_test3 = {'min_samples_split': [800, 1000, 1200, 1400, 1600] , 'min_samples_leaf': [30, 40, 50, 60, 70]}
gsearch3 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.05, n_estimators=90,\
max_depth=7,max_features='sqrt', subsample=0.8, random_state=10),
param_grid = param_test3, scoring='roc_auc',n_jobs=4,iid=False, cv=5)
In [ ]:
gsearch3.fit(df[predictors], y)
In [222]:
gsearch3.grid_scores_, gsearch3.best_params_, gsearch3.best_score_
Out[222]:
([mean: 0.78660, std: 0.02750, params: {'min_samples_leaf': 30, 'min_samples_split': 800},
mean: 0.78640, std: 0.02748, params: {'min_samples_leaf': 30, 'min_samples_split': 1000},
mean: 0.78510, std: 0.02794, params: {'min_samples_leaf': 30, 'min_samples_split': 1200},
mean: 0.78463, std: 0.02771, params: {'min_samples_leaf': 30, 'min_samples_split': 1400},
mean: 0.78187, std: 0.02797, params: {'min_samples_leaf': 30, 'min_samples_split': 1600},
mean: 0.78615, std: 0.03011, params: {'min_samples_leaf': 40, 'min_samples_split': 800},
mean: 0.78732, std: 0.02671, params: {'min_samples_leaf': 40, 'min_samples_split': 1000},
mean: 0.78704, std: 0.02895, params: {'min_samples_leaf': 40, 'min_samples_split': 1200},
mean: 0.78528, std: 0.02780, params: {'min_samples_leaf': 40, 'min_samples_split': 1400},
mean: 0.78355, std: 0.02818, params: {'min_samples_leaf': 40, 'min_samples_split': 1600},
mean: 0.78623, std: 0.02916, params: {'min_samples_leaf': 50, 'min_samples_split': 800},
mean: 0.78951, std: 0.02649, params: {'min_samples_leaf': 50, 'min_samples_split': 1000},
mean: 0.78605, std: 0.02696, params: {'min_samples_leaf': 50, 'min_samples_split': 1200},
mean: 0.78511, std: 0.02805, params: {'min_samples_leaf': 50, 'min_samples_split': 1400},
mean: 0.78514, std: 0.02908, params: {'min_samples_leaf': 50, 'min_samples_split': 1600},
mean: 0.78508, std: 0.02920, params: {'min_samples_leaf': 60, 'min_samples_split': 800},
mean: 0.78711, std: 0.02683, params: {'min_samples_leaf': 60, 'min_samples_split': 1000},
mean: 0.78524, std: 0.02867, params: {'min_samples_leaf': 60, 'min_samples_split': 1200},
mean: 0.78563, std: 0.02896, params: {'min_samples_leaf': 60, 'min_samples_split': 1400},
mean: 0.78372, std: 0.03005, params: {'min_samples_leaf': 60, 'min_samples_split': 1600},
mean: 0.78497, std: 0.02866, params: {'min_samples_leaf': 70, 'min_samples_split': 800},
mean: 0.78627, std: 0.02649, params: {'min_samples_leaf': 70, 'min_samples_split': 1000},
mean: 0.78524, std: 0.02805, params: {'min_samples_leaf': 70, 'min_samples_split': 1200},
mean: 0.78399, std: 0.02894, params: {'min_samples_leaf': 70, 'min_samples_split': 1400},
mean: 0.78280, std: 0.02959, params: {'min_samples_leaf': 70, 'min_samples_split': 1600}],
{'min_samples_leaf': 50, 'min_samples_split': 1000},
0.7895124836661931)
In [221]:
modelfit(gsearch3.best_estimator_, df, predictors)
Model Report
Accuracy : 0.9557
AUC Score (Train): 0.857750
CV Score : Mean - 0.7895125 | Std - 0.02649423 | Min - 0.768983 | Max - 0.8403744
In [223]:
#test 4
param_test4 = {'max_features': [7, 9, 11, 13, 15, 17, 19, 21]}
gsearch4 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.05, min_samples_split = 1000, n_estimators=70,max_depth=7,\
max_features='sqrt', subsample=0.8, random_state=10,min_samples_leaf = 50),
param_grid = param_test4, scoring='roc_auc',n_jobs=4,iid=False, cv=5)
In [224]:
gsearch4.fit(df[predictors], y )
gsearch4.grid_scores_, gsearch4.best_params_, gsearch4.best_score_
Out[224]:
([mean: 0.78744, std: 0.02699, params: {'max_features': 7},
mean: 0.78831, std: 0.02603, params: {'max_features': 9},
mean: 0.78990, std: 0.02486, params: {'max_features': 11},
mean: 0.79034, std: 0.02486, params: {'max_features': 13},
mean: 0.79478, std: 0.02473, params: {'max_features': 15},
mean: 0.79731, std: 0.02469, params: {'max_features': 17},
mean: 0.79403, std: 0.02776, params: {'max_features': 19},
mean: 0.79541, std: 0.02483, params: {'max_features': 21}],
{'max_features': 17},
0.7973140508980135)
In [227]:
#test 5
param_test5 = {'subsample':[0.6,0.7,0.75,0.8,0.85,0.9]}
gsearch5 = GridSearchCV(estimator = GradientBoostingClassifier(learning_rate=0.05, min_samples_split = 1000, n_estimators=70,max_depth=7,\
subsample=0.8, \
random_state=10,min_samples_leaf = 50,max_features=17),
param_grid = param_test5, scoring='roc_auc',n_jobs=4,iid=False, cv=5)
In [228]:
gsearch5.fit(df[predictors], y )
gsearch5.grid_scores_, gsearch5.best_params_, gsearch5.best_score_
Out[228]:
([mean: 0.79196, std: 0.02388, params: {'subsample': 0.6},
mean: 0.79809, std: 0.02678, params: {'subsample': 0.7},
mean: 0.79499, std: 0.02698, params: {'subsample': 0.75},
mean: 0.79731, std: 0.02469, params: {'subsample': 0.8},
mean: 0.79579, std: 0.02545, params: {'subsample': 0.85},
mean: 0.79430, std: 0.02638, params: {'subsample': 0.9}],
{'subsample': 0.7},
0.7980930249966234)
In [232]:
gbm_tuned_2 = GradientBoostingClassifier(learning_rate=0.05, min_samples_split = 1000, n_estimators=500,max_depth=10,\
subsample=0.8, random_state=10,min_samples_leaf = 50,max_features=17)
modelfit(gbm_tuned_2, df, predictors)
Model Report
Accuracy : 0.9666
AUC Score (Train): 0.982117
CV Score : Mean - 0.7730332 | Std - 0.02045937 | Min - 0.7509213 | Max - 0.8020239
In [235]:
test.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5048 entries, 0 to 5047
Data columns (total 22 columns):
victim_id 5048 non-null category
incident_time 5048 non-null category
incident_location 5048 non-null category
incident_tehsil 5048 non-null category
cause_of_emergency 5048 non-null category
base_to_scene_distance 5048 non-null int64
scene_to_hospital_distance 5048 non-null int64
roadway_feature 5048 non-null category
road_type 5048 non-null category
surrounding_area 5048 non-null category
incident_Year 5048 non-null int64
incident_Month 5048 non-null int64
incident_Week 5048 non-null int64
incident_Day 5048 non-null int64
incident_Dayofweek 5048 non-null int64
incident_Dayofyear 5048 non-null int64
incident_Is_month_end 5048 non-null bool
incident_Is_month_start 5048 non-null bool
incident_Is_quarter_end 5048 non-null bool
incident_Is_quarter_start 5048 non-null bool
incident_Is_year_end 5048 non-null bool
incident_Is_year_start 5048 non-null bool
dtypes: bool(6), category(8), int64(8)
memory usage: 899.9 KB
In [233]:
prediction_proba_2 = gbm_tuned_2.predict_proba(test)
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-233-5e1979a5407b> in <module>()
----> 1 prediction_proba_2 = gbm_tuned_2.predict_proba(test)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\gradient_boosting.py in predict_proba(self, X)
1577 classes corresponds to that in the attribute `classes_`.
1578 """
-> 1579 score = self.decision_function(X)
1580 try:
1581 return self.loss_._score_to_proba(score)
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\ensemble\gradient_boosting.py in decision_function(self, X)
1484 [n_samples].
1485 """
-> 1486 X = check_array(X, dtype=DTYPE, order="C", accept_sparse='csr')
1487 score = self._decision_function(X)
1488 if score.shape[1] == 1:
C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
431 force_all_finite)
432 else:
--> 433 array = np.array(array, dtype=dtype, order=order, copy=copy)
434
435 if ensure_2d:
ValueError: could not convert string to float: 'AGL'
In [ ]:
submit = make_submission(prediction_proba_2[:,1])
Content source: AdityaSoni19031997/Machine-Learning
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