kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
8,370,213 | DF_all['item_shop_first_sale'] = \
DF_all['date_block_num'] - DF_all.groupby(['item_id','shop_id'])['date_block_num'].transform('min')
DF_all['item_first_sale'] = \
DF_all['date_block_num'] - DF_all.groupby('item_id')['date_block_num'].transform('min')
DF_all<count_missing_values> | train_data[['FareBand', 'Survived']].groupby('FareBand', as_index=False ).mean() | Titanic - Machine Learning from Disaster |
8,370,213 | DF_all.isnull().any()<correct_missing_values> | train_data.loc[ train_data['Fare'] <= 10, 'Fare'] = 0
train_data.loc[(train_data['Fare'] > 10)&(train_data['Fare'] <= 40), 'Fare'] = 1
train_data.loc[train_data['Fare'] > 40 , 'Fare'] = 2 | Titanic - Machine Learning from Disaster |
8,370,213 | DF_all = fill_na_test(DF_all)
DF_all.isnull().any()<load_pretrained> | train_data.drop(labels='FareBand', axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
8,370,213 | DF_all.to_pickle('dataset.pkl' )<drop_column> | test_data = pd.read_csv(".. /input/titanic/test.csv" ) | Titanic - Machine Learning from Disaster |
8,370,213 | del DF_all
del temp
del DF_sales
del DF_items
del DF_item_cat
del DF_shops
gc.collect()<load_pretrained> | test_data.drop(columns=['Name', 'Ticket', 'Cabin'], axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
8,370,213 | data = pd.read_pickle('dataset.pkl' )<create_dataframe> | test_data['Sex'] = test_data['Sex'].map({'male':0,'female':1} ) | Titanic - Machine Learning from Disaster |
8,370,213 | data = data[[
'date_block_num', 'shop_id', 'item_id', 'shop_city', 'shop_cat',
'item_category_id', 'item_sub_cat_1', 'item_cnt_month',
'item_cnt_month_lag_1', 'item_cnt_month_lag_2', 'item_cnt_month_lag_3',
'avg_month_lag_1', 'avg_month_lag_2', 'avg_month_lag_3',
'avg_item_month_lag_1', 'avg_item_month_lag_2', 'avg_ite... | test_data['Embarked'] = test_data['Embarked'].map({'S':0,'C':1,'Q':2} ) | Titanic - Machine Learning from Disaster |
8,370,213 | 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... | test_data['Fare'].fillna(value=test_data['Fare'].median() ,axis=0, inplace=True ) | Titanic - Machine Learning from Disaster |
8,370,213 | lgb_train = lgb.Dataset(X_train, Y_train)
lgb_eval = lgb.Dataset(X_valid, Y_valid, reference=lgb_train )<find_best_params> | test_data.fillna(value=test_data['Age'].mean() , axis=0, inplace=True ) | Titanic - Machine Learning from Disaster |
8,370,213 | def objective(trial):
param = {
"objective": "regression",
"metric": "rmse",
"verbosity": -1,
"boosting_type": "gbdt",
"lambda_l1": trial.suggest_float("lambda_l1", 1e-8, 10.0, log=True),
"lambda_l2": trial.suggest_float("lambda_l2", 1e-8, 10.0, log=True),
"num_leaves": trial.suggest_int("num_leaves", 2, 256),
"feature... | test_data.loc[ test_data['Age'] <= 18, 'Age'] = 0
test_data.loc[(test_data['Age'] > 18)&(test_data['Age'] <= 44), 'Age'] = 1
test_data.loc[(test_data['Age'] > 44)&(test_data['Age'] <= 53), 'Age'] = 2
test_data.loc[(test_data['Age'] > 53)&(test_data['Age'] <= 62), 'Age'] = 3
test_data.loc[ test_data['Age'] > 62, 'Age'] ... | Titanic - Machine Learning from Disaster |
8,370,213 | study = optuna.create_study(direction='minimize')
study.optimize(objective, n_trials=5)
print('Number of finished trials:', len(study.trials))
print('Best trial:', study.best_trial.params )<train_model> | test_data.loc[ test_data['Fare'] <= 10, 'Fare'] = 0
test_data.loc[(test_data['Fare'] > 10)&(test_data['Fare'] <= 40), 'Fare'] = 1
test_data.loc[test_data['Fare'] > 40 , 'Fare'] = 2 | Titanic - Machine Learning from Disaster |
8,370,213 | best_params = study.best_trial.params
print(f'Best trial parameters
{best_params}' )<find_best_params> | submission = pd.read_csv('.. /input/titanic/gender_submission.csv' ) | Titanic - Machine Learning from Disaster |
8,370,213 | x = {"objective": "regression",
"metric" : "rmse",
"verbosity": -1,
"boosting_type": "gbdt"}
best_params.update(x)
best_params<train_model> | features = ["Pclass", "Sex", "Age","Fare","SibSp","Parch","Embarked"]
targets = ["Survived"]
X_train, X_test = train_data[features], test_data[features]
y_train, y_test = train_data[targets],submission[targets] | Titanic - Machine Learning from Disaster |
8,370,213 | evals_result = {}
model = lgb.train(best_params,
lgb_train,
valid_sets=[lgb_train,lgb_eval],
evals_result=evals_result,
early_stopping_rounds=30,
verbose_eval=1,
)<predict_on_test> | import statsmodels.api as sm
from sklearn.linear_model import LogisticRegression
from sklearn import metrics | Titanic - Machine Learning from Disaster |
8,370,213 | y_pred = model.predict(X_valid)
rmsle(Y_valid, y_pred )<predict_on_test> | lr = LogisticRegression()
model = lr.fit(X_train,y_train)
predictions_lr = model.predict(X_test ) | Titanic - Machine Learning from Disaster |
8,370,213 | Y_test = model.predict(X_test ).clip(0, 20)
submission = pd.DataFrame({
"ID": DF_test.index,
"item_cnt_month": Y_test
})
submission.head(10 )<save_to_csv> | predictions_lr = model.predict(X_test ) | Titanic - Machine Learning from Disaster |
8,370,213 | submission.to_csv('submission.csv', index=False )<save_to_csv> | print(pd.crosstab(predictions_lr,submission['Survived'],margins=True,rownames=['Previsto'],colnames=[' Real']))
print(metrics.classification_report(y_test,predictions_lr)) | Titanic - Machine Learning from Disaster |
8,370,213 | submission.to_csv('submission.csv', index=False )<set_options> | output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions_lr} ) | Titanic - Machine Learning from Disaster |
8,370,213 | warnings.filterwarnings('ignore')
<data_type_conversions> | output = output.to_csv("My_submission_Logistic.csv", index=False ) | Titanic - Machine Learning from Disaster |
8,370,213 | def downcast_dtypes(df):
float_cols = [c for c in df if df[c].dtype == "float64"]
int_cols = [c for c in df if df[c].dtype == "int64"]
df[float_cols] = df[float_cols].astype(np.float32)
df[int_cols] = df[int_cols].astype(np.int32)
return df<load_from_csv> | from sklearn.neighbors import KNeighborsClassifier | Titanic - Machine Learning from Disaster |
8,370,213 | sales = pd.read_csv('.. /input/competitive-data-science-predict-future-sales/sales_train.csv')
shops = pd.read_csv('.. /input/competitive-data-science-predict-future-sales/shops.csv')
items = pd.read_csv('.. /input/competitive-data-science-predict-future-sales/items.csv')
items_categories = pd.read_csv('.. /input/co... | from sklearn.neighbors import KNeighborsClassifier | Titanic - Machine Learning from Disaster |
8,370,213 | test_block = sales['date_block_num'].max() + 1
test_data['date_block_num'] = test_block
test_data = test_data.drop(columns=['ID'])
test_data.head()<merge> | from sklearn.neighbors import KNeighborsClassifier | Titanic - Machine Learning from Disaster |
8,370,213 | gb = sales.groupby(index_cols, as_index=False)['item_cnt_day'].sum()
gb = gb.rename(columns={'item_cnt_day': 'target'})
all_data = pd.merge(grid, gb, how='left', on=index_cols ).fillna(0)
gb = sales.groupby(['shop_id', 'date_block_num'], as_index=False)['item_cnt_day'].sum()
gb = gb.rename(columns={'item_cnt_day': 't... | def KNN_1(neighbors):
neighbors = range(1,neighbors+1,1)
print("Para p = 1")
for i in neighbors:
KNN = KNeighborsClassifier(n_neighbors=i,p=1)
model = KNN.fit(X_train,np.ravel(y_train))
results_KNN = model.predict(test_data[features])
print("Para uma quantidade de vizinhos de:", i,"a precisão do modelo foi de ",rou... | Titanic - Machine Learning from Disaster |
8,370,213 | cols_to_rename = list(all_data.columns.difference(index_cols))
shift_range = [1, 2, 3, 4, 5, 12]
for month_shift in tqdm_notebook(shift_range):
train_shift = all_data[index_cols + cols_to_rename].copy()
train_shift['date_block_num'] = train_shift['date_block_num'] + month_shift
foo = lambda x: '{}_lag_{}'.format(x, mon... | def KNN_2(neighbors):
neighbors = range(1,neighbors+1,1)
print("Para p = 2")
for i in neighbors:
KNN = KNeighborsClassifier(n_neighbors=i,p=2)
model = KNN.fit(X_train,np.ravel(y_train))
results_KNN = model.predict(test_data[features])
print("Para uma quantidade de vizinhos de:", i,"a precisão do modelo foi de ",rou... | Titanic - Machine Learning from Disaster |
8,370,213 | all_data = all_data[all_data['date_block_num'] >= 12]
fit_cols = [col for col in all_data.columns if col[-1] in [str(item)for item in shift_range]]
to_drop_cols = ['target_item', 'target_shop', 'target', 'date_block_num']
to_drop_cols = list(set(list(all_data.columns)) -(set(fit_cols)|set(index_cols)))+ ['date_block_nu... | def choosen_KNN(neighbors,p):
KNN = KNeighborsClassifier(n_neighbors=neighbors,p=p)
model = KNN.fit(X_train,np.ravel(y_train))
results_KNN = model.predict(test_data[features])
print("Precisão ",round(( model.score(X_train,y_train)) *100,2),"%
")
output_KNN = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survi... | Titanic - Machine Learning from Disaster |
8,370,213 | dates = all_data['date_block_num']
dates_train = dates[dates < test_block]
dates_test = dates[dates == test_block]<prepare_x_and_y> | from sklearn.tree import DecisionTreeClassifier | Titanic - Machine Learning from Disaster |
8,370,213 | X_train = all_data.loc[dates < test_block].drop(to_drop_cols, axis=1)
X_test = all_data.loc[dates == test_block].drop(to_drop_cols, axis=1)
y_train = all_data.loc[dates < test_block, 'target'].values
y_test = all_data.loc[dates == test_block, 'target'].values<define_variables> | def DTC(depth):
depth = range(1,depth+1,1)
for i in depth:
DTC = DecisionTreeClassifier(max_depth=i)
model = DTC.fit(X_train, y_train)
predictions_DTC = model.predict(X_test)
print("Para uma profundidade de:", i,"a precisão do modelo foi de ",round(( model.score(X_train,y_train)) *100,2),"%" ) | Titanic - Machine Learning from Disaster |
8,370,213 | target_range = [0, 20]
target_range<init_hyperparams> | def choosen_DTC(depth):
DTC = DecisionTreeClassifier(max_depth=depth)
model = DTC.fit(X_train, y_train)
predictions_DTC = model.predict(X_test)
print(pd.crosstab(predictions_DTC, submission['Survived'], margins=True,rownames=['Previsto'],colnames=[' Real']))
print(metrics.classification_report(submission['Survived']... | Titanic - Machine Learning from Disaster |
8,370,213 | lgb_params = {
'feature_fraction': 0.75,
'metric': 'rmse',
'nthread':1,
'min_data_in_leaf': 2**7,
'bagging_fraction': 0.7,
'learning_rate': 0.04,
'objective': 'mse',
'bagging_seed': 2**7,
'num_leaves': 2**7,
'bagging_freq':1,
'verbose':0
}
model = lgb.train(lgb_params, lgb.Dataset(X_train, label=y_train), 500)
pred_lg... | from sklearn.model_selection import cross_val_predict,cross_val_score,cross_validate
from sklearn import metrics | Titanic - Machine Learning from Disaster |
8,370,213 | submission = pd.DataFrame({'ID': sample_submission.ID, 'item_cnt_month': pred_lgb})
submission.to_csv('submission.csv', index=False )<set_options> | def DTC_CROSS(depth, folds):
model_DTC = DecisionTreeClassifier(max_depth=depth)
model_DTC.fit(X_train, y_train)
cross_validate(model_DTC,X_train,y_train,cv=folds)
predictions_DTC_cross = model_DTC.predict(X_test)
accuracy = metrics.accuracy_score(y_test,predictions_DTC_cross)
print("
")
print(pd.crosstab(predict... | Titanic - Machine Learning from Disaster |
8,370,213 | warnings.filterwarnings("ignore")
<load_from_csv> | from sklearn import svm | Titanic - Machine Learning from Disaster |
8,370,213 | df_train = pd.read_csv('.. /input/train.csv' ).astype('float32')
df_test = pd.read_csv('.. /input/test.csv' )<drop_column> | features = ['Sex', 'Pclass','Fare','Age','Embarked','SibSp','Parch']
targets = ['Survived'] | Titanic - Machine Learning from Disaster |
8,370,213 | df_train = reduce_mem_usage(df_train)
df_test = reduce_mem_usage(df_test )<feature_engineering> | X_train = train_data[features]
X_test = test_data[features]
X_train.head() | Titanic - Machine Learning from Disaster |
8,370,213 | df_train["distance"] = df_train["rideDistance"]+df_train["walkDistance"]+df_train["swimDistance"]
df_train["skill"] = df_train["headshotKills"]+df_train["roadKills"]
df_test["distance"] = df_test["rideDistance"]+df_test["walkDistance"]+df_test["swimDistance"]
df_test["skill"] = df_test["headshotKills"]+df_test["roadKil... | y_train = train_data[targets]
y_test = submission['Survived']
y_train.head() | Titanic - Machine Learning from Disaster |
8,370,213 |
df_train_size = df_train.groupby(['matchId','groupId'] ).size().reset_index(name='group_size')
df_test_size = df_test.groupby(['matchId','groupId'] ).size().reset_index(name='group_size')
df_train_mean = df_train.groupby(['matchId','groupId'] ).mean().reset_index()
df_test_mean = df_test.groupby(['matchId','groupId... | def choosen_SVM(C, gamma):
model_SVM = svm.SVC(C=C,gamma=gamma,random_state=0)
model_SVM.fit(X_train,y_train)
predictions_SVM = model_SVM.predict(X_test)
print("Para C = ",C,"e gamma = ", gamma," Precisão = ",round(( model_SVM.score(X_train,y_train)) *100,2),"% ")
print(pd.crosstab(predictions_SVM,submission['Survi... | Titanic - Machine Learning from Disaster |
8,370,213 |
df_train_match_mean = df_train.groupby(['matchId'] ).mean().reset_index()
df_test_match_mean = df_test.groupby(['matchId'] ).mean().reset_index()
df_train = pd.merge(df_train, df_train_mean, suffixes=["", "_mean"], how='left', on=['matchId', 'groupId'])
df_test = pd.merge(df_test, df_test_mean, suffixes=["", "_mean"... | train_data['Survived'].value_counts() | Titanic - Machine Learning from Disaster |
8,370,213 |
train_columns.remove("Id")
train_columns.remove("matchId")
train_columns.remove("groupId")
train_columns.remove("Id_mean")
train_columns.remove("Id_max")
train_columns.remove("Id_min")
train_columns.remove("Id_match_mean" )<prepare_x_and_y> | from imblearn.under_sampling import NearMiss | Titanic - Machine Learning from Disaster |
8,370,213 | X = df_train[train_columns]
Y = df_test[train_columns]
T = df_train[target]
del df_train
<normalization> | X = train_data[features]
X_test = test_data[features]
y = train_data[targets]
y_test = submission[targets] | Titanic - Machine Learning from Disaster |
8,370,213 | x_train, x_test, t_train, t_test = train_test_split(X, T, test_size = 0.2, random_state = 1234)
scaler = preprocessing.QuantileTransformer().fit(x_train)
x_train = scaler.transform(x_train)
x_test = scaler.transform(x_test)
Y = scaler.transform(Y)
print("x_train", x_train.shape, x_train.min() , x_train.max())
pri... | nrm = NearMiss() | Titanic - Machine Learning from Disaster |
8,370,213 | model = Sequential()
model.add(Dense(512, kernel_initializer='he_normal', input_dim=x_train.shape[1], activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(Dense(256, kernel_initializer='he_normal', activation='relu'))
model.add(BatchNormalization())
model.add(Dropout(0.1))
model.add(D... | X, y = nrm.fit_sample(X,y ) | Titanic - Machine Learning from Disaster |
8,370,213 | optimizer = optimizers.Adam(lr=0.01, epsilon=1e-8, decay=1e-4, amsgrad=False)
model.compile(optimizer=optimizer, loss='mse', metrics=['mae'] )<train_model> | lr_us = LogisticRegression()
model = lr_us.fit(X,y)
predictions_lr_us = model.predict(X_test)
print(pd.crosstab(predictions_lr_us,submission['Survived'],margins=True,rownames=['Previsto'],colnames=[' Real']))
print(metrics.classification_report(y_test,predictions_lr_us)) | Titanic - Machine Learning from Disaster |
8,370,213 | history = model.fit(x_train, t_train,
validation_data=(x_test, t_test),
epochs=30,
batch_size=32768,
callbacks=[lr_sched,early_stopping],
verbose=1 )<save_to_csv> | output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions_lr_us})
output.to_csv("My_submission_Logistic_US.csv", index=False ) | Titanic - Machine Learning from Disaster |
8,370,213 | pred = model.predict(Y)
pred = pred.ravel()
df_test['winPlacePercPred'] = np.clip(pred, a_min=0, a_max=1)
aux = df_test.groupby(['matchId','groupId'])['winPlacePercPred'].agg('mean' ).groupby('matchId' ).rank(pct=True ).reset_index()
aux.columns = ['matchId','groupId','winPlacePerc']
df_test = df_test.merge(aux, how=... | from imblearn.over_sampling import SMOTE | Titanic - Machine Learning from Disaster |
8,370,213 | from lightgbm import LGBMRegressor
from sklearn.model_selection import KFold, StratifiedKFold
from sklearn.metrics import mean_absolute_error
import gc
from sklearn.model_selection import GridSearchCV<load_from_csv> | features = ['Sex', 'Pclass','Fare','Age','Embarked','SibSp','Parch']
targets = ['Survived'] | Titanic - Machine Learning from Disaster |
8,370,213 | train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv(".. /input/test.csv" )<load_from_csv> | sampling = np.linspace(0.65,1,8)
sampling | Titanic - Machine Learning from Disaster |
8,370,213 | train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv(".. /input/test.csv" )<concatenate> | def Over_samp(train, test, submisson):
features = ['Sex', 'Pclass','Fare','Age','Embarked','SibSp','Parch']
targets = ['Survived']
sampling = np.linspace(0.65,1,16)
X = train[features]
X_test = test[features]
y = train[targets]
y_test = submission[targets]
for i in sampling:
print("For sampling strategy = ",i*100,"%
"... | Titanic - Machine Learning from Disaster |
8,370,213 | concat = pd.concat([train, test])
del train
del test
gc.collect()<drop_column> | Over_samp(train_data,test_data, submission ) | Titanic - Machine Learning from Disaster |
8,370,213 | concat = reduce_mem_usage(concat )<categorify> | X = train_data[features]
X_test = test_data[features]
y = train_data[targets]
y_test = submission[targets] | Titanic - Machine Learning from Disaster |
8,370,213 | def count_transform(df, cols):
for c in cols:
df[c + "_count"] = df.groupby(c)[c].transform('count')
return df
<categorify> | smt = SMOTE(sampling_strategy = 0.65)
X, y = smt.fit_sample(X,y)
lr_os = LogisticRegression()
model = lr_os.fit(X,y)
predictions_lr_os = model.predict(X_test)
print(pd.crosstab(predictions_lr_os,submission['Survived'],margins=True,rownames=['Previsto'],colnames=[' Real']))
print(metrics.classification_report(y_test... | Titanic - Machine Learning from Disaster |
8,370,213 | concat = count_transform(concat, ['groupId', 'matchId'] )<define_variables> | def DTC_US(depth):
features = ['Sex', 'Pclass','Fare','Age','Embarked','SibSp','Parch']
targets = ['Survived']
X_train = train_data[features]
X_test = test_data[features]
y_train = train_data[targets]
y_test = submission[targets]
X,y = nrm.fit_sample(X_train, y_train)
sns.distplot(y, kde=False)
DTC = DecisionTreeClas... | Titanic - Machine Learning from Disaster |
8,370,213 | per_dist_stats = ['assists', 'boosts', 'damageDealt', 'DBNOs',
'headshotKills', 'heals', 'kills',
'teamKills', 'vehicleDestroys', 'weaponsAcquired']<feature_engineering> | def DTC_OS(depth):
features = ['Sex', 'Pclass','Fare','Age','Embarked','SibSp','Parch']
targets = ['Survived']
X_train = train_data[features]
X_test = test_data[features]
y_train = train_data[targets]
y_test = submission[targets]
smt = SMOTE(sampling_strategy = 0.70)
X,y = smt.fit_sample(X_train, y_train)
sns.distplo... | Titanic - Machine Learning from Disaster |
8,370,213 | concat['LogWalk'] = np.log1p(concat['walkDistance'] )<feature_engineering> | def SVM_US(C, gamma):
features = ['Sex', 'Pclass','Fare','Age','Embarked','SibSp','Parch']
targets = ['Survived']
X_train = train_data[features]
X_test = test_data[features]
y_train = train_data[targets]
y_test = submission[targets]
X,y = nrm.fit_sample(X_train, y_train)
sns.distplot(y, kde=False)
sv = svm.SVC(C=C, g... | Titanic - Machine Learning from Disaster |
8,370,213 | for stat in per_dist_stats:
concat[stat + '_perLogWalk'] = concat[stat] / concat['LogWalk']<feature_engineering> | SVM_US(5,0.01 ) | Titanic - Machine Learning from Disaster |
8,370,213 | concat['grpSizeMult'] = concat['groupId_count'] /(concat['matchId_count'] / concat['numGroups'] )<define_variables> | def SVM_OS(C,gamma):
features = ['Sex', 'Pclass','Fare','Age','Embarked','SibSp','Parch']
targets = ['Survived']
X_train = train_data[features]
X_test = test_data[features]
y_train = train_data[targets]
y_test = submission[targets]
smt = SMOTE(sampling_strategy = 0.75)
X,y = smt.fit_sample(X_train, y_train)
sns.distp... | Titanic - Machine Learning from Disaster |
8,370,213 | match_stats = ['DBNOs',
'assists',
'boosts',
'damageDealt',
'headshotKills',
'heals',
'killPlace',
'killPoints',
'killStreaks',
'kills',
'longestKill',
'revives',
'rideDistance',
'roadKills',
'swimDistance',
'vehicleDestroys',
'walkDistance',
'weaponsAcquired',
'winPoints',
'LogWalk',
'assists_perLogWalk',
'boosts_perL... | SVM_OS(100,0.01 ) | Titanic - Machine Learning from Disaster |
2,721,831 | for stat in match_stats:
concat['matchRel_' + stat] = concat[stat] / concat.groupby('matchId')[stat].transform('mean' )<define_variables> | df = pd.read_csv('.. /input/train.csv')
df.head() | Titanic - Machine Learning from Disaster |
2,721,831 | drop_features = ["winPlacePerc", "Id", "groupId", "matchId"]
feats = [c for c in concat.columns if c not in drop_features]<define_variables> | d = df[['Survived','Pclass','Sex', 'Age', 'SibSp',
'Parch','Embarked']]
d = d.dropna() | Titanic - Machine Learning from Disaster |
2,721,831 | aggs = {
'grpSizeMult' : ['mean'],
'groupId_count' : ['mean'],
'matchId_count' : ['mean'],
'winPlacePerc' : ['mean'],
}
for c in feats:
if c not in aggs:
aggs[c] = ['mean', 'min', 'max', 'std']
new_cols = [k + '_' + agg for k in aggs.keys() for agg in aggs[k]]<groupby> | from sklearn import preprocessing | Titanic - Machine Learning from Disaster |
2,721,831 | groups = concat.groupby('groupId' ).agg(aggs)
<rename_columns> | import seaborn as sns
import matplotlib.pyplot as plt | Titanic - Machine Learning from Disaster |
2,721,831 | groups.columns = new_cols<set_options> | df['title'] = df.title.replace(['Lady', 'Countess','Capt', 'Col',\
'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
df['title'] = df['title'].replace('Mlle', 'Miss')
df['title'] = df['title'].replace('Ms', 'Miss')
df['title'] = df['title'].replace('Mme', 'Mrs')
df[['title','Survived']].groupby('tit... | Titanic - Machine Learning from Disaster |
2,721,831 | del concat
gc.collect()<groupby> | df.title = df.title.fillna(0)
df['title'] = df.title.map({'Rare':0, 'Master':1, 'Miss':2, 'Mr':3, 'Mrs':4})
df.Sex = df.Sex.map({'female':0, 'male':1} ).astype(int ) | Titanic - Machine Learning from Disaster |
2,721,831 | groups = reduce_mem_usage(groups )<init_hyperparams> | df.Age = df.Age.fillna(df.Age.mean())
df.Embarked = df.Embarked.fillna('S' ) | Titanic - Machine Learning from Disaster |
2,721,831 | params = {
'num_leaves': 144,
'learning_rate': 0.1,
'n_estimators': 800,
'max_depth':13,
'max_bin':55,
'bagging_fraction':0.8,
'bagging_freq':5,
'feature_fraction':0.9
}<define_search_model> | df = df.drop(columns = ['PassengerId', 'Name', 'Ticket', 'Cabin'])
df['Embarked'] = df.Embarked.map({'S':0,'C':1,'Q':2} ).astype(int)
df.Age = round(df.Age ).astype(int)
df.head()
| Titanic - Machine Learning from Disaster |
2,721,831 | def kfold_lightgbm(df, num_folds, stratified = False, debug= False):
train_df = df[df['winPlacePerc_mean'].notnull() ]
test_df = df[df['winPlacePerc_mean'].isnull() ]
print("Starting LightGBM.Train shape: {}, test shape: {}".format(train_df.shape, test_df.shape))
del df
gc.collect()
if stratified:
folds = StratifiedKFo... | df.loc[(round(df['Age'])<=16),'Age'] = 0
df.loc[(round(df['Age'])>16)&(round(df['Age'])<=32),'Age'] = 1
df.loc[(round(df['Age'])>32)&(round(df['Age'])<=48),'Age'] = 2
df.loc[(round(df['Age'])>48)&(round(df['Age'])<=64),'Age'] = 3
df.loc[(round(df['Age'])>64)&(round(df['Age'])<=80),'Age'] = 4 | Titanic - Machine Learning from Disaster |
2,721,831 | feat_importances, test_df = kfold_lightgbm(groups, num_folds=5, stratified=False, debug=False )<compute_test_metric> | df[['Age','Survived']].groupby('Age',as_index = False ).mean() | Titanic - Machine Learning from Disaster |
2,721,831 | display_importances(feat_importances )<sort_values> | df.loc[(round(df['Fare'])<=7.9),'Fare'] = 0
df.loc[(round(df['Fare'])>7.9)&(round(df['Fare'])<=14.45),'Fare'] = 1
df.loc[(round(df['Fare'])>14.45)&(round(df['Fare'])<=31.0),'Fare'] = 2
df.loc[(round(df['Fare'])>31.0),'Fare'] = 3 | Titanic - Machine Learning from Disaster |
2,721,831 | feat_gb = feat_importances.groupby('feature' ).mean().sort_values(by="importance", ascending=False )<save_to_csv> | df[['Fare','Survived']].groupby('Fare' ).mean() | Titanic - Machine Learning from Disaster |
2,721,831 | feat_gb.to_csv("feature_importance.csv" )<load_from_csv> | df['Familysize'] = df['SibSp'] + df['Parch'] + 1
df[['Familysize','Survived']].groupby('Familysize' ).mean() | Titanic - Machine Learning from Disaster |
2,721,831 | test = pd.read_csv(".. /input/test.csv" )<merge> | df['Familysize'] = df['SibSp'] + df['Parch'] + 1
df.loc[df['Familysize'] == 1,'Familysize'] = 0
df.loc[df.Familysize >1,'Familysize'] = 1 | Titanic - Machine Learning from Disaster |
2,721,831 | test = test.merge(test_df, right_index=True, left_on='groupId' )<save_to_csv> | df[['Familysize','Survived']].groupby('Familysize' ).mean() | Titanic - Machine Learning from Disaster |
2,721,831 | test[['Id', 'winPlacePerc']].to_csv("submission.csv", index=False )<set_options> | df = df.drop(columns = ['Parch','SibSp'] ) | Titanic - Machine Learning from Disaster |
2,721,831 | warnings.filterwarnings('ignore')
%matplotlib inline
py.init_notebook_mode(connected=True)
<load_from_csv> | df = df.drop(columns = ['AgeBand','FareBand'])
df.head() | Titanic - Machine Learning from Disaster |
2,721,831 | debug = False
if debug == True:
df_train = pd.read_csv('.. /input/train_V2.csv', nrows=10000)
df_test = pd.read_csv('.. /input/test_V2.csv')
else:
df_train = pd.read_csv('.. /input/train_V2.csv')
df_test = pd.read_csv('.. /input/test_V2.csv' )<filter> | df.Embarked = df.Embarked.fillna(0)
df['Embarked'] = round(df['Embarked'] ).astype(int)
df.Fare = df.Fare.astype(int)
df.info() | Titanic - Machine Learning from Disaster |
2,721,831 | df_train[df_train['groupId']=='4d4b580de459be']<filter> | from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.naive_bayes import GaussianNB
from sklearn.neural_network import MLPClassifier
from sklearn.svm import SVC | Titanic - Machine Learning from Disaster |
2,721,831 | len(df_train[df_train['matchId']=='a10357fd1a4a91'] )<count_values> | DTC = DecisionTreeClassifier()
RFC = RandomForestClassifier(n_estimators = 500)
LR = LogisticRegression()
GNB = GaussianNB()
MLPC = MLPClassifier()
svc = SVC(kernel = 'linear', C = 0.1, gamma = 'scale' ) | Titanic - Machine Learning from Disaster |
2,721,831 | df_train['roadKills'].value_counts()<count_values> | X = np.asanyarray(df.drop(columns = ['Survived']))
y = np.asanyarray(df.Survived)
x_train,x_test, y_train, y_test = train_test_split(X,y , test_size = 0.2,random_state = 0 ) | Titanic - Machine Learning from Disaster |
2,721,831 | df_train['teamKills'].value_counts()<count_values> | DTC.fit(x_train,y_train)
y_hat1 = DTC.predict(x_test)
RFC.fit(x_train,y_train)
y_hat2 = RFC.predict(x_test)
LR.fit(x_train,y_train)
y_hat3 = LR.predict(x_test)
GNB.fit(x_train,y_train)
y_hat4 = GNB.predict(x_test)
MLPC.fit(x_train,y_train)
y_hat5 = MLPC.predict(x_test)
svc.fit(x_train,y_train)
y_hat6 = svc.p... | Titanic - Machine Learning from Disaster |
2,721,831 | df_train['headshotKills'].value_counts()<count_values> | print('Accuracy_yhat1_DTC=',accuracy_score(y_test,y_hat1))
print('Accuracy_yhat2_RFC=',accuracy_score(y_test,y_hat2))
print('Accuracy_yhat3_LR=',accuracy_score(y_test,y_hat3))
print('Accuracy_yhat4_GNB=',accuracy_score(y_test,y_hat4))
print('Accuracy_yhat5_MLPC=',accuracy_score(y_test,y_hat5))
print('Accuracy_yhat6_SVC... | Titanic - Machine Learning from Disaster |
2,721,831 | df_train['vehicleDestroys'].value_counts()<filter> | DTC.score(x_train,y_train ) | Titanic - Machine Learning from Disaster |
2,721,831 | df_train = df_train[df_train['Id']!='f70c74418bb064']<feature_engineering> | RFC.score(x_train,y_train ) | Titanic - Machine Learning from Disaster |
2,721,831 | headshot = df_train[['kills','winPlacePerc','headshotKills']]
headshot['headshotrate'] = headshot['kills'] / headshot['headshotKills']<drop_column> | ETC = ExtraTreesClassifier(n_estimators = 500)
ETC.fit(x_train,y_train)
y_hat7 = ETC.predict(x_test)
ETC.score(x_train,y_train)
print('Accuracy_yhat7_ETC=',accuracy_score(y_test,y_hat7)) | Titanic - Machine Learning from Disaster |
2,721,831 | del headshot<feature_engineering> | df1 = pd.read_csv('.. /input/test.csv')
df1.head() | Titanic - Machine Learning from Disaster |
2,721,831 | df_train['headshotrate'] = df_train['kills']/df_train['headshotKills']
df_test['headshotrate'] = df_test['kills']/df_test['headshotKills']<feature_engineering> | df1['title'] = df1['Name'].str.extract('([A-Za-z]+)\.', expand = False)
df1['title'] = df1.title.replace(['Lady', 'Countess','Capt', 'Col','Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
df1['title'] = df1['title'].replace('Mlle', 'Miss')
df1['title'] = df1['title'].replace('Ms', 'Miss')
df1['title... | Titanic - Machine Learning from Disaster |
2,721,831 | killStreak = df_train[['kills','winPlacePerc','killStreaks']]
killStreak['killStreakrate'] = killStreak['killStreaks']/killStreak['kills']
killStreak.corr()<drop_column> | df1.title = df1.title.fillna(0)
df1['title'] = df1.title.map({'Rare':0, 'Master':1, 'Miss':2, 'Mr':3, 'Mrs':4})
df1.Sex = df1.Sex.map({'female':0, 'male':1} ).astype(int ) | Titanic - Machine Learning from Disaster |
2,721,831 | del healthitems<feature_engineering> | df1 = df1.drop(columns = ['PassengerId', 'Name', 'Ticket', 'Cabin'])
df1['Embarked'] = df1.Embarked.map({'S':0,'C':1,'Q':2} ).astype(int)
df1.Age = df1.Age.fillna(df1.Age.mean())
df1.Age = round(df1.Age ).astype(int)
| Titanic - Machine Learning from Disaster |
2,721,831 | kills = df_train[['assists','winPlacePerc','kills']]
kills['kills_assists'] =(kills['kills'] + kills['assists'])
kills.corr()<set_options> | df1.loc[(round(df1['Age'])<=16),'Age'] = 0
df1.loc[(round(df1['Age'])>16)&(round(df1['Age'])<=32),'Age'] = 1
df1.loc[(round(df1['Age'])>32)&(round(df1['Age'])<=48),'Age'] = 2
df1.loc[(round(df1['Age'])>48)&(round(df1['Age'])<=64),'Age'] = 3
df1.loc[(round(df1['Age'])>64)&(round(df1['Age'])<=80),'Age'] = 4 | Titanic - Machine Learning from Disaster |
2,721,831 | del df_train,df_test;
gc.collect()<load_from_csv> | df1.loc[(round(df1['Fare'])<=7.9),'Fare'] = 0
df1.loc[(round(df1['Fare'])>7.9)&(round(df1['Fare'])<=14.45),'Fare'] = 1
df1.loc[(round(df1['Fare'])>14.45)&(round(df1['Fare'])<=31.0),'Fare'] = 2
df1.loc[(round(df1['Fare'])>31.0),'Fare'] = 3 | Titanic - Machine Learning from Disaster |
2,721,831 | def feature_engineering(is_train=True,debug=True):
test_idx = None
if is_train:
print("processing train.csv")
if debug == True:
df = pd.read_csv('.. /input/train_V2.csv', nrows=10000)
else:
df = pd.read_csv('.. /input/train_V2.csv')
df = df[df['maxPlace'] > 1]
else:
print("processing test.csv")
df = pd.read_csv('..... | df1['Familysize'] = df1['SibSp'] + df1['Parch'] + 1
df1.loc[df1['Familysize'] == 1,'Familysize'] = 0
df1.loc[df1.Familysize >1,'Familysize'] = 1 | Titanic - Machine Learning from Disaster |
2,721,831 | x_train['headshotrate'] = x_train['kills']/x_train['headshotKills']
x_test['headshotrate'] = x_test['kills']/x_test['headshotKills']
x_train['killStreakrate'] = x_train['killStreaks']/x_train['kills']
x_test['killStreakrate'] = x_test['killStreaks']/x_test['kills']
x_train['healthitems'] = x_train['heals'] + x_train['b... | df1 = df1.drop(columns = ['Parch','SibSp'])
df1.Fare = df1.Fare.fillna(0)
df1.head() | Titanic - Machine Learning from Disaster |
2,721,831 | x_train = reduce_mem_usage(x_train)
x_test = reduce_mem_usage(x_test )<set_options> | predictions = RFC.predict(np.asanyarray(df1)) | Titanic - Machine Learning from Disaster |
2,721,831 | warnings.filterwarnings("ignore")
<split> | df3 = pd.read_csv('.. /input/test.csv')
submissions = pd.DataFrame({'PassengerID':df3['PassengerId'],'Survived':predictions})
submissions.to_csv('submission.csv',index = False, header = True ) | Titanic - Machine Learning from Disaster |
1,505,879 | folds = KFold(n_splits=3,random_state=6)
oof_preds = np.zeros(x_train.shape[0])
sub_preds = np.zeros(x_test.shape[0])
start = time.time()
valid_score = 0
feature_importance_df = pd.DataFrame()
for n_fold,(trn_idx, val_idx)in enumerate(folds.split(x_train, y_train)) :
trn_x, trn_y = x_train.iloc[trn_idx], y_train[trn... | train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv(".. /input/test.csv" ) | Titanic - Machine Learning from Disaster |
1,505,879 | df_test = pd.read_csv('.. /input/' + 'test_V2.csv')
pred = sub_preds
print("fix winPlacePerc")
for i in range(len(df_test)) :
winPlacePerc = pred[i]
maxPlace = int(df_test.iloc[i]['maxPlace'])
if maxPlace == 0:
winPlacePerc = 0.0
elif maxPlace == 1:
winPlacePerc = 1.0
else:
gap = 1.0 /(maxPlace - 1)
winPlacePerc = ... | train = pd.read_csv(".. /input/train.csv")
test = pd.read_csv(".. /input/test.csv" ) | Titanic - Machine Learning from Disaster |
1,505,879 | def toTapleList(list1,list2):
return list(itertools.product(list1,list2))<load_from_csv> | train_drop = train.copy()
train_drop = train_drop.dropna(subset = ['Age'])
def encodeSex(sex):
if sex == "male": return 0
return 1
def encodeAge(age):
return int(age/5)
train_drop.Sex = train_drop.Sex.apply(encodeSex)
train_drop.Age = train_drop.Age.apply(encodeAge ) | Titanic - Machine Learning from Disaster |
1,505,879 | %%time
train = pd.read_csv('.. /input/train_V2.csv')
train = reduce_mem_usage(train)
test = pd.read_csv('.. /input/test_V2.csv')
test = reduce_mem_usage(test)
print(train.shape, test.shape )<count_missing_values> | features = ['Pclass','Sex', 'Age', 'Fare'] | Titanic - Machine Learning from Disaster |
1,505,879 | null_cnt = train.isnull().sum().sort_values()
print(null_cnt[null_cnt > 0])
train.dropna(inplace=True )<count_unique_values> | X = train_drop[features]
y = train_drop.Survived
train_X, test_X, train_y, test_y = train_test_split(X, y, random_state=1)
forest = RandomForestClassifier(max_leaf_nodes = 55)
forest.fit(train_X, train_y)
print("Feature importance: ", forest.feature_importances_)
print("Accuracy: ", forest.score(test_X, test_y)) | Titanic - Machine Learning from Disaster |
1,505,879 | for c in ['Id','groupId','matchId']:
print(f'unique [{c}] count:', train[c].nunique() )<count_values> | test_features = ["Title", "SibSp", "Parch", "Pclass", "Fare"]
X = train_with_title[test_features]
y = train_with_title.Age
train_X, test_X, train_y, test_y = train_test_split(X, y, random_state=1)
line.fit(train_X, train_y)
print("Accuracy: ", line.score(test_X, test_y)) | Titanic - Machine Learning from Disaster |
1,505,879 | for q in ['numGroups == maxPlace','numGroups != maxPlace']:
print(q, ':', len(train.query(q)) )<drop_column> | predicted_ages = train[train['Age'].isnull() ]
predicted_ages['Title'] = predicted_ages.Name.apply(extractTitle)
predicted_ages['Title'] = predicted_ages.Title.apply(encodeTitle)
predictions = pd.Series(line.predict(predicted_ages[test_features]))
train_with_ages = train
inc = 0
for i in range(0, len(train['Age'])) :... | Titanic - Machine Learning from Disaster |
1,505,879 | print(group['players in group'].nlargest(5))
del match,group<count_unique_values> | train_with_ages.Sex = train_with_ages.Sex.apply(encodeSex)
train_with_ages.Age = train_with_ages.Age.apply(encodeAge)
features = ['Pclass','Sex', 'Age', 'Fare']
X = train_with_ages[features]
y = train_with_ages.Survived
train_X, test_X, train_y, test_y = train_test_split(X, y, random_state=1)
forest = RandomForestCl... | Titanic - Machine Learning from Disaster |
1,505,879 | <groupby><EOS> | predicted_ages = test[test['Age'].isnull() ]
predicted_ages['Title'] = predicted_ages.Name.apply(extractTitle)
predicted_ages['Title'] = predicted_ages.Title.apply(encodeTitle)
predictions = pd.Series(line.predict(predicted_ages[test_features]))
inc = 0
for i in range(0, len(test['Age'])) :
if math.isnan(test['Age'][... | Titanic - Machine Learning from Disaster |
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