kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
10,029,649 | train['First_Man'] = train.groupby('groupId')['matchDuration'].transform('min')
test['First_Man'] = test.groupby('groupId')['matchDuration'].transform('min' )<categorify> | dataset1.isnull().sum() | Titanic - Machine Learning from Disaster |
10,029,649 | train['Last_Man'] = train.groupby('groupId')['matchDuration'].transform('max')
test['Last_Man'] = test.groupby('groupId')['matchDuration'].transform('max' )<feature_engineering> | dataset1['Age'] = dataset['Age'].fillna(round(np.mean(dataset['Age'])) ) | Titanic - Machine Learning from Disaster |
10,029,649 | train['Time_Survival'] = train['Last_Man'] - train['First_Man']
test['Time_Survival'] = test['Last_Man'] - test['First_Man']<feature_engineering> | dataset1.isnull().sum() | Titanic - Machine Learning from Disaster |
10,029,649 | train['Kill_Percentile'] = train['killPlace'] /(train['maxPlace'] + 1e-9)
test['Kill_Percentile'] = test['killPlace'] /(test['maxPlace'] + 1e-9 )<drop_column> | for i in dataset1.keys() :
print('the unique values for {} is {}'.format(i , len(dataset1[i].unique())) ) | Titanic - Machine Learning from Disaster |
10,029,649 | train.drop(["matchId","groupId",'Id','killPoints', 'maxPlace', 'winPoints','vehicleDestroys'],axis=1,inplace=True)
test.drop(["matchId","groupId",'Id','killPoints', 'maxPlace', 'winPoints','vehicleDestroys'],axis=1,inplace=True )<feature_engineering> | dataset1.groupby('Survived')['Survived'].agg('count' ) | Titanic - Machine Learning from Disaster |
10,029,649 | train['Headshot_rate'] = train['kills'] /(train['headshotKills'] + 1e-9)
test['Headshot_rate'] = test['kills'] /(test['headshotKills'] + 1e-9)
train['KillStreak_rate'] = train['killStreaks'] /(train['kills'] + 1e-9)
test['KillStreak_rate'] = test['killStreaks'] /(test['kills'] + 1e-9 )<feature_engineering> | dataset1[dataset1['Sex'] == 'male'].groupby('Survived')['Survived'].agg('count' ) | Titanic - Machine Learning from Disaster |
10,029,649 | train['Total_Damage'] = train['damageDealt'] + train['teamKills']*100
test['Total_Damage'] = test['damageDealt'] + test['teamKills']*100<feature_engineering> | print('Total female survivors: {}'.format(dataset1[dataset1['Sex'] == 'female'].groupby('Survived')['Survived'].agg('count')[1]))
print('Total male survivors: {}'.format(dataset1[dataset1['Sex'] == 'male'].groupby('Survived')['Survived'].agg('count')[1]))
print('Total survivors: {}'.format(dataset1.groupby('Survived')[... | Titanic - Machine Learning from Disaster |
10,029,649 | train['New']=(train['matchDuration'] < train['matchDuration'].mean())
test['New']=(test['matchDuration'] < test['matchDuration'].mean() )<feature_engineering> | dataset2 = dataset1.copy()
data_pop = dataset2.pop('Ticket' ) | Titanic - Machine Learning from Disaster |
10,029,649 | train['ProKiller']=(train['headshotKills']/train['kills']+1e-9)
test['ProKiller']=(test['headshotKills']/test['kills']+1e-9 )<feature_engineering> | dataset2['Name'] = dataset2['Name'].apply(lambda x: x.split(',')[0] ) | Titanic - Machine Learning from Disaster |
10,029,649 | train['Is_Sniper']=(train['longestKill']>=250)
test['Is_Sniper']=(test['longestKill']>=250 )<feature_engineering> | dataset2['Embarked'] = dataset['Embarked'] | Titanic - Machine Learning from Disaster |
10,029,649 | train['killsOverWalkDistance'] = train['kills'] /(train['walkDistance'] + 1e-9)
test['killsOverWalkDistance'] = test['kills'] /(test['walkDistance'] + 1e-9 )<feature_engineering> | dataset2['Embarked'] = dataset2['Embarked'].fillna('S' ) | Titanic - Machine Learning from Disaster |
10,029,649 | train['Total_Distance'] =(train['rideDistance']+train['swimDistance']+train['walkDistance'])
test['Total_Distance'] =(test['rideDistance']+test['swimDistance']+test['walkDistance'] )<feature_engineering> | dataset2.isnull().sum() | Titanic - Machine Learning from Disaster |
10,029,649 | train['killsOverDistance'] = train['kills'] /(train['distance'] + 1e-9)
test['killsOverDistance'] = test['kills'] /(test['distance'] + 1e-9 )<feature_engineering> | dataset2.groupby('Embarked')['Embarked'].agg('count' ) | Titanic - Machine Learning from Disaster |
10,029,649 | train['Total_enemies'] = train['playersInMatch'] - train['playersInGroup']
test['Total_enemies'] = test['playersInMatch'] - test['playersInGroup']<feature_engineering> | female_survival_ratio = dataset2[dataset2['Sex'] == 'female'].groupby('Survived')['Survived'].agg('count')[1]/dataset2.groupby('Sex')['Sex'].agg('count')['female'] | Titanic - Machine Learning from Disaster |
10,029,649 | train['Team_Spirit'] = train['heals'] + train['revives'] + train['boosts']
test['Team_Spirit'] = test['heals'] + test['revives'] + test['boosts']<define_variables> | male_survival_ratio = dataset2[dataset2['Sex'] == 'male'].groupby('Survived')['Survived'].agg('count')[1]/dataset2.groupby('Sex')['Sex'].agg('count')['male'] | Titanic - Machine Learning from Disaster |
10,029,649 | set1=set(i for i in train[(train['kills']>40)&(train['heals']==0)].index.tolist())
set2=set(i for i in train[(train['distance']==0)&(train['kills']>20)].index.tolist())
set3=set(i for i in train[(train['damageDealt']>4000)&(train['heals']<2)].index.tolist())
set4=set(i for i in train[(train['rideDistance']>25000)].i... | print('the male survival ratio is :{}'.format(male_survival_ratio))
print('the female survival ratio is :{}'.format(female_survival_ratio)) | Titanic - Machine Learning from Disaster |
10,029,649 | train=train.drop(list(sets))
y_train=y_train.drop(list(sets))<feature_engineering> | passenger_survived =(dataset2['PassengerId'][dataset2['Survived'] == 1])-1
passenger_not_survived =(dataset2['PassengerId'][dataset2['Survived'] == 0])-1 | Titanic - Machine Learning from Disaster |
10,029,649 | fpp=['crashfpp','duo-fpp','flare-fpp','normal-duo-fpp','normal-solo-fpp','normal-squad-fpp','solo-fpp','squad-fpp']
train["fpp"] = np.where(train["matchType"].isin(fpp),1,0)
test["fpp"] = np.where(test["matchType"].isin(fpp),1,0 )<define_variables> | dataset3 = dataset2.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | change={'crashfpp':'crash',
'crashtpp':'crash',
'duo':'duo',
'duo-fpp':'duo',
'flarefpp':'flare',
'flaretpp':'flare',
'normal-duo':'duo',
'normal-duo-fpp':'duo',
'normal-solo':'solo',
'normal-solo-fpp':'solo',
'normal-squad':'squad',
'normal-squad-fpp':'squad',
'solo-fpp':'solo',
'squad-fpp':'squad',
'solo':'solo',
'sq... | dataset3 = dataset3.drop(['Name' , 'Sex' , 'Embarked'] , axis=1 ) | Titanic - Machine Learning from Disaster |
10,029,649 | modes={'crash':1,
'duo':2,
'flare':3,
'solo':4,
'squad':5
}
train['matchType']=train['matchType'].map(modes)
test['matchType']=test['matchType'].map(modes )<categorify> | dataset4 = dataset2.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | d1=pd.get_dummies(train['matchType'])
train=train.drop(['matchType'],axis=1)
train=train.join(d1)
d2=pd.get_dummies(test['matchType'])
test=test.drop(['matchType'],axis=1)
test=test.join(d2 )<normalization> | features = ["Sex"] | Titanic - Machine Learning from Disaster |
10,029,649 | scaler = MinMaxScaler()
scaler.fit(train)
train=scaler.transform(train)
test=scaler.transform(test )<split> | dataset5 = pd.get_dummies(dataset[features] ) | Titanic - Machine Learning from Disaster |
10,029,649 | X_train,X_test,y_train,y_test= train_test_split(train,y_train,test_size=0.3 )<define_variables> | dataset4['Sex_female'] = dataset5['Sex_female']
dataset4['Sex_male'] = dataset5['Sex_male'] | Titanic - Machine Learning from Disaster |
10,029,649 | train_pool = Pool(X_train, y_train)
test_pool = Pool(X_test, y_test )<choose_model_class> | dataset6 = dataset4.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | model = CatBoostRegressor(
iterations=5000,
depth=10,
learning_rate=0.1,
l2_leaf_reg= 2,
loss_function='RMSE',
eval_metric='MAE',
random_strength=0.1,
bootstrap_type='Bernoulli',
leaf_estimation_method='Gradient',
leaf_estimation_iterations=1,
boosting_type='Plain'
,task_type = "GPU"
,feature_border_type='GreedyLogSum... | dataset6.pop('Sex')
dataset6.pop('Name' ) | Titanic - Machine Learning from Disaster |
10,029,649 | model.fit(train_pool, eval_set=test_pool, plot=True )<compute_test_metric> | dataset7 = pd.get_dummies(dataset2['Embarked'] ) | Titanic - Machine Learning from Disaster |
10,029,649 | train_mse =(mean_absolute_error(y_train, model.predict(X_train)))
test_mse =(mean_absolute_error(y_test, model.predict(X_test)))
print('Train error= ',train_mse)
print('Test error= ',test_mse)
<save_to_csv> | dataset6['C'] = dataset7['C']
dataset6['Q'] = dataset7['Q']
dataset6['S'] = dataset7['S'] | Titanic - Machine Learning from Disaster |
10,029,649 | subm = pd.read_csv('.. /input/sample_submission_V2.csv')
predictions = model.predict(test)
test = pd.read_csv('.. /input/test_V2.csv')
test['winPlacePerc'] = predictions
test['winPlacePerc'] = test.groupby('groupId')['winPlacePerc'].transform('median')
subm['winPlacePerc'] = test['winPlacePerc']
subm['Id']=ID
subm.... | dataset6.pop('Embarked' ) | Titanic - Machine Learning from Disaster |
10,029,649 | %matplotlib inline<set_options> | dataset6.pop('Fare')
dataset6.pop('Age' ) | Titanic - Machine Learning from Disaster |
10,029,649 | pd.set_option('display.float_format', '{:.2f}'.format)
pd.set_option('display.max_columns', 200)
pd.set_option('display.max_rows', 300)
<set_options> | dataset_test = pd.read_csv("/kaggle/input/titanic/test.csv")
dataset_test.head() | Titanic - Machine Learning from Disaster |
10,029,649 | try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
strategy = tf.distrib... | dataset_test.isnull().sum() | Titanic - Machine Learning from Disaster |
10,029,649 | def reload() :
gc.collect()
df = pd.read_csv('/kaggle/input/pubg-finish-placement-prediction/train_V2.csv')
invalid_match_ids = df[df['winPlacePerc'].isna() ]['matchId'].values
df = df[-df['matchId'].isin(invalid_match_ids)]
return df<load_from_csv> | dataset_test1 = dataset_test.dropna(axis = 1 ) | Titanic - Machine Learning from Disaster |
10,029,649 | train = pd.read_csv('/kaggle/input/pubg-finish-placement-prediction/train_V2.csv')
train = reduce_mem_usage(train )<count_unique_values> | dataset_test2 = dataset_test1.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | for c in ['Id','groupId','matchId']:
print(f'unique [{c}] count:', train[c].nunique() )<filter> | features_test = ['Sex' , 'Embarked'] | Titanic - Machine Learning from Disaster |
10,029,649 | display(list(train.columns[train.dtypes != 'object']))
display(list(train.columns[train.dtypes == 'object']))<count_missing_values> | dataset_test3 = pd.get_dummies(dataset_test2[features_test] ) | Titanic - Machine Learning from Disaster |
10,029,649 | train = train.dropna(axis='rows')
display(train.isna().sum() )<drop_column> | dataset_test2['Sex_female'] = dataset_test3['Sex_female']
dataset_test2['Sex_male'] = dataset_test3['Sex_male']
dataset_test2['C'] = dataset_test3['Embarked_C']
dataset_test2['Q'] = dataset_test3['Embarked_Q']
dataset_test2['S'] = dataset_test3['Embarked_S'] | Titanic - Machine Learning from Disaster |
10,029,649 | def delete_cheaters(df):
df.drop(df[df['roadKills'] >= 10].index, inplace=True)
df.drop(df[df['kills'] >= 50].index, inplace=True)
df.drop(df[df['longestKill'] >= 1000].index, inplace=True)
df.drop(df[df['walkDistance'] >= 13000].index, inplace=True)
df.drop(df[df['rideDistance'] >= 25000].index, inplace=True)
df.... | dataset_test2.pop('Name')
dataset_test2.pop('Sex')
dataset_test2.pop('Ticket')
dataset_test2.pop('Embarked' ) | Titanic - Machine Learning from Disaster |
10,029,649 | def feature_engineering(df):
df['heals_and_boosts'] = df['heals']+df['boosts']
df['total_distance'] = df['walkDistance']+df['rideDistance']+df['swimDistance']
df['kills_over_walkDistance'] = df['kills'] / df['walkDistance']
df['killPlace_over_maxPlace'] = df['killPlace'] / df['maxPlace']
df['players_joined'] = df.group... | dataset8 = dataset6.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | def isolation_forest(df, contamination):
cols = list(df.columns[df.dtypes != 'object'])
train_df = df[cols]
outlier_detect = IsolationForest(n_estimators=500,
contamination=contamination,
max_features=train_df.shape[1])
outlier_detect.fit(train_df)
outliers_predicted = outlier_detect.predict(train_df)
df['outlier']... | dataset8.pop('PassengerId' ) | Titanic - Machine Learning from Disaster |
10,029,649 | def delete_outlier(y_pred, y_true, remain=0.99):
mse_array = np.square(np.subtract(y_pred, y_true)).mean(axis=1)
mse_series = pd.Series(mse_array)
check_value = mse_series.quantile(remain)
check_outlier = np.where(mse_array <= check_value, 1, -1)
return check_outlier, mse_series
def sampling(args):
z_mean, z_log_... | dataset9 = dataset8.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | def VAE(df, remain_ratio):
np.random.seed(0)
tf.random.set_seed(0)
lr_sched = step_decay_schedule(initial_lr=0.001, decay_factor=0.97, step_size=1, verbose=0)
early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1)
cols = list(df.columns[df.dtypes != 'object'])
train_df = df[cols]
input_shape =(... | dataset9.pop('C')
dataset9.pop('Q')
dataset9.pop('S' ) | Titanic - Machine Learning from Disaster |
10,029,649 | def std_n_sigma(df, n, filter_):
cols = list(df.columns[df.dtypes != 'object'])
filter_ = list(df[filter_])
df = df[cols]
scaler = StandardScaler()
std_array = scaler.fit_transform(df.astype(float))
std_df = pd.DataFrame(std_array, columns=df.columns, index=filter_)
remove_idx = set([])
for col in std_df.columns:
r... | y = dataset8.pop('Survived' ) | Titanic - Machine Learning from Disaster |
10,029,649 | def data_preparation(df, train_flag=True):
origin_size = df.shape[0]
if train_flag:
print("Delete PUBG Cheaters
")
df = VAE(df, remain_ratio=0.998)
print('PUBG cheaters: {0}, {1}%
'.format(origin_size -len(df),
round(100 - 100 * len(df)/ origin_size,4)))
else:
pass
print("Feature engineering")
df = feature_engineer... | dataset_test4 = dataset_test2.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | train_ = data_preparation(train)
train_.head()
gc.collect()<split> | dataset_test4.pop('PassengerId' ) | Titanic - Machine Learning from Disaster |
10,029,649 | train_data = train_.drop(columns = ['winPlacePerc'])
train_labels = train_['winPlacePerc']
train_x, val_x, train_y, val_y = train_test_split(train_data, train_labels, test_size=0.1, random_state=0)
print(train_x.shape, train_y.shape, val_y.shape, val_x.shape)
del train_, train_data, train_labels
gc.collect()<prepare... | dataset_test5 = dataset_test4.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | use_features = 80
im_features = feature_importance.sort_values(by='Value', ascending=False)[:use_features].Feature
scaler = StandardScaler()
X_train = scaler.fit_transform(train_x[im_features].astype(np.float32))
Y_train = train_y.values
X_val = scaler.fit_transform(val_x[im_features].astype(np.float32))
Y_val = val_y.... | dataset_test5.pop('C')
dataset_test5.pop('Q')
dataset_test5.pop('S' ) | Titanic - Machine Learning from Disaster |
10,029,649 | from keras import optimizers, regularizers
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, BatchNormalization
from keras.callbacks import LearningRateScheduler, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Input, Dense, Lambda
from keras.loss... | from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import * | Titanic - Machine Learning from Disaster |
10,029,649 | def model() :
hidden_layer = tf.keras.layers.Dense(2048, kernel_initializer='he_normal', activation='relu' )(inputs)
hidden_layer = tf.keras.layers.BatchNormalization()(hidden_layer)
hidden_layer = tf.keras.layers.Dropout(rate=0.1, seed=1234 )(hidden_layer)
hidden_layer = tf.keras.layers.Dense(1024, kernel_initializ... | data = dataset.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | np.random.seed(0)
tf.random.set_seed(0)
epochs = 500
batch_size = 20480
steps = len(X_train)// batch_size
optimizer = tf.keras.optimizers.Adam()
lr_sched = step_decay_schedule(initial_lr=0.001, decay_factor=0.97, step_size=1, verbose=0)
early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_mae', mode='min',... | data['Name'] = data['Name'].apply(lambda x: x.split(',')[1] ) | Titanic - Machine Learning from Disaster |
10,029,649 | del X_train, Y_train, X_val, Y_val
gc.collect()<import_modules> | data['Name'] = data['Name'].apply(lambda x: x.split('.')[0] ) | Titanic - Machine Learning from Disaster |
10,029,649 | %matplotlib inline<set_options> | data1 = pd.get_dummies(data['Name'] ) | Titanic - Machine Learning from Disaster |
10,029,649 | pd.set_option('display.float_format', '{:.2f}'.format)
pd.set_option('display.max_columns', 200)
pd.set_option('display.max_rows', 300)
<set_options> | dataset11 = dataset8.copy()
| Titanic - Machine Learning from Disaster |
10,029,649 | try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print('Running on TPU ', tpu.master())
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
strategy = tf.distrib... | t = [' Col' , ' Dr' , ' Master' , ' Miss' ,' Mr', ' Mrs' , ' Ms' , ' Rev']
for i in t:
dataset11[i] = data1[i] | Titanic - Machine Learning from Disaster |
10,029,649 | def reload() :
gc.collect()
df = pd.read_csv('/kaggle/input/pubg-finish-placement-prediction/train_V2.csv')
invalid_match_ids = df[df['winPlacePerc'].isna() ]['matchId'].values
df = df[-df['matchId'].isin(invalid_match_ids)]
return df<load_from_csv> | data1.isnull().sum() | Titanic - Machine Learning from Disaster |
10,029,649 | train = pd.read_csv('/kaggle/input/pubg-finish-placement-prediction/train_V2.csv')
train = reduce_mem_usage(train )<count_unique_values> | dataset_test6 = dataset_test.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | for c in ['Id','groupId','matchId']:
print(f'unique [{c}] count:', train[c].nunique() )<filter> | dataset_test6['Name'] = dataset_test6['Name'].apply(lambda x: x.split(',')[1] ) | Titanic - Machine Learning from Disaster |
10,029,649 | display(list(train.columns[train.dtypes != 'object']))
display(list(train.columns[train.dtypes == 'object']))<count_missing_values> | dataset_test6['Name'] = dataset_test6['Name'].apply(lambda x: x.split('.')[0] ) | Titanic - Machine Learning from Disaster |
10,029,649 | train = train.dropna(axis='rows')
display(train.isna().sum() )<drop_column> | dataset_test7 = pd.get_dummies(dataset_test6['Name'] ) | Titanic - Machine Learning from Disaster |
10,029,649 | def delete_cheaters(df):
df.drop(df[df['roadKills'] >= 10].index, inplace=True)
df.drop(df[df['kills'] >= 50].index, inplace=True)
df.drop(df[df['longestKill'] >= 1000].index, inplace=True)
df.drop(df[df['walkDistance'] >= 13000].index, inplace=True)
df.drop(df[df['rideDistance'] >= 25000].index, inplace=True)
df.... | for i in dataset_test7.keys() :
dataset_test4[i] = dataset_test7[i] | Titanic - Machine Learning from Disaster |
10,029,649 | def feature_engineering(df):
df['heals_and_boosts'] = df['heals']+df['boosts']
df['total_distance'] = df['walkDistance']+df['rideDistance']+df['swimDistance']
df['kills_over_walkDistance'] = df['kills'] / df['walkDistance']
df['killPlace_over_maxPlace'] = df['killPlace'] / df['maxPlace']
df['players_joined'] = df.group... | dataset_test4.pop(' Dona' ) | Titanic - Machine Learning from Disaster |
10,029,649 | def isolation_forest(df, contamination):
cols = list(df.columns[df.dtypes != 'object'])
train_df = df[cols]
outlier_detect = IsolationForest(n_estimators=500,
contamination=contamination,
max_features=train_df.shape[1])
outlier_detect.fit(train_df)
outliers_predicted = outlier_detect.predict(train_df)
df['outlier']... | dataset_test4.isnull().sum() | Titanic - Machine Learning from Disaster |
10,029,649 | def delete_outlier(y_pred, y_true, remain=0.99):
mse_array = np.square(np.subtract(y_pred, y_true)).mean(axis=1)
mse_series = pd.Series(mse_array)
check_value = mse_series.quantile(remain)
check_outlier = np.where(mse_array <= check_value, 1, -1)
return check_outlier, mse_series
def sampling(args):
z_mean, z_log_... | dataset11.isnull().sum() | Titanic - Machine Learning from Disaster |
10,029,649 | def VAE(df, remain_ratio):
np.random.seed(0)
tf.random.set_seed(0)
lr_sched = step_decay_schedule(initial_lr=0.001, decay_factor=0.97, step_size=1, verbose=0)
early_stopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1)
cols = list(df.columns[df.dtypes != 'object'])
train_df = df[cols]
input_shape =(... | dataset_final = dataset_test4.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | def std_n_sigma(df, n, filter_):
cols = list(df.columns[df.dtypes != 'object'])
filter_ = list(df[filter_])
df = df[cols]
scaler = StandardScaler()
std_array = scaler.fit_transform(df.astype(float))
std_df = pd.DataFrame(std_array, columns=df.columns, index=filter_)
remove_idx = set([])
for col in std_df.columns:
r... | dataset_final['Total_SibSp'] = dataset_test4['SibSp']+dataset_test4['Parch'] | Titanic - Machine Learning from Disaster |
10,029,649 | def data_preparation(df, train_flag=True):
origin_size = df.shape[0]
if train_flag:
print("Delete PUBG Cheaters
")
df = VAE(df, remain_ratio=0.998)
print('PUBG cheaters: {0}, {1}%
'.format(origin_size -len(df),
round(100 - 100 * len(df)/ origin_size,4)))
else:
pass
print("Feature engineering")
df = feature_engineer... | dataset_final.pop('SibSp')
dataset_final.pop('Parch' ) | Titanic - Machine Learning from Disaster |
10,029,649 | train_ = data_preparation(train)
train_.head()
gc.collect()<split> | dataset12 = dataset11.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | train_data = train_.drop(columns = ['winPlacePerc'])
train_labels = train_['winPlacePerc']
train_x, val_x, train_y, val_y = train_test_split(train_data, train_labels, test_size=0.1, random_state=0)
print(train_x.shape, train_y.shape, val_y.shape, val_x.shape)
del train_, train_data, train_labels
gc.collect()<prepare... | datasettrain_final = dataset11.copy() | Titanic - Machine Learning from Disaster |
10,029,649 | use_features = 80
im_features = feature_importance.sort_values(by='Value', ascending=False)[:use_features].Feature
scaler = StandardScaler()
X_train = scaler.fit_transform(train_x[im_features].astype(np.float32))
Y_train = train_y.values
X_val = scaler.fit_transform(val_x[im_features].astype(np.float32))
Y_val = val_y.... | datasettrain_final['Total_SibSp'] = dataset11['SibSp']+dataset11['Parch'] | Titanic - Machine Learning from Disaster |
10,029,649 | from keras import optimizers, regularizers
from keras.models import Sequential, Model, load_model
from keras.layers import Dense, Dropout, BatchNormalization
from keras.callbacks import LearningRateScheduler, EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Input, Dense, Lambda
from keras.loss... | datasettrain_final.pop('SibSp')
datasettrain_final.pop('Parch' ) | Titanic - Machine Learning from Disaster |
10,029,649 | def model() :
hidden_layer = tf.keras.layers.Dense(2048, kernel_initializer='he_normal', activation='relu' )(inputs)
hidden_layer = tf.keras.layers.BatchNormalization()(hidden_layer)
hidden_layer = tf.keras.layers.Dropout(rate=0.1, seed=1234 )(hidden_layer)
hidden_layer = tf.keras.layers.Dense(1024, kernel_initializ... | x_train , x_test , y_train , y_test = train_test_split(datasettrain_final , y , test_size = 0.2 , random_state = 10 ) | Titanic - Machine Learning from Disaster |
10,029,649 | np.random.seed(0)
tf.random.set_seed(0)
epochs = 500
batch_size = 20480
steps = len(X_train)// batch_size
optimizer = tf.keras.optimizers.Adam()
lr_sched = step_decay_schedule(initial_lr=0.001, decay_factor=0.97, step_size=1, verbose=0)
early_stopping = tf.keras.callbacks.EarlyStopping(monitor='val_mae', mode='min',... | from sklearn.ensemble import *
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from xgboost import XGBClassifier | Titanic - Machine Learning from Disaster |
10,029,649 | del X_train, Y_train, X_val, Y_val
gc.collect()<data_type_conversions> | obj = XGBClassifier(learning_rate=0.02, n_estimators=750,
max_depth= 3, min_child_weight= 1,
colsample_bytree= 0.6, gamma= 0.0,
reg_alpha= 0.001, subsample= 0.8)
model = obj.fit(x_train, y_train)
predicted_val = obj.predict(x_test)
predicted_test = obj.predict(dataset_final)
predicted_train = obj.predict(x_train ) | Titanic - Machine Learning from Disaster |
10,029,649 | def cleanPeople(people):
people = people.drop(['date'],axis=1)
people['people_id'] = people['people_id'].apply(lambda x : x.split('_')[1])
people['people_id'] = pd.to_numeric(people['people_id'] ).astype(int)
fields = list(people.columns)
cat_data = fields[1:11]
bool_data = fields[11:]
for data in cat_data:
people[... | accuracy_train = accuracy_score(y_true=y_train , y_pred= predicted_train)
accuracy_val = accuracy_score(y_true=y_test, y_pred= predicted_val ) | Titanic - Machine Learning from Disaster |
10,029,649 | people = pd.read_csv(".. /input/people.csv")
people = cleanPeople(people)
act_train = pd.read_csv(".. /input/act_train.csv",parse_dates=['date'])
act_train_cleaned = cleanAct(act_train,train=True)
act_test = pd.read_csv(".. /input/act_test.csv",parse_dates=['date'])
act_test_cleaned = cleanAct(act_test)
train = a... | output = pd.DataFrame({'PassengerId': dataset_test.PassengerId, 'Survived': predicted_test} ) | Titanic - Machine Learning from Disaster |
10,029,649 | train = pd.get_dummies(train,columns=['activity_category'],sparse=True,drop_first=True)
test = pd.get_dummies(test,columns=['activity_category'],sparse=True,drop_first=True )<prepare_x_and_y> | output.to_csv('my_submission.csv', index=False)
print("Your submission was successfully saved!" ) | Titanic - Machine Learning from Disaster |
13,636,756 | train_mask, valid_mask = list(LabelKFold(train['people_id'], n_folds=10)) [0]
x_test = test.drop(['people_id','activity_id'],axis=1)
y = act_train['outcome']
train = train.drop(['people_id', 'activity_id'], axis=1)
kklo=x_train = np.array(train)[train_mask]
y_train = np.array(y)[train_mask]
x_valid = np.array(train)[... | train_data = pd.read_csv(".. /input/titanic/train.csv")
test_data = pd.read_csv(".. /input/titanic/test.csv")
train_data.columns | Titanic - Machine Learning from Disaster |
13,636,756 | clf = xgb.train(params, d_train, 800, watchlist, xgb_model ='mymodel.model', early_stopping_rounds=40 )<save_to_csv> | def outlier_detect(feature, data):
outlier_index = []
for each in feature:
Q1 = np.percentile(data[each], 25)
Q3 = np.percentile(data[each], 75)
IQR = Q3 - Q1
min_quartile = Q1 - 1.5*IQR
max_quartile = Q3 + 1.5*IQR
outlier_list = data[(data[each] < min_quartile)|(data[each] > max_quartile)].index
outlier_index.extend... | Titanic - Machine Learning from Disaster |
13,636,756 | p_test = clf.predict(xgb.DMatrix(np.array(x_test)))
sub = pd.DataFrame()
sub['activity_id'] = act_test['activity_id']
sub['outcome'] = p_test
sub.to_csv('submission.csv', index=False )<import_modules> | outlier_data = outlier_detect(["Age","SibSp","Parch","Fare"], train_data)
train_data.loc[outlier_data]
| Titanic - Machine Learning from Disaster |
13,636,756 | import numpy as np
import pandas as pd
import xgboost as xgb
<data_type_conversions> | train_data = train_data.drop(outlier_data, axis=0 ).reset_index(drop=True ) | Titanic - Machine Learning from Disaster |
13,636,756 | def cleanPeople(people):
people = people.drop(['date'],axis=1)
people['people_id'] = people['people_id'].apply(lambda x : x.split('_')[1])
people['people_id'] = pd.to_numeric(people['people_id'] ).astype(int)
fields = list(people.columns)
cat_data = fields[1:11]
bool_data = fields[11:]
for data in cat_data:
people[... | data = pd.concat([train_data, test_data], axis=0 ).reset_index(drop=True ) | Titanic - Machine Learning from Disaster |
13,636,756 | model = xgb.XGBClassifier(max_depth=8,n_estimators=500,learning_rate=0.1,objective='binary:logistic',seed =7,reg_lambda=1 )<train_model> | data[["Sex", "Survived"]].groupby(["Sex"], as_index = False ).mean() | Titanic - Machine Learning from Disaster |
13,636,756 | model.fit(X_train,y_train )<compute_train_metric> | data.columns[data.isnull().any() ] | Titanic - Machine Learning from Disaster |
13,636,756 | results = model.predict_proba(X_test)
s = results[:,1]
score = roc_auc_score(y_test,s)
print(score )<save_to_csv> | data.isnull().sum() | Titanic - Machine Learning from Disaster |
13,636,756 | results = model.predict_proba(test)
s = results[:,1]
activity = act_test['activity_id']
result = pd.DataFrame({'activity_id': activity, 'outcome': s})
result.to_csv("Result.csv",index=False )<load_from_csv> | data.isnull().sum() | Titanic - Machine Learning from Disaster |
13,636,756 | items = pd.read_csv('.. /input/items.csv')
shops = pd.read_csv('.. /input/shops.csv')
cats = pd.read_csv('.. /input/item_categories.csv')
train = pd.read_csv('.. /input/sales_train.csv')
test = pd.read_csv('.. /input/test.csv' ).set_index('ID' )<filter> | data[data["Fare"].isnull() ] | Titanic - Machine Learning from Disaster |
13,636,756 | train = train[train.item_price<100000]
train = train[train.item_cnt_day<1001]<feature_engineering> | data["Fare"] = data["Fare"].fillna(np.mean(data[(( data["Pclass"]==3)&(data["Embarked"]==0)) ]["Fare"]))
data[data["Fare"].isnull() ] | Titanic - Machine Learning from Disaster |
13,636,756 | median = train[(train.shop_id==32)&(train.item_id==2973)&(train.date_block_num==4)&(train.item_price>0)].item_price.median()
train.loc[train.item_price<0, 'item_price'] = median<feature_engineering> | data[data["Embarked"].isnull() ] | Titanic - Machine Learning from Disaster |
13,636,756 | train.loc[train.shop_id == 0, 'shop_id'] = 57
test.loc[test.shop_id == 0, 'shop_id'] = 57
train.loc[train.shop_id == 1, 'shop_id'] = 58
test.loc[test.shop_id == 1, 'shop_id'] = 58
train.loc[train.shop_id == 10, 'shop_id'] = 11
test.loc[test.shop_id == 10, 'shop_id'] = 11<categorify> | data["Embarked"] = data["Embarked"].fillna(1)
data[data["Embarked"].isnull() ] | Titanic - Machine Learning from Disaster |
13,636,756 | shops.loc[shops.shop_name == 'Сергиев Посад ТЦ "7Я"', 'shop_name'] = 'СергиевПосад ТЦ "7Я"'
shops['city'] = shops['shop_name'].str.split(' ' ).map(lambda x: x[0])
shops.loc[shops.city == '!Якутск', 'city'] = 'Якутск'
shops['city_code'] = LabelEncoder().fit_transform(shops['city'])
shops = shops[['shop_id','city_code'... | data[data["Age"].isnull() ] | Titanic - Machine Learning from Disaster |
13,636,756 | len(list(set(test.item_id)- set(test.item_id ).intersection(set(train.item_id)))) , len(list(set(test.item_id))), len(test )<data_type_conversions> | data_age_nan_index = data[data["Age"].isnull() ].index
for i in data_age_nan_index:
mean_age = data["Age"][(data["Pclass"]==data.iloc[i]["Pclass"])].median()
data["Age"].iloc[i] = mean_age | Titanic - Machine Learning from Disaster |
13,636,756 | ts = time.time()
matrix = []
cols = ['date_block_num','shop_id','item_id']
for i in range(34):
sales = train[train.date_block_num==i]
matrix.append(np.array(list(product([i], sales.shop_id.unique() , sales.item_id.unique())) , dtype='int16'))
matrix = pd.DataFrame(np.vstack(matrix), columns=cols)
matrix['date_block_nu... | data["Alone"] = [1 if i == 0 else 0 for i in data["Family"]]
data["Family"].replace([0,1,2,3,4,5,6,7,10], [0,1,1,1,0,2,0,2,2], inplace=True)
data.head() | Titanic - Machine Learning from Disaster |
13,636,756 | train['revenue'] = train['item_price'] * train['item_cnt_day']<merge> | data['Title']=data.Name.str.extract('([A-Za-z]+)\.' ) | Titanic - Machine Learning from Disaster |
13,636,756 | ts = time.time()
group = train.groupby(['date_block_num','shop_id','item_id'] ).agg({'item_cnt_day': ['sum']})
group.columns = ['item_cnt_month']
group.reset_index(inplace=True)
matrix = pd.merge(matrix, group, on=cols, how='left')
matrix['item_cnt_month'] =(matrix['item_cnt_month']
.fillna(0)
.clip(0,20)
.astype(n... | data['Title'].replace(['Mme','Ms','Mlle','Lady','Countess','Dona','Dr','Major','Sir','Capt','Don','Rev','Col', 'Jonkheer'],['Miss','Miss','Miss','Mrs','Mrs','Mrs','Mr','Mr','Mr','Mr','Mr','Other','Other','Other'], inplace=True ) | Titanic - Machine Learning from Disaster |
13,636,756 | test['date_block_num'] = 34
test['date_block_num'] = test['date_block_num'].astype(np.int8)
test['shop_id'] = test['shop_id'].astype(np.int8)
test['item_id'] = test['item_id'].astype(np.int16 )<concatenate> | data['Age_Limit'] = LabelEncoder().fit_transform(data['Age_Limit'])
| Titanic - Machine Learning from Disaster |
13,636,756 | ts = time.time()
matrix = pd.concat([matrix, test], ignore_index=True, sort=False, keys=cols)
matrix.fillna(0, inplace=True)
time.time() - ts<data_type_conversions> | data['Fare_Limit'] = LabelEncoder().fit_transform(data['Fare_Limit'])
| Titanic - Machine Learning from Disaster |
13,636,756 | ts = time.time()
matrix = pd.merge(matrix, shops, on=['shop_id'], how='left')
matrix = pd.merge(matrix, items, on=['item_id'], how='left')
matrix = pd.merge(matrix, cats, on=['item_category_id'], how='left')
matrix['city_code'] = matrix['city_code'].astype(np.int8)
matrix['item_category_id'] = matrix['item_category... | data['Age']=data['Age'].astype(int)
data.drop(labels=["SibSp","Parch","Cabin","Fare","Age", "Ticket", "Name", "PassengerId"], axis=1, inplace = True)
data.head() | Titanic - Machine Learning from Disaster |
13,636,756 | def lag_feature(df, lags, col):
tmp = df[['date_block_num','shop_id','item_id',col]]
for i in lags:
shifted = tmp.copy()
shifted.columns = ['date_block_num','shop_id','item_id', col+'_lag_'+str(i)]
shifted['date_block_num'] += i
df = pd.merge(df, shifted, on=['date_block_num','shop_id','item_id'], how='left')
return d... | data = pd.get_dummies(data,columns=["Pclass"])
data = pd.get_dummies(data,columns=["Embarked"])
data = pd.get_dummies(data,columns=["Family"])
data = pd.get_dummies(data,columns=["Age_Limit"])
data = pd.get_dummies(data,columns=["Fare_Limit"])
data = pd.get_dummies(data,columns=["Title"])
data.head() | Titanic - Machine Learning from Disaster |
13,636,756 | ts = time.time()
group = matrix.groupby(['date_block_num'] ).agg({'item_cnt_month': ['mean']})
group.columns = [ 'date_avg_item_cnt' ]
group.reset_index(inplace=True)
matrix = pd.merge(matrix, group, on=['date_block_num'], how='left')
matrix['date_avg_item_cnt'] = matrix['date_avg_item_cnt'].astype(np.float16)
matr... | from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC | Titanic - Machine Learning from Disaster |
13,636,756 | ts = time.time()
group = matrix.groupby(['date_block_num', 'item_id'] ).agg({'item_cnt_month': ['mean']})
group.columns = [ 'date_item_avg_item_cnt' ]
group.reset_index(inplace=True)
matrix = pd.merge(matrix, group, on=['date_block_num','item_id'], how='left')
matrix['date_item_avg_item_cnt'] = matrix['date_item_avg... | if len(data)==(len(train_data)+ len(test_data)) :
print("success" ) | Titanic - Machine Learning from Disaster |
13,636,756 | ts = time.time()
group = matrix.groupby(['date_block_num', 'shop_id'] ).agg({'item_cnt_month': ['mean']})
group.columns = [ 'date_shop_avg_item_cnt' ]
group.reset_index(inplace=True)
matrix = pd.merge(matrix, group, on=['date_block_num','shop_id'], how='left')
matrix['date_shop_avg_item_cnt'] = matrix['date_shop_avg... | test = data[len(train_data):]
test.drop(labels="Survived", axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
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