av_student_fest_remake


imports


In [1]:
%load_ext autoreload
%autoreload 2
%matplotlib inline

In [3]:
from fastai.imports import *
from fastai.structured import *

import time
from gplearn.genetic import SymbolicTransformer
from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from IPython.display import display
import xgboost as xgb
import lightgbm as lgb
from catboost import CatBoostClassifier
import gc
from scipy.cluster import hierarchy as hc
from sklearn import metrics
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics import  roc_auc_score, log_loss
from sklearn.model_selection import StratifiedKFold

def ignore_warn(*args, **kwargs):
    pass
warnings.warn = ignore_warn

#will ignore all warning from sklearn, seaborn etc..

In [4]:
PATH = os.getcwd();
PATH


Out[4]:
'D:\\Github\\fastai\\courses\\ml1'

In [32]:
df_raw  = pd.read_csv(f'{PATH}\\AV_Stud\\train_HK6lq50_encoded_v3_250518.csv', low_memory= False)
df_test = pd.read_csv(f'{PATH}\\AV_Stud\\test_2nAIblo_encoded_v3_250518.csv',  low_memory=False)

init fe


In [5]:
df_raw.head(2)


Out[5]:
id program_id program_type program_duration test_id test_type difficulty_level trainee_id gender education city_tier age total_programs_enrolled is_handicapped trainee_engagement_rating is_pass
0 9389_150 Y_1 Y 136 150 offline intermediate 9389 M Matriculation 3 24.0 5 N 1.0 0
1 16523_44 T_1 T 131 44 offline easy 16523 F High School Diploma 4 26.0 2 N 3.0 1

In [6]:
#dropping id columns
df_raw.drop(['id','trainee_id'], inplace = True, axis =1)
df_test.drop(['id', 'trainee_id'], inplace = True, axis =1);

target = df_raw.is_pass;
df_raw.drop('is_pass', axis =1, inplace= True)

df_raw['age'].fillna(value = 45., inplace=True)
df_raw['trainee_engagement_rating'].fillna(value = 1., inplace=True)

In [34]:
df_raw.head(3)


Out[34]:
program_id program_type program_duration test_id test_type difficulty_level gender education city_tier age total_programs_enrolled is_handicapped trainee_engagement_rating
0 Y_1 Y 136 150 offline intermediate M Matriculation 3 24.0 5 N 1
1 T_1 T 131 44 offline easy F High School Diploma 4 26.0 2 N 3
2 Z_2 Z 120 178 online easy M Matriculation 1 40.0 1 N 2

In [38]:
#############################################################################################################
##########################################TRAIN SET FE'S######################################################
#############################################################################################################

df_raw['program_type__program_duration'] = df_raw.program_type.str.cat(df_raw.program_duration.astype(str),sep='_')
df_raw['program_type__city_tier'] = df_raw.program_type.str.cat(df_raw.city_tier.astype(str),sep='_')
df_raw['program_type__test_type'] = df_raw.program_type.str.cat(df_raw.test_type.astype(str),sep='_')
df_raw['program_type__difficulty_level'] = df_raw.program_type.str.cat(df_raw.difficulty_level.astype(str),sep='_')

df_raw['test_id__program_duration'] = df_raw.test_id.astype(str).str.cat(df_raw.program_duration.astype(str),sep='_')
df_raw['test_id__test_type'] = df_raw.test_id.astype(str).str.cat(df_raw.test_type.astype(str),sep='_')
df_raw['test_id_test_type__difficulty_level'] = df_raw.test_id__test_type.str.cat(df_raw.difficulty_level.astype(str),sep='_')
df_raw['test_type__difficulty_level'] = df_raw.test_type.str.cat(df_raw.difficulty_level.astype(str),sep='_')

df_raw['education__gender'] = df_raw.education.str.cat(df_raw.gender.astype(str),sep='_')
df_raw['education__total_programs_enrolled'] = df_raw.education.str.cat(df_raw.total_programs_enrolled.astype(str),sep='_')
df_raw['gender__city_tier'] = df_raw.gender.str.cat(df_raw.city_tier.astype(str),sep='_')
df_raw['gender__is_handicapped'] = df_raw.gender.str.cat(df_raw.is_handicapped.astype(str),sep='_')
df_raw['education__city_tier'] = df_raw.education.str.cat(df_raw.city_tier.astype(str),sep='_')

df_raw['program_duration_months'] = df_raw['program_duration'] / (7.)
df_raw['program_duration_years'] = df_raw['program_duration'] / (365.)
df_raw['program_duration_avg'] = df_raw['program_duration']/df_raw['total_programs_enrolled']

df_raw['is_age_39'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('age<=39.').index
df_raw.iloc[my_query, -1] = 1

df_raw['is_age_39_45'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('age>=39. & age<=45.').index
df_raw.iloc[my_query, -1] = 1

df_raw['is_age_45'] = np.zeros(df_raw.shape[0])
my_query = df_raw.query('age>=45.').index
df_raw.iloc[my_query, -1] = 1

###################young age (13–30), middle age (31–50) and senior age (51–70)########################

df_raw['age_group'] = np.zeros(df_raw.shape[0])

my_query = df_raw.query('age>=13. & age<=30.').index
df_raw.iloc[my_query, -1] = 'young'

my_query = df_raw.query('age>=31. & age<=50.').index
df_raw.iloc[my_query, -1] = 'middle_aged'

my_query = df_raw.query('age>=51. & age<=70.').index
df_raw.iloc[my_query, -1] = 'senior_aged'

df_raw['program_level'] = df_raw['program_id'].str.split(pat='_', expand=True).get(1).astype(object)

#df_raw['is_pass'] = target
#df_raw.drop('is_pass', axis = 1, inplace = True);

#############################################################################################################
##########################################TEST SET FE'S######################################################
#############################################################################################################

df_test['program_type__program_duration'] = df_test.program_type.str.cat(df_test.program_duration.astype(str),sep='_')
df_test['program_type__city_tier'] = df_test.program_type.str.cat(df_test.city_tier.astype(str),sep='_')
df_test['program_type__test_type'] = df_test.program_type.str.cat(df_test.test_type.astype(str),sep='_')
df_test['program_type__difficulty_level'] = df_test.program_type.str.cat(df_test.difficulty_level.astype(str),sep='_')

df_test['test_id__program_duration'] = df_test.test_id.astype(str).str.cat(df_test.program_duration.astype(str),sep='_')
df_test['test_id__test_type'] = df_test.test_id.astype(str).str.cat(df_test.test_type.astype(str),sep='_')
df_test['test_id_test_type__difficulty_level'] = df_test.test_id__test_type.str.cat(df_test.difficulty_level.astype(str),sep='_')
df_test['test_type__difficulty_level'] = df_test.test_type.str.cat(df_test.difficulty_level.astype(str),sep='_')

df_test['education__gender'] = df_test.education.str.cat(df_test.gender.astype(str),sep='_')
df_test['education__total_programs_enrolled'] = df_test.education.str.cat(df_test.total_programs_enrolled.astype(str),sep='_')
df_test['gender__city_tier'] = df_test.gender.str.cat(df_test.city_tier.astype(str),sep='_')
df_test['gender__is_handicapped'] = df_test.gender.str.cat(df_test.is_handicapped.astype(str),sep='_')
df_test['education__city_tier'] = df_test.education.str.cat(df_test.city_tier.astype(str),sep='_')

df_test['program_duration_months'] = df_test['program_duration'] / (7.)
df_test['program_duration_years'] = df_test['program_duration'] / (365.)
df_test['program_duration_avg'] = df_test['program_duration']/df_test['total_programs_enrolled']

df_test['is_age_39'] = np.zeros(df_test.shape[0])
my_query = df_test.query('age<=39.').index
df_test.iloc[my_query, -1] = 1

df_test['is_age_39_45'] = np.zeros(df_test.shape[0])
my_query = df_test.query('age>=39. & age<=45.').index
df_test.iloc[my_query, -1] = 1

df_test['is_age_45'] = np.zeros(df_test.shape[0])
my_query = df_test.query('age>=45.').index
df_test.iloc[my_query, -1] = 1

###################young age (13–30), middle age (31–50) and senior age (51–70)########################

df_test['age_group'] = np.zeros(df_test.shape[0])

my_query = df_test.query('age>=13. & age<=30.').index
df_test.iloc[my_query, -1] = 'young'

my_query = df_test.query('age>=31. & age<=50.').index
df_test.iloc[my_query, -1] = 'middle_aged'

my_query = df_test.query('age>=51. & age<=70.').index
df_test.iloc[my_query, -1] = 'senior_aged'

df_test['program_level'] = df_test['program_id'].str.split(pat='_', expand=True).get(1).astype(object)

########################################### Dropping few cols ##############################################3
df_raw.drop('program_id', inplace=True, axis =1)
df_test.drop('program_id', inplace=True, axis =1)

In [83]:
##################### sanity check  should be empty #####################
set(df_raw.columns) - set(df_test.columns)


Out[83]:
set()

In [84]:
# This way we have randomness and are able to reproduce the behaviour within this cell.
np.random.seed(13)
from sklearn.model_selection import KFold

def impact_coding(data, feature, target='y'):
    '''
    In this implementation we get the values and the dictionary as two different steps.
    This is just because initially we were ignoring the dictionary as a result variable.
    
    In this implementation the KFolds use shuffling. If you want reproducibility the cv 
    could be moved to a parameter.
    '''
    n_folds = 10
    n_inner_folds = 7
    impact_coded = pd.Series()
    
    oof_default_mean = data[target].mean() # Gobal mean to use by default (you could further tune this)
    kf = KFold(n_splits=n_folds, shuffle=True)
    oof_mean_cv = pd.DataFrame()
    split = 0
    for infold, oof in kf.split(data[feature]):
            impact_coded_cv = pd.Series()
            kf_inner = KFold(n_splits=n_inner_folds, shuffle=True)
            inner_split = 0
            inner_oof_mean_cv = pd.DataFrame()
            oof_default_inner_mean = data.iloc[infold][target].mean()
            for infold_inner, oof_inner in kf_inner.split(data.iloc[infold]):
                # The mean to apply to the inner oof split (a 1/n_folds % based on the rest)
                oof_mean = data.iloc[infold_inner].groupby(by=feature)[target].mean()
                impact_coded_cv = impact_coded_cv.append(data.iloc[infold].apply(
                            lambda x: oof_mean[x[feature]]
                                      if x[feature] in oof_mean.index
                                      else oof_default_inner_mean
                            , axis=1))

                # Also populate mapping (this has all group -> mean for all inner CV folds)
                inner_oof_mean_cv = inner_oof_mean_cv.join(pd.DataFrame(oof_mean), rsuffix=inner_split, how='outer')
                inner_oof_mean_cv.fillna(value=oof_default_inner_mean, inplace=True)
                inner_split += 1

            # Also populate mapping
            oof_mean_cv = oof_mean_cv.join(pd.DataFrame(inner_oof_mean_cv), rsuffix=split, how='outer')
            oof_mean_cv.fillna(value=oof_default_mean, inplace=True)
            split += 1
            
            impact_coded = impact_coded.append(data.iloc[oof].apply(
                            lambda x: inner_oof_mean_cv.loc[x[feature]].mean()
                                      if x[feature] in inner_oof_mean_cv.index
                                      else oof_default_mean
                            , axis=1))

    return impact_coded, oof_mean_cv.mean(axis=1), oof_default_mean

In [85]:
features = df_raw.columns
numeric_features = []
categorical_features = []

for dtype, feature in zip(df_raw.dtypes, df_raw.columns):
    if dtype == object:
        categorical_features.append(feature)
    else:
        numeric_features.append(feature)
categorical_features


Out[85]:
['program_type',
 'test_type',
 'difficulty_level',
 'gender',
 'education',
 'is_handicapped',
 'trainee_engagement_rating',
 'program_type__program_duration',
 'program_type__city_tier',
 'program_type__test_type',
 'program_type__difficulty_level',
 'test_id__program_duration',
 'test_id__test_type',
 'test_id_test_type__difficulty_level',
 'test_type__difficulty_level',
 'education__gender',
 'education__total_programs_enrolled',
 'gender__city_tier',
 'gender__is_handicapped',
 'education__city_tier',
 'age_group',
 'program_level']

In [86]:
%%time
# Apply the encoding to training and test data, and preserve the mapping
df_raw['is_pass'] = target
impact_coding_map = {}
for f in categorical_features:
    print("Impact coding for {}".format(f))
    df_raw["impact_encoded_{}".format(f)], impact_coding_mapping, default_coding = impact_coding(df_raw, f,'is_pass')
    impact_coding_map[f] = (impact_coding_mapping, default_coding)
    mapping, default_mean = impact_coding_map[f]
    df_test["impact_encoded_{}".format(f)] = df_test.apply(lambda x: mapping[x[f]]
                                                                         if x[f] in mapping
                                                                         else default_mean
                                                               , axis=1)
df_raw.drop('is_pass', inplace=True, axis=1)


Impact coding for program_type
Impact coding for test_type
Impact coding for difficulty_level
Impact coding for gender
Impact coding for education
Impact coding for is_handicapped
Impact coding for trainee_engagement_rating
Impact coding for program_type__program_duration
Impact coding for program_type__city_tier
Impact coding for program_type__test_type
Impact coding for program_type__difficulty_level
Impact coding for test_id__program_duration
Impact coding for test_id__test_type
Impact coding for test_id_test_type__difficulty_level
Impact coding for test_type__difficulty_level
Impact coding for education__gender
Impact coding for education__total_programs_enrolled
Impact coding for gender__city_tier
Impact coding for gender__is_handicapped
Impact coding for education__city_tier
Impact coding for age_group
Impact coding for program_level
Wall time: 54min 3s

In [89]:
df_raw['is_pass'] = target

In [90]:
df_raw.to_csv(f'{PATH}\\AV_Stud\\train_HK6lq50_encoded_v3_250518.csv',index=False)
df_test.to_csv(f'{PATH}\\AV_Stud\\test_2nAIblo_encoded_v3_250518.csv',index=False)

modelling part


In [92]:
categorical_features_indices = np.where(df_raw.dtypes == 'object')[0];
df_raw.drop('is_pass',axis=1,inplace=True);
categorical_features_indices


Out[92]:
array([ 0,  3,  4,  5,  6, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 31, 32], dtype=int64)

In [94]:
X_train, X_validation, y_train, y_validation = train_test_split(df_raw, target, train_size=0.8, random_state=1234, shuffle=True)

model=CatBoostClassifier(iterations=1000, depth=12, learning_rate=0.01, loss_function='Logloss',use_best_model=True,\
                class_weights = [0.3045921227117995, 0.6954078772882005 ])

model.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_validation, y_validation));


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996:	learn: 0.4266650	test: 0.4263271	best: 0.4263256 (575)	total: 2m 35s	remaining: 467ms
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999:	learn: 0.4266650	test: 0.4263271	best: 0.4263256 (575)	total: 2m 35s	remaining: 0us

bestTest = 0.4263255838
bestIteration = 575

Shrink model to first 576 iterations.

In [106]:
prediction_proba = model.predict_proba(df_test)

In [22]:
gc.collect()


Out[22]:
4388

In [7]:
def make_submission(probs):
    sample = pd.read_csv(f'{PATH}\\AV_Stud\\sample_submission_vaSxamm.csv')
    submit = sample.copy()
    submit['is_pass'] = probs
    return submit

In [74]:
#submit = make_submission(prediction_proba[:,1]);
#submit = make_submission(preds_xgb)
submit = make_submission(new_preds)

In [75]:
submit.head(2)


Out[75]:
id is_pass
0 1626_45 0.491504
1 11020_130 0.830501

In [76]:
submit.to_csv(f'{PATH}\\AV_Stud\\nn2.csv', index=False)

xgb


In [33]:
target = df_raw['is_pass'];
df_raw.drop(['is_pass'], axis =1, inplace =True)

'''df_raw.drop(['is_pass','program_id', 'program_type', 'test_type', 'difficulty_level', 'gender',
       'education', 'is_handicapped'], inplace =True, axis =1)

df_test.drop(['program_id', 'program_type', 'test_type', 'difficulty_level', 'gender',
       'education', 'is_handicapped'], inplace =True, axis =1)
'''

cv = StratifiedKFold(n_splits=7, shuffle=True, random_state=1337)
folds = list(cv.split(df_raw, target))
x_trn, x_val, y_trn, y_val = train_test_split(df_raw, target, test_size=0.2, random_state=42, shuffle= True)
#sanity check
set(df_raw.columns) - set(df_test.columns)


Out[33]:
set()

In [7]:
def cross_val_xgb(params, X, y, folds):
    n = 1
    num_rounds = 3000
    
    list_rounds = []
    list_scores = []
    
    for train_idx, valid_idx in folds:
        print('#################################')
        print('#########  Validating for fold:', n)

        xgtrain = xgb.DMatrix(X[train_idx], label=y[train_idx])
        xgtest = xgb.DMatrix(X[valid_idx], label=y[valid_idx])

        watchlist = [ (xgtest, 'test') ]
        model = xgb.train(params, xgtrain, num_rounds, watchlist, early_stopping_rounds=50, verbose_eval=True)
        
        rounds = model.best_ntree_limit
        score = model.best_score
        
        print('\nFold', n,'- best round:', rounds)
        print('Fold', n,'- best score:', score)
        
        list_rounds.append(rounds)
        list_scores.append(score)
        n +=1
    
    mean_score = np.mean(list_scores)
    std_score = np.std(list_scores)
    mean_round = np.mean(list_rounds)
    std_round = np.std(list_rounds)
    
    print('End cross validating',n-1,'folds') #otherwise it displays 6 folds
    print("Cross Validation Scores are: ", np.round(list_scores,3))
    print("Mean CrossVal score is: ", round(mean_score,3))
    print("Std Dev CrossVal score is: ", round(std_score,3))
    print("Cross Validation early stopping rounds are: ", np.round(list_rounds,3))
    print("Mean early stopping round is: ", round(mean_round,3))
    print("Std Dev early stopping round is: ", round(std_round,3))
    
    return mean_round, model_cv

In [8]:
def runXGB(train_X, train_y, test_X, test_y=None, seed_val=1, depth = 10):
    
        params = {}
        params['booster'] = 'gbtree'
        #params['updater'] = 'coord_descent'
        params["objective"] = "binary:logistic"
        params['eval_metric'] = 'auc'
        params["eta"] = 0.05 #0.00334
        params["subsample"] = .9
        params["silent"] = 0
        params['verbose'] = 2
        params["max_depth"] = depth
        params["seed"] = seed_val
        params["max_delta_step"] = 4
        params['scale_pos_weight'] =  0.4380049934141978
        #params['alpha'] = 0.05
        params["gamma"] = 0.3
        params['colsample_bytree'] = 0.9
        num_rounds = 2000 #3600

        plst = list(params.items())
        xgtrain = xgb.DMatrix(train_X, label=train_y)
        
        if test_y is not None:
                print('1st block\n')
                xgtest = xgb.DMatrix(test_X, label=test_y)
                watchlist = [ (xgtrain,'train'), (xgtest, 'test') ]
                model = xgb.train(plst, xgtrain, num_rounds, watchlist, early_stopping_rounds= 50,verbose_eval=True)
        else:
                print('2nd block\n')
                xgtest = xgb.DMatrix(test_X)
                #watchlist = [ (xgtrain,'train')]
                #cv_results = xgb.cv(plst, xgtrain, num_rounds, nfold=5, stratified=True, show_stdv=False,\
                #               verbose_eval=True, folds=7, metrics=['auc', 'logloss'], early_stopping_rounds=50)
                #print('########################### model ######################\n', model)
                model = xgb.train(plst, xgtrain, num_rounds)

        pred_test_y = model.predict(xgtest,ntree_limit=model.best_ntree_limit)
        
        return pred_test_y, model, plst, model.best_ntree_limit

In [34]:
cols_not_to_be_encoded = ['program_type__program_duration','program_duration','test_id',
 'program_type__city_tier',
 'program_type__test_type',
 'program_type__difficulty_level',
 'test_id__program_duration',
 'test_id__test_type',
 'test_id_test_type__difficulty_level',
 'test_type__difficulty_level',
 'education__gender',
 'education__total_programs_enrolled',
 'gender__city_tier',
 'gender__is_handicapped',
 'education__city_tier',
 'program_level']

In [35]:
encoded_cols = ['program_type', 'test_type',
       'difficulty_level', 'gender', 'education',
       'total_programs_enrolled', 'is_handicapped',
       'trainee_engagement_rating','age_group']
df_raw = pd.get_dummies(df_raw, drop_first=True, prefix = 'one_hot', columns=encoded_cols);
df_test = pd.get_dummies(df_test, drop_first=True, prefix = 'one_hot',  columns=encoded_cols);

In [36]:
df_raw.drop(cols_not_to_be_encoded, inplace=True, axis=1);
df_test.drop(cols_not_to_be_encoded, inplace=True, axis=1);
df_test.drop('one_hot_middle_aged', axis =1, inplace=True);

In [20]:
%%time
preds_xgb, model, params, num_rounds = runXGB(df_raw, target, df_test)


2nd block

Wall time: 8min 28s

In [21]:
xgb.plot_importance(model, max_num_features=15, importance_type='weight');



In [37]:
len(df_test.columns), len(np.unique(df_test.columns))


Out[37]:
(64, 63)

In [38]:
df_test.columns.value_counts().sort_values(ascending=False)[:2]


Out[38]:
one_hot_Y      2
one_hot_2.0    1
dtype: int64

In [39]:
len(df_raw.columns), len(np.unique(df_raw.columns))


Out[39]:
(64, 63)

In [40]:
set(df_raw.columns) - set(df_test.columns)


Out[40]:
set()

In [41]:
set(df_test.columns) - set(df_raw.columns)


Out[41]:
set()

In [42]:
df_test.drop(['one_hot_Y'], axis =1, inplace=True)

In [43]:
df_raw.drop(['one_hot_Y','one_hot_60.0'], axis =1, inplace=True)

nn


In [8]:
from keras.models import Sequential
from keras.layers import Dense, BatchNormalization, Flatten, LeakyReLU, Dropout
from keras.activations import relu, softmax
from keras import metrics


Using TensorFlow backend.

In [9]:
model = Sequential()

In [10]:
df_raw.shape


Out[10]:
(73147, 56)

In [64]:
def build_model():
    model = Sequential()
    model.add(Dense(256, input_dim = df_raw.shape[1], activation = 'relu', kernel_initializer='normal'))
    model.add(Dense(128, activation = 'relu',kernel_initializer='normal'))
    model.add(BatchNormalization())
    model.add(Dense(64, activation = 'relu',kernel_initializer='normal'))
    model.add(Dropout(0.13))
    model.add(Dense(32,activation = 'relu',kernel_initializer='normal'))
    model.add(Dropout(0.1))
    model.add(Dense(16,activation = 'relu',kernel_initializer='normal'))
    model.add(Dropout(0.1))
    model.add(Dense(1,kernel_initializer='normal',activation = 'sigmoid'))
    model.compile(loss='binary_crossentropy',optimizer='rmsprop',metrics=['accuracy'],)
    return model

In [65]:
model = build_model()

In [66]:
model.summary()


_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_13 (Dense)             (None, 256)               16128     
_________________________________________________________________
dense_14 (Dense)             (None, 128)               32896     
_________________________________________________________________
batch_normalization_1 (Batch (None, 128)               512       
_________________________________________________________________
dense_15 (Dense)             (None, 64)                8256      
_________________________________________________________________
dropout_9 (Dropout)          (None, 64)                0         
_________________________________________________________________
dense_16 (Dense)             (None, 32)                2080      
_________________________________________________________________
dropout_10 (Dropout)         (None, 32)                0         
_________________________________________________________________
dense_17 (Dense)             (None, 16)                528       
_________________________________________________________________
dropout_11 (Dropout)         (None, 16)                0         
_________________________________________________________________
dense_18 (Dense)             (None, 1)                 17        
=================================================================
Total params: 60,417
Trainable params: 60,161
Non-trainable params: 256
_________________________________________________________________

In [53]:
df_test.fillna(36.,inplace=True, axis =1)

In [55]:
scaler = StandardScaler()
X_train = scaler.fit_transform(df_raw)
X_test = scaler.transform(df_test)

In [68]:
hist = model.fit(X_train,target,epochs = 25,batch_size = 32,verbose=2,validation_split=.2)


Train on 58517 samples, validate on 14630 samples
Epoch 1/25
18s - loss: 0.4988 - acc: 0.7604 - val_loss: 0.5807 - val_acc: 0.7286
Epoch 2/25
19s - loss: 0.4991 - acc: 0.7606 - val_loss: 0.5582 - val_acc: 0.7284
Epoch 3/25
20s - loss: 0.4982 - acc: 0.7599 - val_loss: 0.5757 - val_acc: 0.7273
Epoch 4/25
19s - loss: 0.4987 - acc: 0.7621 - val_loss: 0.5838 - val_acc: 0.7242
Epoch 5/25
18s - loss: 0.4977 - acc: 0.7615 - val_loss: 0.5657 - val_acc: 0.7295
Epoch 6/25
17s - loss: 0.4982 - acc: 0.7606 - val_loss: 0.6031 - val_acc: 0.7240
Epoch 7/25
17s - loss: 0.4963 - acc: 0.7625 - val_loss: 0.5677 - val_acc: 0.7267
Epoch 8/25
22s - loss: 0.4948 - acc: 0.7633 - val_loss: 0.5718 - val_acc: 0.7245
Epoch 9/25
17s - loss: 0.4953 - acc: 0.7640 - val_loss: 0.5769 - val_acc: 0.7282
Epoch 10/25
18s - loss: 0.4948 - acc: 0.7629 - val_loss: 0.5622 - val_acc: 0.7298
Epoch 11/25
17s - loss: 0.4929 - acc: 0.7660 - val_loss: 0.6058 - val_acc: 0.7267
Epoch 12/25
17s - loss: 0.4935 - acc: 0.7634 - val_loss: 0.5789 - val_acc: 0.7279
Epoch 13/25
26s - loss: 0.4912 - acc: 0.7660 - val_loss: 0.6050 - val_acc: 0.7308
Epoch 14/25
17s - loss: 0.4894 - acc: 0.7673 - val_loss: 0.6184 - val_acc: 0.7167
Epoch 15/25
18s - loss: 0.4917 - acc: 0.7669 - val_loss: 0.5823 - val_acc: 0.7257
Epoch 16/25
28s - loss: 0.4896 - acc: 0.7668 - val_loss: 0.5870 - val_acc: 0.7214
Epoch 17/25
18s - loss: 0.4914 - acc: 0.7642 - val_loss: 0.5996 - val_acc: 0.7274
Epoch 18/25
17s - loss: 0.4901 - acc: 0.7663 - val_loss: 0.6038 - val_acc: 0.7290
Epoch 19/25
22s - loss: 0.4882 - acc: 0.7665 - val_loss: 0.6187 - val_acc: 0.7264
Epoch 20/25
17s - loss: 0.4883 - acc: 0.7685 - val_loss: 0.5952 - val_acc: 0.7306
Epoch 21/25
17s - loss: 0.4882 - acc: 0.7676 - val_loss: 0.6034 - val_acc: 0.7271
Epoch 22/25
30s - loss: 0.4903 - acc: 0.7668 - val_loss: 0.6088 - val_acc: 0.7261
Epoch 23/25
24s - loss: 0.4912 - acc: 0.7661 - val_loss: 0.6092 - val_acc: 0.7137
Epoch 24/25
17s - loss: 0.4906 - acc: 0.7683 - val_loss: 0.5907 - val_acc: 0.7267
Epoch 25/25
21s - loss: 0.4901 - acc: 0.7678 - val_loss: 0.5906 - val_acc: 0.7255

In [69]:
preds = model.predict_proba(X_test)


31349/31349 [==============================] - ETA: 61 - ETA: 6 - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - ETA:  - 2s     

In [70]:
# serialize model to JSON
model_json = model.to_json()
with open(f'{PATH}\\AV_Stud\\model.json', "w") as json_file:
    json_file.write(model_json)
# serialize weights to HDF5
model.save_weights(f"{PATH}\\AV_Stud\\model.h5")
print("Saved model to disk")


Saved model to disk

load json and create model json_file = open('model.json', 'r')

loaded_model_json = json_file.read()

json_file.close()

loaded_model = model_from_json(loaded_model_json)

load weights into new model

loaded_model.load_weights("model.h5")

print("Loaded model from disk")

evaluate loaded model on test data

loaded_model.compile(loss='binary_crossentropy', optimizer='rmsprop', metrics=['accuracy'])

score = loaded_model.evaluate(X, Y, verbose=0)

print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))


In [71]:
preds[12]


Out[71]:
array([ 0.87713], dtype=float32)

In [72]:
new_preds = []

In [73]:
for i in range(df_test.shape[0]):
    new_preds.append(preds[i][0])

In [ ]: