av_rookree



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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996:	learn: 0.1152458	test: 0.1534024	best: 0.1533056 (977)	total: 15m 50s	remaining: 2.86s
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998:	learn: 0.1150877	test: 0.1534009	best: 0.1533056 (977)	total: 15m 56s	remaining: 958ms
999:	learn: 0.1150566	test: 0.1534112	best: 0.1533056 (977)	total: 15m 59s	remaining: 0us

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)

gridcv


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])