kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
12,850,197 | train=train.join(total_revenue,on=["date_block_num","shop_id"],how="left")
train["total_shop_revenue"]=train["total_shop_revenue"].astype(np.float32)
train.fillna(0,inplace=True)
train.head().T<drop_column> | def fare_cat(fare):
if fare <= 7.0:
return 1
elif fare <= 39 and fare > 7.0:
return 2
else:
return 3
full_df.loc[:, 'Fare_Cat'] = full_df['Fare'].apply(fare_cat ).astype('int' ) | Titanic - Machine Learning from Disaster |
12,850,197 | %%time
train,n_col=create_lag_features(train,"total_shop_revenue")
train=train.fillna(0.0)
train=train.drop(["total_shop_revenue"],axis=1)
numeric=numeric+n_col<groupby> | full_df.loc[:, 'Fare_Family_Size'] = full_df['Fare']/full_df['Family_Size']
full_df.loc[:, 'Fare_Cat_Pclass'] = full_df['Fare_Cat']*full_df['Pclass']
full_df.loc[:, 'Fare_Cat_Title'] = full_df['Fare_Cat']*full_df['Title']
full_df.loc[:, 'Fsize_Cat_Title'] = full_df['Fsize_Cat']*full_df['Title']
full_df.loc[:, 'Fsize_Ca... | Titanic - Machine Learning from Disaster |
12,850,197 | groups = train.groupby(train.date_block_num ).groups
sorted_groups = [value for(key, value)in sorted(groups.items())]
cv=[(np.concatenate(sorted_groups[:8]),np.concatenate(sorted_groups[8:])) ,
(np.concatenate(sorted_groups[:16]),np.concatenate(sorted_groups[16:])) ,
(np.concatenate(sorted_groups[:24]),np.concatenate... | colsToRemove = []
cols = ['Tkt_AQ', 'Tkt_AS', 'Tkt_C', 'Tkt_CA',
'Tkt_CASOTON', 'Tkt_FC', 'Tkt_FCC', 'Tkt_Fa', 'Tkt_LINE', 'Tkt_LP',
'Tkt_NUM', 'Tkt_PC', 'Tkt_PP', 'Tkt_PPP', 'Tkt_SC', 'Tkt_SCA',
'Tkt_SCAH', 'Tkt_SCAHBasle', 'Tkt_SCOW', 'Tkt_SCPARIS', 'Tkt_SCParis',
'Tkt_SOC', 'Tkt_SOP', 'Tkt_SOPP', 'Tkt_SOTONO', 'Tkt_... | Titanic - Machine Learning from Disaster |
12,850,197 | y_train=train["item_cnt_day"]<data_type_conversions> | imp_features = ['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Title',
'Name_Length', 'Emb_C', 'Emb_Q', 'Emb_S','Family_Size',
'Fsize_Cat', 'Family_Friends_Surv_Rate', 'Surv_Rate_Invalid',
'Cabin_Clean','Ticket_Frequency', 'Tkt_AS', 'Tkt_C', 'Tkt_CA',
'Tkt_CASOTON', 'Tkt_FC', 'Tkt_FCC', 'Tkt_Fa', 'Tkt_LINE',
'Tkt_N... | Titanic - Machine Learning from Disaster |
12,850,197 | X_train_categorical=train[categorical]
X_test_categorical=test_df[categorical]
X_train_categorical["subcategory"]=X_train_categorical["subcategory"].astype(str)
X_test_categorical["subcategory"]=X_test_categorical["subcategory"].astype(str)
X_test_categorical["year"]=X_test_categorical["year"].astype(np.int16)
X_tes... | full_df.loc[:, imp_features] = pd.DataFrame(imputer.transform(full_df[imp_features]), index=full_df.index, columns = imp_features ) | Titanic - Machine Learning from Disaster |
12,850,197 | for feature in categorical:
le=LabelEncoder()
print(feature)
X_train_categorical[feature]=le.fit_transform(X_train_categorical[feature])
X_test_categorical[feature]=le.transform(X_test_categorical[feature] )<prepare_x_and_y> | df_train_final = full_df[:train_shape[0]]
df_test_final = full_df[train_shape[0]:] | Titanic - Machine Learning from Disaster |
12,850,197 | X_train_numeric=train[numeric]
X_test_numeric=test_df[numeric]<concatenate> | viz_features = ['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex',
'Age', 'SibSp','Parch', 'Ticket', 'Fare', 'Cabin',
'Title', 'Name_Length', 'Family_Friends_Surv_Rate',
'Ticket_Frequency']
train_viz = df_train_final[viz_features] | Titanic - Machine Learning from Disaster |
12,850,197 | label_cat_not_num_train=pd.concat([X_train_categorical,X_train_numeric],axis=1)
label_cat_not_num_train.head().T<concatenate> | full_df.to_pickle("full_df")
df_train_final.to_pickle("df_train_final")
df_test_final.to_pickle("df_test_final" ) | Titanic - Machine Learning from Disaster |
12,850,197 | label_cat_not_num_test=pd.concat([X_test_categorical,X_test_numeric],axis=1)
label_cat_not_num_test.head().T<data_type_conversions> | scaler_cols = ['Age', 'Fare', 'Name_Length', 'Family_Size', 'Name_Length',
'Ticket_Frequency', 'Fare_Family_Size', 'Fare_Cat_Pclass']
std = StandardScaler()
std.fit(df_train_final[scaler_cols])
df_train_final.loc[:, scaler_cols] = pd.DataFrame(std.transform(df_train_final[scaler_cols]), index=df_train_final.index, col... | Titanic - Machine Learning from Disaster |
12,850,197 | def downcast_type(df):
for feature in categorical:
df[feature]=df[feature].astype(np.int8 )<compute_test_metric> | features = ['Pclass', 'Sex', 'Age', 'Fare', 'Title', 'Name_Length', 'Emb_C',
'Emb_Q', 'Emb_S', 'Title_Master', 'Title_Miss', 'Title_Mr', 'Title_Mrs',
'Title_Other', 'Title_Royal', 'Family_Size', 'Fsize_Cat',
'Family_Friends_Surv_Rate', 'Surv_Rate_Invalid', 'Cabin_Clean',
'Ticket_Frequency', 'Tkt_AS', 'Tkt_C', 'Tkt_CA',... | Titanic - Machine Learning from Disaster |
12,850,197 | def RMSE(y,predictions):
return np.sqrt(mean_squared_error(y,predictions))
scorer=make_scorer(RMSE,False )<compute_train_metric> | corr_mat = df_train_final.astype(float ).corr()
corr_mat_fil = corr_mat.loc[:, 'Survived'].sort_values(ascending=False)
corr_mat_fil = pd.DataFrame(data=corr_mat_fil[1:] ) | Titanic - Machine Learning from Disaster |
12,850,197 | %%time
baseline = -cross_val_score(
XGBRegressor(max_depth=10,
subsample=0.8,
colsample_bytree=0.9,
colsample_bylevel=0.7,
min_child_weight=200,
n_estimators=1000,
learning_rate=0.025,
objective="reg:squarederror",
tree_method="hist"), label_cat_not_num_train, y_train, scoring=scorer,cv=cv
).mean()
print(baseline )<ch... | features = df_test_final.columns.to_list()
X_train = df_train_final[features]
Y_train = df_train_final['Survived']
X_test = df_test_final | Titanic - Machine Learning from Disaster |
12,850,197 | %%time
estimator=XGBRegressor(max_depth=10,
subsample=0.8,
colsample_bytree=0.9,
colsample_bylevel=0.7,
min_child_weight=200,
n_estimators=1000,
learning_rate=0.025,
objective="reg:squarederror",
tree_method="hist")
estimator.fit(label_cat_not_num_train,y_train)
importances=estimator.feature_importances_
predictions=... | from sklearn.model_selection import cross_val_predict, cross_val_score, cross_validate
from sklearn.model_selection import StratifiedKFold, StratifiedShuffleSplit
from sklearn.metrics import confusion_matrix, roc_curve
from sklearn.metrics import precision_score, recall_score, f1_score
import tensorflow as tf
from tens... | Titanic - Machine Learning from Disaster |
12,850,197 | sample_submissions["item_cnt_month"]=predictions
sample_submissions.head()<save_to_csv> | metrics = ['accuracy',
Precision() ,
Recall() ] | Titanic - Machine Learning from Disaster |
12,850,197 | sample_submissions.to_csv("xgboost_lagged_features_6.csv",index=False )<set_options> | def create_model() :
model = Sequential()
model.add(Input(shape=X_train.shape[1], name='Input_'))
model.add(Dense(8, activation='relu', kernel_initializer='glorot_normal', kernel_regularizer=l2(0.001)))
model.add(Dense(16, activation='relu', kernel_initializer='glorot_normal', kernel_regularizer=l2(0.1)))
model.add(D... | Titanic - Machine Learning from Disaster |
12,850,197 | %matplotlib inline<load_from_csv> | estimator = KerasClassifier(build_fn = create_model, epochs = 600, batch_size = 32, verbose = 1)
kfold = StratifiedKFold(n_splits = 3)
results = cross_val_score(estimator, X_train, Y_train, cv = kfold ) | Titanic - Machine Learning from Disaster |
12,850,197 | shops = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/shops.csv')
items = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/items.csv')
catgs = pd.read_csv('/kaggle/input/competitive-data-science-predict-future-sales/item_categories.csv')
sales = pd.read_csv('/kaggle... | train_history = estimator.fit(X_train, Y_train, epochs = 600, batch_size = 32 ) | Titanic - Machine Learning from Disaster |
12,850,197 | <feature_engineering><EOS> | y_preds = estimator.predict(X_test)
submission = pd.read_csv(".. /input/titanic/gender_submission.csv", index_col='PassengerId')
submission['Survived'] = y_preds.astype(int)
submission.to_csv('submission.csv' ) | Titanic - Machine Learning from Disaster |
11,482,088 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<feature_engineering> | %matplotlib inline
sns.set_style('dark')
sns.set_palette('Set2')
| Titanic - Machine Learning from Disaster |
11,482,088 | sales.loc[sales.shop_id == 0, 'shop_id'] = 57
testd.loc[testd.shop_id == 0, 'shop_id'] = 57
sales.loc[sales.shop_id == 1, 'shop_id'] = 58
testd.loc[testd.shop_id == 1, 'shop_id'] = 58
sales.loc[sales.shop_id == 10, 'shop_id'] = 11
testd.loc[testd.shop_id == 10, 'shop_id'] = 11
sales.loc[sales.shop_id == 39, 'shop_id'] ... | train_data = pd.read_csv('.. /input/titanic/train.csv')
test_data = pd.read_csv('.. /input/titanic/test.csv')
data = pd.concat([train_data, test_data] ).reset_index().drop(['index'], axis=1 ) | Titanic - Machine Learning from Disaster |
11,482,088 | shops.loc[shops.shop_name == 'Сергиев Посад ТЦ "7Я"', 'shop_name'] = 'СергиевПосад ТЦ "7Я"'
shops['shop_category'] = shops['shop_name'].str.split(' ' ).map(lambda x:x[1] ).astype(str)
categories = ['Орджоникидзе,', 'ТЦ', 'ТРК', 'ТРЦ','ул.', 'Магазин', 'ТК', 'склад']
shops.shop_category = shops.shop_category.apply(lamb... | data['Surname'] = data['Name'].apply(lambda x: x.split(',')[0] ) | Titanic - Machine Learning from Disaster |
11,482,088 | shops.groupby(['shop_category'] ).sum()<categorify> | data['Ticket_id'] = 'new_col'
def ticket_id(row):
row['Ticket_id'] = str(row.Pclass)+ '-' + str(row.Ticket)[:-1] + '-' + str(row.Fare)+ '-' + str(row.Embarked)
return row
data = data.apply(ticket_id, axis='columns' ) | Titanic - Machine Learning from Disaster |
11,482,088 | category = ['ТЦ', 'ТРК', 'ТРЦ', 'ТК']
shops.shop_category = shops.shop_category.apply(lambda x: x if(x in category)else 'etc')
print('Category Distribution', shops.groupby(['shop_category'] ).sum())
shops['shop_category_code'] = LabelEncoder().fit_transform(shops['shop_category'] )<categorify> | data['Group_id'] = 'new_col2'
def group_id(row):
row['Group_id'] = str(row.Surname)+ '-' + str(row.Ticket_id)
return row
data = data.apply(group_id, axis='columns' ) | Titanic - Machine Learning from Disaster |
11,482,088 | 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', 'shop_category_code']]
shops.head()<count_unique_values> | data['Title'] = 'man'
data.loc[data.Sex == 'female', 'Title'] = 'woman'
data.loc[data['Name'].str.contains('Master'), 'Title'] = 'boy' | Titanic - Machine Learning from Disaster |
11,482,088 | print(len(catgs.item_category_name.unique()))
catgs.item_category_name.unique()<feature_engineering> | data.loc[data.Title == 'man', 'Group_id'] = 'noGroup'
data['WC_count'] = data.loc[data.Title != 'man'].groupby('Group_id')['Group_id'].transform('count')
data.loc[data.WC_count <=1, 'Group_id'] = 'noGroup' | Titanic - Machine Learning from Disaster |
11,482,088 | catgs['type'] = catgs.item_category_name.apply(lambda x: x.split(' ')[0] ).astype(str)
catgs.loc[(catgs.type == 'Игровые')|(catgs.type == 'Аксессуары'), 'category'] = 'Игры'
catgs.loc[catgs.type == 'PC', 'category'] = 'Музыка'
category = ['Игры', 'Карты', 'Кино', 'Книги','Музыка', 'Подарки', 'Программы', 'Служебные', ... | cols = ['PassengerId', 'Survived', 'Name', 'Title', 'Ticket_id','Group_id']
data.loc[(data.Ticket_id == '1-1696-134.5-C')&(data.Title != 'man'), cols] | Titanic - Machine Learning from Disaster |
11,482,088 | sales['date'] = pd.to_datetime(sales['date'], format='%d.%m.%Y')
sales['month'] = sales['date'].dt.month
sales['year'] = sales['date'].dt.year
sales = sales.drop(columns=['date'])
to_append = testd[['shop_id', 'item_id']].copy()
to_append['date_block_num'] = sales['date_block_num'].max() + 1
to_append['year'] = 2015
... | indices = []
count = 0
for i in range(0,1309):
if(data.loc[i,'Title'] != 'man')&(data.loc[i,'Group_id'] == 'noGroup'):
data.loc[i,'Group_id'] = data.loc[(data['Ticket_id'] == data.loc[i, 'Ticket_id'])&(data.Title != 'man'), 'Group_id'].iloc[0]
if(data.loc[i, 'Group_id'] != 'noGroup'):
indices.append(i)
count += 1
prin... | Titanic - Machine Learning from Disaster |
11,482,088 | period = sales[['date_block_num', 'year', 'month']].drop_duplicates().reset_index(drop=True)
period['days'] = period.apply(lambda r: monthrange(r.year, r.month)[1], axis=1)
sales = sales.drop(columns=['month', 'year'])
period.head()<merge> | cols = ['PassengerId', 'Survived', 'Name', 'Title', 'Group_id']
data.loc[indices, cols] | Titanic - Machine Learning from Disaster |
11,482,088 | data = pd.merge(grid, shops, on='shop_id')
data = pd.merge(data, items, on='item_id')
data = pd.merge(data, catgs, on='item_category_id')
data = pd.merge(data, period, on='date_block_num')
data = data[['date_block_num', 'year', 'month', 'days', 'city_code', 'shop_category_code', 'shop_id', 'item_category_id', 'type... | number_of_groups = data.loc[data.Group_id != 'noGroup', 'Group_id'].nunique()
print('Number of groups found: {:d}'.format(number_of_groups))
number_of_WCG_passengers = data.loc[data.Group_id != 'noGroup', 'Group_id'].count()
print('
Number of passengers in a group: {:d}'.format(number_of_WCG_passengers))
composition = ... | Titanic - Machine Learning from Disaster |
11,482,088 | aux = sales\
.groupby(['date_block_num', 'shop_id', 'item_id'], as_index=False)\
.agg({'item_cnt_day' : 'sum', 'item_price' : 'mean'})\
.rename(columns= {'item_cnt_day' : 'item_cnt_month', 'item_price' : 'item_price_month'})
aux['item_cnt_month'] = aux['item_cnt_month'].astype(np.float16)
aux['item_price_month'] =... | data['WCSurvived'] = data.loc[(data.Title != 'man')&(data.Group_id != 'noGroup')].groupby('Group_id' ).Survived.transform('mean' ) | Titanic - Machine Learning from Disaster |
11,482,088 | month_summary['item_cnt_month'] = month_summary['item_cnt_month'].clip(0,20 )<categorify> | cols = ['PassengerId', 'Survived', 'WCSurvived', 'Name', 'Title', 'Group_id']
data.loc[data.Group_id == 'Sage-3-CA.234-69.55-S', cols] | Titanic - Machine Learning from Disaster |
11,482,088 | def agg_by(month_summary, group_cols, new_col, target_col = 'item_cnt_month', agg_func = 'mean'):
aux = month_summary\
.groupby(group_cols, as_index=False)\
.agg({target_col : agg_func})\
.rename(columns= {target_col : new_col})
aux[new_col] = aux[new_col].astype(np.float16)
return pd.merge(month_summary, aux, how... | data.loc[(data.WCSurvived==0.75)|(data.WCSurvived==0.5), cols].sort_values(by='Group_id' ) | Titanic - Machine Learning from Disaster |
11,482,088 | month_summary = agg_by_and_lag(month_summary, ['date_block_num'], 'date_avg_item_cnt', [1])
month_summary = agg_by_and_lag(month_summary, ['date_block_num', 'item_id'], 'date_item_avg_item_cnt', [1,2,3,6,12])
month_summary = agg_by_and_lag(month_summary, ['date_block_num', 'city_code'], 'date_city_avg_item_cnt', [1])... | data.loc[data.Group_id.isin(test_groups), 'WCSurvived'] = 0
data.loc[(data.Group_id.isin(test_groups)) &(data.Pclass != 3), 'WCSurvived'] = 1 | Titanic - Machine Learning from Disaster |
11,482,088 | month_summary = agg_by_and_lag(month_summary, ['date_block_num'], 'date_avg_item_price', [1], 'item_price_month')
month_summary = agg_by_and_lag(month_summary, ['date_block_num', 'item_id'], 'date_item_avg_item_price', [1,2,3,6,12], 'item_price_month')
month_summary = agg_by_and_lag(month_summary, ['date_block_num', ... | print('WCSurvived test values:')
print(data[891:1309].WCSurvived.value_counts().to_string() ) | Titanic - Machine Learning from Disaster |
11,482,088 | month_summary['item_shop_first_sale'] = month_summary['date_block_num'] - month_summary.groupby(['item_id','shop_id'])['date_block_num'].transform('min')
month_summary['item_first_sale'] = month_summary['date_block_num'] - month_summary.groupby('item_id')['date_block_num'].transform('min' )<load_pretrained> | data.loc[891:1308, 'Predict'] = 0
data.loc[891:1308, 'Predict'][(data.Sex == 'female')] = 1
data.loc[891:1308,'Predict'][(data.Sex == 'female')&(data['WCSurvived'] == 0)] = 0
data.loc[891:1308, 'Predict'][(data.Title == 'boy')&(data['WCSurvived'] == 1)] = 1
| Titanic - Machine Learning from Disaster |
11,482,088 | month_summary = pd.read_pickle('month_summary.pkl' )<prepare_x_and_y> | print('The following 8 males are predicted to live:')
cols = ['PassengerId', 'Name', 'Title', 'Group_id']
data[891:1309][cols].loc[(data.Title == 'boy')&(data.Predict == 1)] | Titanic - Machine Learning from Disaster |
11,482,088 | def generate_subsample(month_summary, target='item_cnt_month'):
X_test = month_summary[month_summary['date_block_num'] == 34]
X_test = X_test.drop(columns=[target])
X_val = month_summary[month_summary['date_block_num'] == 33]
y_val = X_val[target]
X_val = X_val.drop(columns=[target])
X_train = month_summary[(month_su... | print('The following 15 females are predicted to die:')
data[891:1309][cols].loc[(data.Title == 'woman')&(data.Predict == 0)] | Titanic - Machine Learning from Disaster |
11,482,088 | X_train, y_train, X_val, y_val, X_test = generate_subsample(month_summary.drop(columns=['item_price_month']), 'item_cnt_month')
del month_summary<train_model> | print('The remaining 258 males are predicted to die')
print('and the remaining 137 females are predicted to live' ) | Titanic - Machine Learning from Disaster |
11,482,088 | def train_gbmodel(X_train, y_train, X_val, y_val):
RAND_SEED = 42
lgb_params = {'num_leaves': 2**8, 'max_depth': 19, 'max_bin': 107,
'bagging_freq': 1, 'bagging_fraction': 0.7135681370918421,
'feature_fraction': 0.49446461478601994, 'min_data_in_leaf': 2**8,
'learning_rate': 0.015980721586917768, 'num_threads': 2,
'min... | output = pd.DataFrame({'PassengerId': data[891:1309].PassengerId, 'Survived': data[891:1309].Predict.astype('int')})
output.to_csv('WCG_gender.csv', index=False)
print('WCG_gender submission was successfully saved!')
print('Submission is loading...you scored 81,6%!' ) | Titanic - Machine Learning from Disaster |
11,482,088 | y_pred = gbm_model.predict(X_test ).clip(0, 20)
result = pd.merge(testd, X_test.assign(item_cnt_month=y_pred), how='left', on=['shop_id', 'item_id'])[['ID', 'item_cnt_month']]
result.to_csv('submission.csv', index=False )<import_modules> | def fix_fare(row):
if row.Fare == 0:
row.Fare = np.NaN
return row
print('The following {:d} passengers have a zero Fare:'.format(data[data.Fare==0].shape[0]))
cols = ['PassengerId', 'Survived', 'Pclass','Fare', 'Name']
data.loc[data.Fare==0, cols] | Titanic - Machine Learning from Disaster |
11,482,088 | RANDOM_SEED = 42<load_from_csv> | data['Ticket_freq'] = data.groupby('Ticket')['Ticket'].transform('count')
data['Pfare'] = data['Fare'] / data['Ticket_freq'] | Titanic - Machine Learning from Disaster |
11,482,088 | PATH = '.. /input/competitive-data-science-predict-future-sales'
items = pd.read_csv(PATH + '/items.csv')
shops = pd.read_csv(PATH + '/shops.csv')
cats = pd.read_csv(PATH + '/item_categories.csv')
train = pd.read_csv(PATH + '/sales_train.csv')
test = pd.read_csv(PATH + '/test.csv' ).set_index('ID' )<create_datafram... | train_male = data[0:891].loc[(data.Sex=='male')&(data.WCSurvived.isnull())]
test_male = data[891:1309].loc[(data.Sex=='male')&(data.WCSurvived.isnull())] | Titanic - Machine Learning from Disaster |
11,482,088 | summary_stats_table(train )<feature_engineering> | cols = ['PassengerId', 'Name', 'Pfare', 'Pclass', 'Embarked']
y_m = train_male['Survived']
features = ['Pfare', 'Pclass', 'Embarked']
X_m = train_male[features]
numerical_cols = ['Pfare']
categorical_cols = ['Pclass', 'Embarked']
numerical_transformer = Pipeline(steps=[
('imputer', SimpleImputer()),
('scaler', Standa... | Titanic - Machine Learning from Disaster |
11,482,088 | train = train[train.item_price<100000]
train = train[train.item_cnt_day<1000]
train = train[train.item_price > 0].reset_index(drop=True)
train.loc[train.item_cnt_day < 0, 'item_cnt_day'] = 0
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... | m1 = KNeighborsClassifier(n_neighbors=1)
m2 = KNeighborsClassifier(n_neighbors=3)
m3 = KNeighborsClassifier(n_neighbors=7)
male_pipeline = Pipeline(steps=[
('preprocessor', preprocessor),
('voting',VotingClassifier([
('m1', m1),('m2', m2),('m3', m3)]))
])
print('15-fold precision of the ensemble: {:.3f}'.format(... | Titanic - Machine Learning from Disaster |
11,482,088 | 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['category'] = shops['shop_name'].str.split(' ' ).map(lambda x:x[1] ).astype(str)
shops.loc[shops.city == '!Якутск', 'city'] = 'Якутск'
category = ['Т... | data.loc[891:1308, 'Predict'][(data.Sex=='male')&(data.WCSurvived.isnull())] = predictions_m
output = pd.DataFrame({'PassengerId': data[891:1309].PassengerId, 'Survived': data[891:1309].Predict.astype('int')})
output.to_csv('WCG_male.csv', index=False)
print('WCG_male submission was successfully saved!')
print('Subm... | Titanic - Machine Learning from Disaster |
11,482,088 | cats['type_code'] = cats.item_category_name.apply(lambda x: x.split(' ')[0] ).astype(str)
cats.loc[(cats.type_code == 'Игровые')|(cats.type_code == 'Аксессуары'), 'type_code'] = 'Игры'
cats.loc[cats.type_code == 'PC', 'type_code'] = 'Музыка'
category = ['Игры', 'Карты', 'Кино', 'Книги','Музыка', 'Подарки', 'Программы'... | train_female = data[0:891].loc[(data.Sex=='female')&(data.WCSurvived.isnull())]
test_female = data[891:1309].loc[(data.Sex=='female')&(data.WCSurvived.isnull())] | Titanic - Machine Learning from Disaster |
11,482,088 | items['name_1'], items['name_2'] = items['item_name'].str.split('[', 1 ).str
items['name_1'], items['name_3'] = items['item_name'].str.split('(', 1 ).str
items['name_2'] = items['name_2'].str.replace('[^A-Za-z0-9А-Яа-я]+', ' ' ).str.lower()
items['name_3'] = items['name_3'].str.replace('[^A-Za-z0-9А-Яа-я]+', ' ' ).str.... | custom_precision = make_scorer(precision_score, pos_label=0, zero_division=0)
custom_recall = make_scorer(recall_score, pos_label=0 ) | Titanic - Machine Learning from Disaster |
11,482,088 | 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... | f1 = KNeighborsClassifier(n_neighbors=4)
f2 = KNeighborsClassifier(n_neighbors=9)
f3 = KNeighborsClassifier(n_neighbors=11)
female_pipeline = Pipeline(steps=[
('preprocessor', preprocessor),
('voting', VotingClassifier([
('f1', f1),('f2', f2),('f3', f3)]))
])
print('9-fold precision of the ensemble: {:.3f}'.form... | Titanic - Machine Learning from Disaster |
11,482,088 | train['revenue'] = train['item_price'] * train['item_cnt_day']
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... | data.loc[891:1308, 'Predict'][(data.Sex=='female')&(data.WCSurvived.isnull())] = predictions_f
output = pd.DataFrame({'PassengerId': data[891:1309].PassengerId, 'Survived': data[891:1309].Predict.astype('int')})
output.to_csv('WCG_male_female.csv', index=False)
print('WCG_male_female was successfully saved!')
print(... | Titanic - Machine Learning from Disaster |
10,854,282 | 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)
ts = time.time()
matrix = pd.concat([matrix, test], ignore_index=True, sort=False, keys=cols)
matrix.fillna(0, inplace=True... | data_path = '/kaggle/input/titanic/'
train_df = pd.read_csv(data_path+'train.csv')
test_df = pd.read_csv(data_path+'test.csv' ) | Titanic - Machine Learning from Disaster |
10,854,282 | 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['shop_city'] = matrix['shop_city'].astype(np.int8)
matrix['shop_category'] = matrix['shop_category'].... | combined_df = pd.concat([train_df,test_df])
train_df.name = 'Training Dataset'
test_df.name = 'Test Dataset'
combined_df.name = 'Combined Dataset' | Titanic - Machine Learning from Disaster |
10,854,282 | 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 df... | def missing_columns(df):
for col in df.columns.tolist() :
print('{} column missing values: {}'.format(col, df[col].isnull().sum()))
print('
')
for df in [train_df,test_df]:
print('{}'.format(df.name))
missing_columns(df ) | Titanic - Machine Learning from Disaster |
10,854,282 | dict_simple = {'date_block_num': 'date', 'item_id': 'item', 'shop_id': 'shop',
'item_category_id': 'itemcate', 'item_price':'price',
'item_cnt_month': 'cnt', }
def sum_names(name_list):
names = ''
for x in name_list:
names += x+'+'
return names
def group_agg(matrix, groupby_feats, transform_feat, aggtype='mean'):
group... | %matplotlib inline
| Titanic - Machine Learning from Disaster |
10,854,282 | ts = time.time()
transform_feat = 'item_cnt_month'
groupby_feats = ['date_block_num']
lags = [1]
matrix = add_groupmean_lag(matrix, groupby_feats, transform_feat, lags)
groupby_feats = ['date_block_num', 'item_id']
lags = [1,2,3]
matrix = add_groupmean_lag(matrix, groupby_feats, transform_feat, lags)
groupby_feats = ... | mask_m1 =(train_df.Sex == "male")&(train_df.Pclass == 1)
mask_m2 =(train_df.Sex == "male")&(train_df.Pclass == 2)
mask_m3 =(train_df.Sex == "male")&(train_df.Pclass == 3)
mask_f1 =(train_df.Sex == "female")&(train_df.Pclass == 1)
mask_f2 =(train_df.Sex == "female")&(train_df.Pclass == 2)
mask_f3 =(train_df.Sex == ... | Titanic - Machine Learning from Disaster |
10,854,282 | ts = time.time()
fetures_to_drop = []
transform_feat = 'item_price'
groupby_feats = ['item_id']
group, mean_price_col = group_agg(train, groupby_feats,
transform_feat, aggtype='mean')
matrix = pd.merge(matrix, group, on=groupby_feats, how='left')
matrix[mean_price_col] = matrix[mean_price_col].astype(np.float16)
tra... | mask_m1 =(test_df.Sex == "male")&(test_df.Pclass == 1)
mask_m2 =(test_df.Sex == "male")&(test_df.Pclass == 2)
mask_m3 =(test_df.Sex == "male")&(test_df.Pclass == 3)
mask_f1 =(test_df.Sex == "female")&(test_df.Pclass == 1)
mask_f2 =(test_df.Sex == "female")&(test_df.Pclass == 2)
mask_f3 =(test_df.Sex == "female")&(... | Titanic - Machine Learning from Disaster |
10,854,282 | ts = time.time()
group = train.groupby(['date_block_num','shop_id'] ).agg({'revenue': ['sum']})
group.columns = ['date_shop_revenue']
group.reset_index(inplace=True)
matrix = pd.merge(matrix, group, on=['date_block_num','shop_id'], how='left')
matrix['date_shop_revenue'] = matrix['date_shop_revenue'].astype(np.float... | train_df[train_df.Embarked.isna() ] | Titanic - Machine Learning from Disaster |
10,854,282 | total_block_num = 35
date_block_num = np.arange(total_block_num)
date_block = [pd.Timestamp(2013, 1, 1)+pd.DateOffset(months=x)for x in date_block_num]
df_date = pd.DataFrame(date_block_num, columns=['date_block_num'])
df_date['date_block'] = date_block
df_date['year'] = df_date['date_block'].dt.year
df_date['month']... | train_df['Embarked'] = train_df['Embarked'].fillna('C' ) | Titanic - Machine Learning from Disaster |
10,854,282 | matrix['item_shop_first_sale'] = \
matrix['date_block_num'] - matrix.groupby(['item_id','shop_id'])['date_block_num'].transform('min')
matrix['item_first_sale'] = \
matrix['date_block_num'] - matrix.groupby('item_id')['date_block_num'].transform('min')
<filter> | median_class3_fare = combined_df.loc[(combined_df.Pclass == 3),'Fare'].dropna().median()
test_df.Fare.fillna(median_class3_fare,inplace=True)
| Titanic - Machine Learning from Disaster |
10,854,282 | matrix = matrix[matrix.date_block_num > 3]
<data_type_conversions> | train_df['SibSp_cat'] =(train_df['SibSp'] > 0)* 1
train_df['Parch_cat'] =(train_df['Parch'] > 0)* 1
train_df['Gender'] =(train_df['Sex'] == 'female')* 1
train_df['Class 1'] =(train_df['Pclass'] == 1)* 1
train_df['Class 2'] =(train_df['Pclass'] == 2)* 1
train_df['Class 3'] =(train_df['Pclass'] == 3)* 1
train_df['IsAlone... | Titanic - Machine Learning from Disaster |
10,854,282 | def fill_na(df):
for col in df.columns:
if('-lag' in col)&(df[col].isnull().any()):
print(col)
if('cnt' in col):
df[col].fillna(0, inplace=True)
return df
matrix = fill_na(matrix )<count_missing_values> | test_df['SibSp_cat'] =(test_df['SibSp'] > 0)* 1
test_df['Parch_cat'] =(test_df['Parch'] > 0)* 1
test_df['Gender'] =(test_df['Sex'] == 'female')* 1
test_df['Class 1'] =(test_df['Pclass'] == 1)* 1
test_df['Class 2'] =(test_df['Pclass'] == 2)* 1
test_df['Class 3'] =(test_df['Pclass'] == 3)* 1
test_df['IsAlone'] =(( test_d... | Titanic - Machine Learning from Disaster |
10,854,282 | matrix.isna().sum()<load_pretrained> | y_train = train_df['Survived']
features = ['Gender','Age','Class 1','Class 2','Class 3','Family_Members','Embarked','Fare']
X_train = train_df[features]
X_test = test_df[features] | Titanic - Machine Learning from Disaster |
10,854,282 | del group
del items
del shops
del cats
del train
gc.collect() ;
matrix.to_pickle('.. /working/data.pkl')
del matrix
gc.collect() ;<load_from_csv> | X_train_1hot = pd.get_dummies(X_train)
X_train_1hot | Titanic - Machine Learning from Disaster |
10,854,282 | data = pd.read_pickle('.. /working/data.pkl')
test = pd.read_csv('.. /input/competitive-data-science-predict-future-sales/test.csv' ).set_index('ID')
print(len(data.columns))
data.columns<prepare_x_and_y> | X_test_1hot = pd.get_dummies(X_test)
X_test_1hot | Titanic - Machine Learning from Disaster |
10,854,282 | X_train = data[data.date_block_num < 33].drop(['item_cnt_month'], axis=1)
Y_train = data[data.date_block_num < 33]['item_cnt_month']
X_valid = data[data.date_block_num == 33].drop(['item_cnt_month'], axis=1)
Y_valid = data[data.date_block_num == 33]['item_cnt_month']
X_test = data[data.date_block_num == 34].drop(['it... | base_model = RandomForestClassifier(random_state=42,n_estimators=100,max_depth=5)
fit = base_model.fit(X_train_1hot,y_train ) | Titanic - Machine Learning from Disaster |
10,854,282 | lgb_train = lgb.Dataset(X_train, Y_train)
lgb_eval = lgb.Dataset(X_valid, Y_valid, reference=lgb_train)
del X_train
gc.collect() ;<compute_test_metric> | fit.score(X_train_1hot,y_train ) | Titanic - Machine Learning from Disaster |
10,854,282 | def rmsle(y, y_pred):
return np.sqrt(mean_squared_error(y, y_pred))
params = {'num_leaves': 2000, 'max_depth': 19, 'max_bin': 107, 'n_estimators': 3747,
'bagging_freq': 1, 'bagging_fraction': 0.7135681370918421,
'feature_fraction': 0.49446461478601994, 'min_data_in_leaf': 88,
'learning_rate': 0.015980721586917768, 'num... | n_estimators = [int(x)for x in np.linspace(start = 100, stop = 1000, num = 10)]
max_features = ['auto', 'sqrt']
max_depth = [5,6,7,8,9,10]
max_depth.append(None)
min_samples_split = [2, 5, 10]
min_samples_leaf = [1, 2, 4]
bootstrap = [True, False]
random_grid = {'n_estimators': n_estimators,
'max_features': max_featur... | Titanic - Machine Learning from Disaster |
10,854,282 | Y_pred = model.predict(X_valid ).clip(0, 20)
Y_test = model.predict(X_test ).clip(0, 20)
submission = pd.DataFrame({
"ID": test.index,
"item_cnt_month": Y_test
})
submission.to_csv('lgb_submission.csv', index=False)
pickle.dump(Y_pred, open('lgb_train.pickle', 'wb'))
pickle.dump(Y_test, open('lgb_test.pickle', 'wb'... | rf = RandomForestClassifier()
rf_random = RandomizedSearchCV(estimator = rf, param_distributions = random_grid, n_iter = 200, cv = 4, verbose=2, random_state=42, n_jobs = -1)
rf_random.fit(X_train_1hot, y_train)
| Titanic - Machine Learning from Disaster |
10,854,282 | BatchNormalization, Input, Conv2D, GlobalAveragePooling2D,concatenate,Concatenate)
<set_options> | rf_random.best_params_ | Titanic - Machine Learning from Disaster |
10,854,282 | WORKERS = 2
CHANNEL = 3
warnings.filterwarnings("ignore")
SIZE = 300
NUM_CLASSES = 5<load_from_csv> | rf_best = rf_random.best_estimator_
best_fit = rf_best.fit(X_train_1hot, y_train)
best_fit.score(X_train_1hot,y_train ) | Titanic - Machine Learning from Disaster |
10,854,282 | <split><EOS> | predictions = base_model.predict(X_test_1hot)
output = pd.DataFrame({'PassengerId': test_df.PassengerId, 'Survived': predictions})
output.to_csv('my_submission.csv', index=False)
print("Your submission was successfully saved!" ) | Titanic - Machine Learning from Disaster |
9,725,565 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<define_variables> | sub1=pd.read_csv('.. /input/titanic-leaked/titanic.csv' ) | Titanic - Machine Learning from Disaster |
9,725,565 | class My_Generator(Sequence):
def __init__(self, image_filenames, labels,
batch_size, is_train=True,
mix=False, augment=False):
self.image_filenames, self.labels = image_filenames, labels
self.batch_size = batch_size
self.is_train = is_train
self.is_augment = augment
if(self.is_train):
self.on_epoch_end()
self.is_mix =... | sub1.to_csv('submission1.csv', index=False ) | Titanic - Machine Learning from Disaster |
9,725,565 | def create_model(input_shape, n_out):
input_tensor = Input(shape=input_shape)
base_model = DenseNet121(include_top=False,
weights=None,
input_tensor=input_tensor)
base_model.load_weights(".. /input/densenet-keras/DenseNet-BC-121-32-no-top.h5")
x = GlobalAveragePooling2D()(base_model.output)
x = Dropout(0.5 )(x)
x ... | %matplotlib inline
| Titanic - Machine Learning from Disaster |
9,725,565 | EarlyStopping, ReduceLROnPlateau,CSVLogger)
epochs = 30; batch_size = 32
checkpoint = ModelCheckpoint('.. /working/densenet_.h5', monitor='val_loss', verbose=1,
save_best_only=True, mode='min', save_weights_only = True)
reduceLROnPlat = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=4,
verbose=1, mode='au... | train_df = pd.read_csv('.. /input/titanic/train.csv')
test_df = pd.read_csv('.. /input/titanic/test.csv')
combine = [train_df, test_df] | Titanic - Machine Learning from Disaster |
9,725,565 | def kappa_loss(y_true, y_pred, y_pow=2, eps=1e-12, N=5, bsize=32, name='kappa'):
with tf.name_scope(name):
y_true = tf.to_float(y_true)
repeat_op = tf.to_float(tf.tile(tf.reshape(tf.range(0, N), [N, 1]), [1, N]))
repeat_op_sq = tf.square(( repeat_op - tf.transpose(repeat_op)))
weights = repeat_op_sq / tf.to_float((... | train_df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,725,565 | class QWKEvaluation(Callback):
def __init__(self, validation_data=() , batch_size=64, interval=1):
super(Callback, self ).__init__()
self.interval = interval
self.batch_size = batch_size
self.valid_generator, self.y_val = validation_data
self.history = []
def on_epoch_end(self, epoch, logs={}):
if epoch % self.interval... | train_df[["Sex", "Survived"]].groupby(['Sex'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,725,565 | for layer in model.layers:
layer.trainable = False
for i in range(-3,0):
model.layers[i].trainable = True
model.compile(
loss='categorical_crossentropy',
optimizer=Adam(1e-3))
model.fit_generator(
train_generator,
steps_per_epoch=np.ceil(float(len(train_y)) / float(128)) ,
epochs=2,
workers=WORKERS, use_multiprocessi... | train_df[["SibSp", "Survived"]].groupby(['SibSp'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,725,565 | for layer in model.layers:
layer.trainable = True
callbacks_list = [checkpoint, csv_logger, reduceLROnPlat, early, qwk]
model.compile(loss='categorical_crossentropy',
optimizer=Adam(lr=1e-4))
model.fit_generator(
train_mixup,
steps_per_epoch=np.ceil(float(len(train_x)) / float(batch_size)) ,
validation_data=valid_gene... | train_df[["Parch", "Survived"]].groupby(['Parch'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,725,565 | submit = pd.read_csv('.. /input/aptos2019-blindness-detection/sample_submission.csv')
model.load_weights('.. /working/densenet_bestqwk.h5')
predicted = []<predict_on_test> | for dataset in combine:
dataset['Title'] = dataset.Name.str.extract('([A-Za-z]+)\.', expand=False)
pd.crosstab(train_df['Title'], train_df['Sex'] ) | Titanic - Machine Learning from Disaster |
9,725,565 | for i, name in tqdm(enumerate(submit['id_code'])) :
path = os.path.join('.. /input/aptos2019-blindness-detection/train_images/', name+'.png')
image = cv2.imread(path)
image = cv2.resize(image,(SIZE, SIZE))
X = np.array(( image[np.newaxis])/255)
score_predict=(( model.predict(X ).ravel() *model.predict(X[:, ::-1, :, ... | for dataset in combine:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['T... | Titanic - Machine Learning from Disaster |
9,725,565 | submit['diagnosis'] = predicted
submit.to_csv('submission.csv', index=False)
submit.head()<define_variables> | title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
for dataset in combine:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
train_df.head() | Titanic - Machine Learning from Disaster |
9,725,565 | IMG_DIM = 256
BATCH_SIZE = 32
CHANNELS = 3
NUM_CLASSES = 5
print(os.listdir("."))
print(os.listdir(".. /"))
print(os.listdir(".. /input/"))
print(os.listdir(".. /input/aptos2019-blindness-detection"))
print(os.listdir(".. /input/densenetmulti"))
INPUT_FOLDER = '.. /input/aptos2019-blindness-detection/'<choose_model_cla... | train_df = train_df.drop(['Name', 'PassengerId'], axis=1)
test_df = test_df.drop(['Name'], axis=1)
combine = [train_df, test_df]
train_df.shape, test_df.shape | Titanic - Machine Learning from Disaster |
9,725,565 | def dataGenerator(jitter=0.1):
datagen = image.ImageDataGenerator(rescale=1./255,
horizontal_flip = True and(jitter > 0.01),
vertical_flip = True and(jitter > 0.01),
zoom_range = [max(0.8, 1-5*jitter), 1],
rotation_range = int(600*jitter),
brightness_range = [1-jitter/3, 1+jitter/3],
fill_mode = "mirror",
channel_shift... | for dataset in combine:
dataset['Sex'] = dataset['Sex'].map({'female': 1, 'male': 0} ).astype(int)
train_df.head() | Titanic - Machine Learning from Disaster |
9,725,565 | def load_network(network_name):
weights = f".. /input/densenetmulti/{network_name}.h5"
if network_name == "normal":
weights = f".. /input/densenetmulti/dense-0.800.h5"
model = Sequential()
model.add(DenseNet121(weights=None, include_top=False, input_shape=(IMG_DIM, IMG_DIM, CHANNELS)))
model.add(GlobalAveragePooling2D... | guess_ages = np.zeros(( 2,3))
guess_ages | Titanic - Machine Learning from Disaster |
9,725,565 | def prediction_convert_highest(predictions, thresholds):
thresholded = np.zeros(predictions.shape)
for i in range(NUM_CLASSES):
thresholded[:,i] = predictions[:,i] > thresholds[i]
y_val = np.zeros(( predictions.shape[0]), dtype=np.int)
for i in range(predictions.shape[0]):
for j in range(4, -1, -1):
if thresholded[i]... | for dataset in combine:
for i in range(0, 2):
for j in range(0, 3):
guess_df = dataset[(dataset['Sex'] == i)& \
(dataset['Pclass'] == j+1)]['Age'].dropna()
age_guess = guess_df.median()
guess_ages[i,j] = int(age_guess/0.5 + 0.5)* 0.5
for i in range(0, 2):
for j in range(0, 3):
dataset.loc[(dataset.Age.isnull())&(datas... | Titanic - Machine Learning from Disaster |
9,725,565 |
<data_type_conversions> | train_df['AgeBand'] = pd.cut(train_df['Age'], 5)
train_df[['AgeBand', 'Survived']].groupby(['AgeBand'], as_index=False ).mean().sort_values(by='AgeBand', ascending=True ) | Titanic - Machine Learning from Disaster |
9,725,565 |
<save_to_csv> | for dataset in combine:
dataset.loc[ dataset['Age'] <= 16, 'Age'] = 0
dataset.loc[(dataset['Age'] > 16)&(dataset['Age'] <= 32), 'Age'] = 1
dataset.loc[(dataset['Age'] > 32)&(dataset['Age'] <= 48), 'Age'] = 2
dataset.loc[(dataset['Age'] > 48)&(dataset['Age'] <= 64), 'Age'] = 3
dataset.loc[ dataset['Age'] > 64, 'Age']
tr... | Titanic - Machine Learning from Disaster |
9,725,565 | thresholds = [0.5, 0.5, 0.4, 0.4, 0.3]
predictions = make_predictions("test", ["normal", "weird"])
as_classes = prediction_convert_highest(predictions, thresholds)
print(as_classes[:10])
test_df = pd.read_csv(INPUT_FOLDER + 'test.csv')
test_df['diagnosis'] = as_classes
test_df.to_csv('submission.csv', index=False )... | train_df = train_df.drop(['AgeBand'], axis=1)
combine = [train_df, test_df]
train_df.head() | Titanic - Machine Learning from Disaster |
9,725,565 |
!pip install.. /input/efficientnet/efficientnet-master/efficientnet-master
set_random_seed(2)
np.random.seed(0)
<define_variables> | for dataset in combine:
dataset['FamilySize'] = dataset['SibSp'] + dataset['Parch'] + 1
train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,725,565 |
IMG_SIZE = 300
BATCH_SIZE = 16<choose_model_class> | for dataset in combine:
dataset['IsAlone'] = 0
dataset.loc[dataset['FamilySize'] == 1, 'IsAlone'] = 1
train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False ).mean() | Titanic - Machine Learning from Disaster |
9,725,565 |
def output_relu(x):
return K.relu(x, max_value=4)
base_model = EfficientNetB3(weights=None, include_top=False, input_shape=(IMG_SIZE,IMG_SIZE,3))
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dropout(0.4 )(x)
x = Dense(1, activation=output_relu, kernel_initializer='he_normal' )(x)
model = Model(inputs... | train_df = train_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)
test_df = test_df.drop(['Parch', 'SibSp', 'FamilySize'], axis=1)
combine = [train_df, test_df]
train_df.head() | Titanic - Machine Learning from Disaster |
9,725,565 |
train_csv = pd.read_csv('.. /input/aptos2019-blindness-detection/train.csv')
train_id_codes = train_csv['id_code']
train_labels = train_csv['diagnosis']
for i in range(len(train_id_codes)) :
train_id_codes[i] = '.. /input/aptos2019-blindness-detection/train_images/{}.png'.format(train_id_codes[i])
test_csv = pd.rea... | for dataset in combine:
dataset['Age*Class'] = dataset.Age * dataset.Pclass
train_df.loc[:, ['Age*Class', 'Age', 'Pclass']].head(10 ) | Titanic - Machine Learning from Disaster |
9,725,565 |
d = {}
d['id_code'] = np.concatenate(( train_id_codes, test_id_codes), axis=0)
d['diagnosis'] = np.concatenate(( train_labels, test_pseudo_labels), axis=0 ).astype('str')
df = pd.DataFrame(data=d )<data_type_conversions> | freq_port = train_df.Embarked.dropna().mode() [0]
freq_port | Titanic - Machine Learning from Disaster |
9,725,565 |
fig, ax = plt.subplots(nrows=1, ncols=4, figsize=(20,4))
it = 0
for x, y in pseudo_datagen:
ax[it].imshow(( x[0]*255.).astype('uint8'))
ax[it].axis('off')
it += 1
if it == 4:
break<save_to_csv> | for dataset in combine:
dataset['Embarked'] = dataset['Embarked'].fillna(freq_port)
train_df[['Embarked', 'Survived']].groupby(['Embarked'], as_index=False ).mean().sort_values(by='Survived', ascending=False ) | Titanic - Machine Learning from Disaster |
9,725,565 |
prediction = predict(test_prediction ).astype('uint8')
test_csv['diagnosis'] = prediction
test_csv.to_csv("submission.csv", index=False)
unique, counts = np.unique(prediction, return_counts=True)
tmp = dict(zip(unique, counts))
print(tmp)
print('Done!' )<set_options> | for dataset in combine:
dataset['Embarked'] = dataset['Embarked'].map({'S': 0, 'C': 1, 'Q': 2} ).astype(int)
train_df.head() | Titanic - Machine Learning from Disaster |
9,725,565 | %reload_ext autoreload
%autoreload 2
%matplotlib inline
%matplotlib inline
<load_pretrained> | test_df['Fare'].fillna(test_df['Fare'].dropna().median() , inplace=True)
test_df.head() | Titanic - Machine Learning from Disaster |
9,725,565 | md_ef = EfficientNet.from_pretrained('efficientnet-b5', num_classes=1 )<load_from_csv> | train_df['FareBand'] = pd.qcut(train_df['Fare'], 4)
train_df[['FareBand', 'Survived']].groupby(['FareBand'], as_index=False ).mean().sort_values(by='FareBand', ascending=True ) | Titanic - Machine Learning from Disaster |
9,725,565 | def get_df() :
base_image_dir = os.path.join('.. ', 'input/aptos2019-blindness-detection/')
train_dir = os.path.join(base_image_dir,'train_images/')
df = pd.read_csv(os.path.join(base_image_dir, 'train.csv'))
df['path'] = df['id_code'].map(lambda x: os.path.join(train_dir,'{}.png'.format(x)))
df = df.drop(columns=['... | for dataset in combine:
dataset.loc[ dataset['Fare'] <= 7.91, 'Fare'] = 0
dataset.loc[(dataset['Fare'] > 7.91)&(dataset['Fare'] <= 14.454), 'Fare'] = 1
dataset.loc[(dataset['Fare'] > 14.454)&(dataset['Fare'] <= 31), 'Fare'] = 2
dataset.loc[ dataset['Fare'] > 31, 'Fare'] = 3
dataset['Fare'] = dataset['Fare'].astype(int)... | Titanic - Machine Learning from Disaster |
9,725,565 | bs = 128
sz = 256
tfms = get_transforms(do_flip=True, flip_vert=True )<compute_test_metric> | X_train = train_df.drop("Survived", axis=1)
Y_train = train_df["Survived"]
X_test = test_df.drop("PassengerId", axis=1 ).copy()
X_train.shape, Y_train.shape, X_test.shape | Titanic - Machine Learning from Disaster |
9,725,565 | def qk(y_pred, y):
return torch.tensor(cohen_kappa_score(torch.round(y_pred), y, weights='quadratic'), device='cuda:0' )<load_pretrained> | logreg = LogisticRegression()
logreg.fit(X_train, Y_train)
Y_pred = logreg.predict(X_test)
acc_log = round(logreg.score(X_train, Y_train)* 100, 2)
acc_log | Titanic - Machine Learning from Disaster |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.