kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
14,388,835 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<define_variables> | warnings.filterwarnings("ignore")
%matplotlib inline | Tabular Playground Series - Jan 2021 |
14,388,835 | path = Path('/kaggle/input/planet-understanding-the-amazon-from-space')
path.ls()<load_from_csv> | features = [feature for feature in train_df.columns if feature not in ['id', 'target']]
X_train = train_df[features]
y_train = train_df['target']
X_test = test_df[features] | Tabular Playground Series - Jan 2021 |
14,388,835 | df = pd.read_csv(path/'train_v2.csv')
df.head()<feature_engineering> | %%time
forest_reg = RandomForestRegressor(random_state=121, n_jobs=-1)
scores = cross_val_score(forest_reg, X_train, y_train, scoring='neg_mean_squared_error', cv=4)
forest_rmse_scores = np.sqrt(-scores)
print('Random Forest performance:', forest_rmse_scores)
print('Random Forest performance_mean:', forest_rmse_sco... | Tabular Playground Series - Jan 2021 |
14,388,835 | tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)<load_from_csv> | %%time
xgb_reg = XGBRegressor(random_state=121, objective = 'reg:squarederror', n_jobs=-1)
scores = cross_val_score(xgb_reg, X_train, y_train, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)
xgb_rmse_scores = np.sqrt(-scores)
print('XGBoost performance:', xgb_rmse_scores)
print('XGBoost performance_mean:', xgb_r... | Tabular Playground Series - Jan 2021 |
14,388,835 | np.random.seed(42)
src =(ImageList.from_csv(path, 'train_v2.csv', folder='train-jpg', suffix='.jpg')
.split_by_rand_pct(0.2)
.label_from_df(label_delim=' '))<normalization> | %%time
lgbm_reg = LGBMRegressor(random_state=121)
scores = cross_val_score(lgbm_reg, X_train, y_train, scoring='neg_mean_squared_error', cv=5, n_jobs=-1)
lgbm_rmse_scores = np.sqrt(-scores)
print('LGBM performance:', lgbm_rmse_scores)
print('LGBM performance_mean:', lgbm_rmse_scores.mean())
| Tabular Playground Series - Jan 2021 |
14,388,835 | data =(src.transform(tfms, size=128)
.databunch().normalize(imagenet_stats))<define_variables> | lasso_reg = Lasso()
scores = cross_val_score(lasso_reg, X_train, y_train, scoring='neg_mean_squared_error', cv=5)
lasso_rmse_scores = np.sqrt(-scores)
print('LASSO performance:', lasso_rmse_scores)
print('LASSO performance_mean:', lasso_rmse_scores.mean() ) | Tabular Playground Series - Jan 2021 |
14,388,835 | data.show_batch(rows=3, figsize=(12,9))<choose_model_class> | ridge_reg = Ridge()
scores = cross_val_score(ridge_reg, X_train, y_train, scoring='neg_mean_squared_error', cv=5)
ridge_rmse_scores = np.sqrt(-scores)
print('Ridge performance:', ridge_rmse_scores)
print('Ridge performance_mean:', ridge_rmse_scores.mean() ) | Tabular Playground Series - Jan 2021 |
14,388,835 | arch = models.resnet50<find_best_params> | def rmse_mean(model):
rmse_scores_mean = np.sqrt(
-cross_val_score(
model, X_train, y_train,
scoring="neg_mean_squared_error",
cv=5,
)).mean()
return(rmse_scores_mean)
alphas = [1, 10**-1, 10**-2, 10**-3, 10**-4, 10**-5]
lasso_regs = [rmse_mean(Lasso(alpha = alpha)) for alpha in alphas]
lasso_regs = pd.Series(las... | Tabular Playground Series - Jan 2021 |
14,388,835 | acc_02 = partial(accuracy_thresh, thresh=0.2)
f_score = partial(fbeta, thresh=0.2)
learn = cnn_learner(data, arch, metrics=[acc_02, f_score],path='.. /working/' )<find_best_params> | X = train_df[features]
y = train_df['target'] | Tabular Playground Series - Jan 2021 |
14,388,835 | learn.lr_find()<init_hyperparams> | def objective(trial,data=X,target=y):
train_x, test_x, train_y, test_y = train_test_split(data, target, test_size=0.15,random_state=42)
param = {
'tree_method':'gpu_hist',
'lambda':
trial.suggest_loguniform('lambda', 1e-3, 1),
'alpha':
trial.suggest_loguniform('alpha', 1e-3, 1),
'colsample_bytree':
trial.suggest_categ... | Tabular Playground Series - Jan 2021 |
14,388,835 | lr = 0.01<train_model> | study = optuna.create_study(direction='minimize' ) | Tabular Playground Series - Jan 2021 |
14,388,835 | learn.fit_one_cycle(5, slice(lr))<save_model> | study.optimize(objective, n_trials=25 ) | Tabular Playground Series - Jan 2021 |
14,388,835 | learn.save('stage-1-rn50' )<train_model> | print('Number of finished trials:', len(study.trials))
print('Best trial:', study.best_trial.params ) | Tabular Playground Series - Jan 2021 |
14,388,835 | learn.fit_one_cycle(5, slice(1e-5, lr/5))<save_model> | study_data_table = study.trials_dataframe()
study_data_table.to_csv('study_xgboost.csv', index=False)
study_data_table | Tabular Playground Series - Jan 2021 |
14,388,835 | learn.save('stage-2-rn50' )<categorify> | best_trial_paras = {'tree_method':'gpu_hist', 'lambda': 0.03349655513592068,
'alpha': 0.12097952030992898, 'colsample_bytree': 0.5,
'subsample': 0.4, 'learning_rate': 0.01,
'n_estimators': 4000, 'max_depth': 11, 'min_child_weight': 179,
'random_state': 2021
} | Tabular Playground Series - Jan 2021 |
14,388,835 | data =(src.transform(tfms, size=256)
.databunch().normalize(imagenet_stats))
learn.data = data
data.train_ds[0][0].shape<find_best_params> | preds = np.zeros(test_df.shape[0])
kf = KFold(n_splits=5,random_state=48,shuffle=True)
rmse=[]
n=0
for trn_idx, test_idx in kf.split(train_df[features],train_df['target']):
X_tr,X_val=train_df[features].iloc[trn_idx],train_df[features].iloc[test_idx]
y_tr,y_val=train_df['target'].iloc[trn_idx],train_df['target'].iloc... | Tabular Playground Series - Jan 2021 |
14,388,835 | lr=1e-2/2<train_model> | sub = pd.read_csv('.. /input/tabular-playground-series-jan-2021/sample_submission.csv')
print(sub.head() ) | Tabular Playground Series - Jan 2021 |
14,388,835 | <save_model><EOS> | sub['target']=preds
print(sub.head())
sub.to_csv('xgboost_submission.csv', index=False ) | Tabular Playground Series - Jan 2021 |
14,593,865 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<train_model> | sns.set(font_scale=1.4)
def pl(nr=1, nc=1,fs1=20,fs2=7):
fig,axes=plt.subplots(nrows=nr, ncols=nc, figsize=(fs1, fs2))
return fig, axes | Tabular Playground Series - Jan 2021 |
14,593,865 | learn.fit_one_cycle(5, slice(1e-5, lr/5))<save_model> | PATH = '/kaggle/input/tabular-playground-series-jan-2021/'
train = pd.read_csv(PATH+'train.csv')
test = pd.read_csv(PATH+'test.csv')
sample_submission = pd.read_csv(PATH+'sample_submission.csv')
FT_COLS = [x for x in train.columns if 'cont' in x]
LABEL='target' | Tabular Playground Series - Jan 2021 |
14,593,865 | learn.save('stage-2-256-rn50',return_path=True )<define_variables> | train['outlier_filter'] = np.where(train[LABEL]<4, True, False)
print('
ol_filt = ~train['outlier_filter'] | Tabular Playground Series - Jan 2021 |
14,593,865 | test = ImageList.from_folder(path/'test-jpg-v2' ).add(ImageList.from_folder(path/'test-jpg-additional'))
len(test )<predict_on_test> | NSUBS=4
SUBMISSION_DESCR = ['LGBM','XGB','KERAS','KERAS FEWER FTS',]
colors=['Blue','Green','Red', 'Pink',]
SUBMISSION_PATHS = ['/kaggle/input/jan21-lgbm-submission/',
'/kaggle/input/jan21-tabular-xgb-sub/',
'/kaggle/input/jan21-tabplayground-nn1-output/',
'/kaggle/input/jan21-tabplayground-nn2-output/',
] | Tabular Playground Series - Jan 2021 |
14,593,865 | learn = load_learner('.. /working/', test=test)
preds, _ = learn.get_preds(ds_type=DatasetType.Test )<define_variables> | weights_range = [0.001 , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,
0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 0.999] | Tabular Playground Series - Jan 2021 |
14,593,865 | thresh = 0.2
labelled_preds = [' '.join([learn.data.classes[i] for i,p in enumerate(pred)if p > thresh])for pred in preds]<define_variables> | %%time
output_wts = np.zeros(( 194481,NSUBS+1))
j=0
for a,b,c,d in itertools.product(weights_range, repeat=NSUBS):
sum_w = np.array([a,b,c,d] ).sum()
wts = np.array([a,b,c,d])/ sum_w
final_oof_preds = np.zeros(( len(train),))
for count, w in enumerate(oof_list):
final_oof_preds+=oof_list[count] * wts[count]
output_wts[... | Tabular Playground Series - Jan 2021 |
14,593,865 | fnames = [f.name[:-4] for f in learn.data.test_ds.items]<create_dataframe> | output_wts = output_wts.sort_values('oof_error' ).reset_index(drop=True)
output_wts.head(10 ) | Tabular Playground Series - Jan 2021 |
14,593,865 | df = pd.DataFrame({'image_name':fnames, 'tags':labelled_preds}, columns=['image_name', 'tags'] )<save_to_csv> | final_test_preds = np.zeros(( len(sample_submission),))
final_oof_preds = np.zeros(( len(train),))
for count, s in enumerate(submission_list):
final_test_preds+=submission_list[count] * selected_wts.values[count]
final_oof_preds+=oof_list[count] * selected_wts[count]
print('final CV error', np.sqrt(mse(train[LABEL], fi... | Tabular Playground Series - Jan 2021 |
14,593,865 | df.to_csv('.. /working/submission.csv', index=False )<set_options> | sample_submission['target'] = final_test_preds
sample_submission.to_csv('submission.csv', index=False)
sample_submission.head(5 ) | Tabular Playground Series - Jan 2021 |
14,454,005 | %matplotlib inline
<install_modules> | train = pd.read_csv("/kaggle/input/tabular-playground-series-jan-2021/train.csv")
test = pd.read_csv("/kaggle/input/tabular-playground-series-jan-2021/test.csv")
sample_submission = pd.read_csv('.. /input/tabular-playground-series-jan-2021/sample_submission.csv' ) | Tabular Playground Series - Jan 2021 |
14,454,005 | !pip install -U category_encoders<load_from_disk> | train.isna().sum() | Tabular Playground Series - Jan 2021 |
14,454,005 | train_data = pd.read_json('.. /input/two-sigma-connect-rental-listing-inquiries/train.json.zip', convert_dates=['created'])
test_data = pd.read_json('.. /input/two-sigma-connect-rental-listing-inquiries/test.json.zip', convert_dates=['created'] )<define_variables> | test.isna().sum() | Tabular Playground Series - Jan 2021 |
14,454,005 | train_size = train_data.shape[0]<feature_engineering> | target = train['target'].values | Tabular Playground Series - Jan 2021 |
14,454,005 | train_data['target'] = train_data['interest_level'].apply(lambda x: 0 if x=='low' else 1 if x=='medium' else 2)
train_data['low'] = train_data['interest_level'].apply(lambda x: 1 if x=='low' else 0)
train_data['medium'] = train_data['interest_level'].apply(lambda x: 1 if x=='medium' else 0)
train_data['high'] = trai... | np.unique(target ).shape
| Tabular Playground Series - Jan 2021 |
14,454,005 | full_data=pd.concat([train_data,test_data] )<define_variables> | train_oof = np.zeros(( 300000,))
test_preds = 0
train_oof.shape | Tabular Playground Series - Jan 2021 |
14,454,005 | num_vars = ['bathrooms','bedrooms','latitude','longitude','price']
cat_vars = ['building_id','manager_id','display_address','street_address']
text_vars = ['description','features']
date_var = 'created'
image_var = 'photos'
id_var = 'listing_id'<feature_engineering> | NUM_FOLDS = 10
kf = KFold(n_splits=NUM_FOLDS, shuffle=True, random_state=0)
for f,(train_ind, val_ind)in tqdm(enumerate(kf.split(train, target))):
train_df, val_df = train.iloc[train_ind][columns], train.iloc[val_ind][columns]
train_target, val_target = target[train_ind], target[val_ind]
model = HistGradientBoostingRe... | Tabular Playground Series - Jan 2021 |
14,454,005 | full_data['created_datetime'] = pd.to_datetime(full_data['created'], format="%Y-%m-%d %H:%M:%S")
full_data['created_year']=full_data['created_datetime'].apply(lambda x:x.year)
full_data['created_datetime'] = pd.to_datetime(full_data['created'], format="%Y-%m-%d %H:%M:%S")
full_data['created_month']=full_data['create... | mean_squared_error(train_oof, target, squared=False ) | Tabular Playground Series - Jan 2021 |
14,454,005 | full_data["geo_area_50"] = \
full_data[['latitude', 'longitude']]\
.apply(lambda x:(int(x[0]*50)%50)*50+(int(-x[1]*50)%50),axis=1)
full_data["geo_area_100"] = \
full_data[['latitude', 'longitude']]\
.apply(lambda x:(int(x[0]*100)%100)*100+(int(-x[1]*100)%100),axis=1)
full_data["geo_area_200"] = \
full_data[['latitu... | np.save('train_oof', train_oof)
np.save('test_preds', test_preds ) | Tabular Playground Series - Jan 2021 |
14,454,005 | <feature_engineering><EOS> | sample_submission['target'] = test_preds
sample_submission.to_csv('submission.csv', index=False ) | Tabular Playground Series - Jan 2021 |
14,201,098 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<count_values> | plt.style.use('fivethirtyeight')
y_ = Fore.YELLOW
r_ = Fore.RED
g_ = Fore.GREEN
b_ = Fore.BLUE
m_ = Fore.MAGENTA
c_ = Fore.CYAN
sr_ = Style.RESET_ALL
warnings.filterwarnings('ignore')
| Tabular Playground Series - Jan 2021 |
14,201,098 | %%time
display=full_data["display_address"].value_counts()
manager_id=full_data["manager_id"].value_counts()
building_id=full_data["building_id"].value_counts()
street=full_data["street_address"].value_counts()
bedrooms=full_data["bedrooms"].value_counts()
bathrooms=full_data["bathrooms"].value_counts()
created_dayofye... | path = '.. /input/tabular-playground-series-jan-2021/'
train_data = pd.read_csv(path + 'train.csv')
test_data = pd.read_csv(path + 'test.csv')
sample = pd.read_csv(path + 'sample_submission.csv' ) | Tabular Playground Series - Jan 2021 |
14,201,098 | num_cat_vars =[]
price_by_manager = full_data.groupby('manager_id')['price'].agg([np.min,np.max,np.median,np.mean] ).reset_index()
price_by_manager.columns = ['manager_id','min_price_by_manager',
'max_price_by_manager','median_price_by_manager','mean_price_by_manager']
full_data = pd.merge(full_data,price_by_manager, h... | train_data['cont13_cont4_mul'] = train_data['cont13']*train_data['cont4']
train_data['cont13_cont11_mul'] = train_data['cont13']*train_data['cont11']
train_data['cont13_cont7_mul'] = train_data['cont13']*train_data['cont7']
train_data['cont13_cont2_mul'] = train_data['cont13']*train_data['cont2']
train_data['cont13_con... | Tabular Playground Series - Jan 2021 |
14,201,098 | for comb in itertools.combinations(cat_vars, 2):
comb_var_name = comb[0] +'-'+ comb[1]
full_data [comb_var_name] = full_data [ comb[0]].astype(str)+'_' + full_data [ comb[1]].astype(str)
cat_vars.append(comb_var_name)
cat_vars<feature_engineering> | num_bins = int(1 + np.log2(len(train_data)))
train_data.loc[:,'bins'] = pd.cut(train_data['target'].to_numpy() ,bins=num_bins,labels=False)
features = [f'cont{x}' for x in range(1,15)]
features += [
'cont13_cont4_mul',
'cont13_cont11_mul',
'cont13_cont7_mul',
'cont13_cont2_mul',
'cont13_cont10_mul',
]
target_feature ... | Tabular Playground Series - Jan 2021 |
14,201,098 | full_data["features"].apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x]))
cntvec = CountVectorizer(stop_words='english', max_features=200)
feature_sparse =cntvec.fit_transform(full_data["features"]\
.apply(lambda x: " ".join(["_".join(i.split(" ")) for i in x])))
feature_vars = ['feature_' + v for v in cn... | def rmse_score(y_true, y_pred):
return np.sqrt(mean_squared_error(y_true, y_pred)) | Tabular Playground Series - Jan 2021 |
14,201,098 | LBL = preprocessing.LabelEncoder()
LE_vars=[]
LE_map=dict()
for cat_var in cat_vars:
print("Label Encoding %s" %(cat_var))
LE_var=cat_var+'_le'
full_data[LE_var]=LBL.fit_transform(full_data[cat_var])
LE_vars.append(LE_var)
LE_map[cat_var]=LBL.classes_
print("Label-encoded feaures: %s" %(LE_vars))<categorify> | seed = 2021
nfolds = 5
params={
'objective':'regression',
'metrics':'rmse',
'boosting':'gbdt',
'min_data_per_group': 5,
'num_leaves': 256,
'max_depth': -1,
'learning_rate': 0.005,
'subsample_for_bin': 200000,
'lambda_l1': 1.074622455507616e-05,
'lambda_l2': 2.0521330798729704e-06,
'n_jobs': -1,
'cat_smooth': 1.0,
'sile... | Tabular Playground Series - Jan 2021 |
14,201,098 | OHE = preprocessing.OneHotEncoder(sparse=True)
start=time.time()
OHE.fit(full_data[LE_vars])
OHE_sparse=OHE.transform(full_data[LE_vars])
print('One-hot-encoding finished in %f seconds' %(time.time() -start))
OHE_vars = [var[:-3] + '_' + str(level ).replace(' ','_')\
for var in cat_vars for level in LE_map[var] ]
pr... | lgbm_preds = np.zeros(test_data.shape[0])
kfold = StratifiedKFold(n_splits=nfolds,random_state=seed)
lgbm_scores = list()
for train_idx, valid_idx in kfold.split(X=train_data,y=bins):
lgb_train = lgb.Dataset(train_data[train_idx],target[train_idx])
lgb_valid = lgb.Dataset(train_data[valid_idx],target[valid_idx],refe... | Tabular Playground Series - Jan 2021 |
14,201,098 | full_vars = num_vars + date_num_vars + additional_num_vars + interactive_num_vars+ geo_cat_vars +geo_num_vars+ count_vars + LE_vars
train_x = sparse.hstack([full_data[full_vars],
feature_sparse,
desc_sparse,
st_addr_sparse] ).tocsr() [:train_size]
train_y = full_data['target'][:train_size].values
test_x = sparse.hstack... | params = {'lambda': 0.0030282073258141168,
'alpha': 0.01563845128469084,
'colsample_bytree': 0.55,
'subsample': 0.7,
'learning_rate': 0.01,
'max_depth': 15,
'random_state': 2020,
'min_child_weight': 257,
} | Tabular Playground Series - Jan 2021 |
14,201,098 | class MeanEncoder:
def __init__(self, categorical_features, n_splits=5, target_type='classification', prior_weight_func=None):
self.categorical_features = categorical_features
self.n_splits = n_splits
self.learned_stats = {}
if target_type == 'classification':
self.target_type = target_type
self.target_values = []
el... | xgb_preds = np.zeros(test_data.shape[0])
kfold = StratifiedKFold(n_splits=nfolds,random_state=seed)
params['random_state'] = seed
xgb_scores = list()
for train_idx, valid_idx in kfold.split(X=train_data,y=bins):
xgb_train = xgb.DMatrix(train_data[train_idx],label=target[train_idx])
xgb_valid = xgb.DMatrix(train_data... | Tabular Playground Series - Jan 2021 |
14,201,098 | mean_encoder = MeanEncoder(categorical_features=['manager_id','building_id'])
mean_encoded_train = mean_encoder.fit_transform(train_data, train_data['target'])
mean_encoded_test = mean_encoder.transform(test_data)
mean_coded_vars = list(set(mean_encoded_train.columns)- set(train_data.columns))
mean_coded_vars.append... | params = {'l2_leaf_reg': 0.02247766515106271,
'max_bin': 364,
'subsample': 0.6708650091202213,
'learning_rate': 0.010290546311954876,
'max_depth': 10,
'verbose':200,
'random_state': seed,
'min_data_in_leaf': 300,
'loss_function': 'RMSE',
'n_estimators': 25000,
'rsm':0.5,
'early_stopping_rounds':100} | Tabular Playground Series - Jan 2021 |
14,201,098 | CB_encoder = PolynomialWrapper(CatBoostEncoder())
train_cb = CB_encoder.fit_transform(full_data[:train_size][cat_vars], full_data[:train_size]['target'])
test_cb = CB_encoder.transform(full_data[train_size:][cat_vars])
CB_vars = [f'cb_{c}' for c in train_cb.columns]
train_cb.columns = CB_vars
test_cb.columns = CB_va... | cat_preds = np.zeros(test_data.shape[0])
kfold = StratifiedKFold(n_splits=nfolds,random_state=seed)
params['random_state'] = seed
cat_scores = list()
for train_idx, valid_idx in kfold.split(X=train_data,y=bins):
cat_train = Pool(train_data[train_idx],target[train_idx])
cat_valid = Pool(train_data[valid_idx],target[v... | Tabular Playground Series - Jan 2021 |
14,201,098 | full_vars = num_vars + date_num_vars + additional_num_vars + interactive_num_vars+ geo_cat_vars +geo_num_vars+ count_vars + LE_vars + mean_coded_vars + CB_vars
train_x = sparse.hstack([full_data[full_vars],
feature_sparse,
desc_sparse,
st_addr_sparse] ).tocsr() [:train_size]
train_y = full_data['target'][:train_size].v... | stacking_preds = pd.read_csv('.. /input/tps-simple-stacking/submission.csv')
stacking_preds = stacking_preds.target.to_numpy() | Tabular Playground Series - Jan 2021 |
14,201,098 | <prepare_x_and_y><EOS> | sample.target =(0.6 * lgbm_preds.ravel() + 0.2 * xgb_preds.ravel() + 0.1 * cat_preds.ravel() + 0.1 * stacking_preds)
sample.to_csv("submission.csv",index=False)
sample.head() | Tabular Playground Series - Jan 2021 |
14,593,174 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<merge> | sns.set(font_scale=1.4)
warnings.filterwarnings("ignore")
| Tabular Playground Series - Jan 2021 |
14,593,174 | mkt_price = full_data.groupby(['building_id', 'display_address', 'bedrooms', 'bathrooms'] ).price.mean().reset_index()
mkt_price = pd.merge(full_data[['building_id', 'display_address', 'bedrooms', 'bathrooms']],
mkt_price, how='left', on=['building_id', 'display_address', 'bedrooms', 'bathrooms'] ).price
full_data['mkt... | def root_mean_squared_error(y_true, y_pred):
return K.sqrt(K.mean(K.square(y_pred - y_true)) ) | Tabular Playground Series - Jan 2021 |
14,593,174 | full_data['photos_str'] = full_data['photos'].astype(str)
full_data['listing_uid'] = full_data[['manager_id', 'building_id','photos_str']].apply(lambda x: hashlib.md5(( x[0] + x[1] + x[2] ).encode() ).hexdigest() , axis=1)
full_data['posted_times'] = full_data.groupby('listing_uid' ).created_datetime.rank(method='fir... | def add_pca(train_df, test_df, cols, n_comp=20, fit_test = True, prefix='pca_', fit_test_first=False):
pca = PCA(n_components=n_comp, random_state=42)
pca_titles = [prefix+'_pca_'+str(x)for x in range(n_comp)]
temp_train = train_df.copy()
temp_test = test_df.copy()
for c in cols:
fv = temp_train[c].mean()
temp_train[c... | Tabular Playground Series - Jan 2021 |
14,593,174 | full_num_vars = num_vars + date_num_vars + additional_num_vars + interactive_num_vars+ geo_cat_vars +geo_num_vars + count_vars \
+ listing_vars + listing_quality_vars
full_cat_vars = LE_vars + mean_coded_vars + CB_vars
full_vars = full_num_vars + full_cat_vars
train_x = sparse.hstack([full_data[full_vars],
feature_spar... | PATH = '/kaggle/input/tabular-playground-series-jan-2021/'
train = pd.read_csv(PATH+'train.csv')
test = pd.read_csv(PATH+'test.csv')
submission = pd.read_csv(PATH+'sample_submission.csv')
FT_COLS = [x for x in train.columns if 'cont' in x]
TARGET='target'
print(train.shape)
train.head(10 ) | Tabular Playground Series - Jan 2021 |
14,593,174 | def p25(x):
return np.percentile(x, 25)
def p50(x):
return np.percentile(x, 50)
def p75(x):
return np.percentile(x, 75)
def nunique(x):
return np.size(np.unique(x))
def max_min(x):
return np.max(x)-np.min(x)
def p75_p25(x):
return np.percentile(x, 75)-np.percentile(x, 25)
def get_group_stats(df, stat_funcs, target... | original_feature_dict = {'cont1': 5,
'cont2': 10,
'cont3': 8,
'cont4': 8,
'cont5': 5,
'cont6': 6,
'cont7': 4,
'cont8': 3,
'cont9': 7,
'cont10':3,
'cont11': 4,
'cont12': 2,
'cont13': 3,
'cont14': 5,}
pca_dict = {
'extra_pca_0': 4,
'extra_pca_1': 1,
'extra_pca_2': 1,
'extra_pca_3': 2,
'extra_pca_4': 1,
'extra_pca_5': 1 }... | Tabular Playground Series - Jan 2021 |
14,593,174 | stat_funcs = {
'mean': np.mean,
'min': np.min,
'max': np.max,
'std': np.std,
'p25': p25,
'p50': p50,
'p75': p75,
'skew': skew,
'kurtosis': kurtosis,
'max_min': max_min,
'p75_p25': p75_p25
}
mgr_aggr = pd.DataFrame()
for num_var in num_vars + additional_num_vars + listing_quality_vars:
mgr_aggr = pd.concat([mgr_aggr,
ge... | mixture_title_cols=[]
for f in FT_COLS+pca_titles2:
train, test, titles = split_distributions(train, test,f,TARGET, n_comp=ft_dict[f], prefix=f+'_dim', add_labels=True)
mixture_title_cols+=titles
mixture_value_cols = []
for count,f in enumerate(FT_COLS+pca_titles2):
for d in range(ft_dict[f]):
t_median = train[f][trai... | Tabular Playground Series - Jan 2021 |
14,593,174 | full_num_vars = num_vars + date_num_vars + additional_num_vars + interactive_num_vars+ geo_cat_vars + count_vars \
+ listing_vars + listing_quality_vars
full_cat_vars = LE_vars + mean_coded_vars + CB_vars
full_vars = full_num_vars + full_cat_vars
train_x = sparse.hstack([full_data[full_vars],
feature_sparse,
desc_spars... | print('Total Original Features', len(FT_COLS))
print('Total Submixtures Labels', len(mixture_title_cols))
print('Total Submixtures Values', len(mixture_value_cols))
print('Total Feature Columns', len(FT_COLS)+len(mixture_title_cols)+len(mixture_value_cols)) | Tabular Playground Series - Jan 2021 |
14,593,174 | stat_funcs = {
'mean': np.mean,
'min': np.min,
'max': np.max,
'std': np.std,
'p25': p25,
'p50': p50,
'p75': p75,
'skew': skew,
'kurtosis': kurtosis,
'max_min': max_min,
'p75_p25': p75_p25
}
building_aggr = pd.DataFrame()
building_aggr = pd.concat([building_aggr,
get_group_stats(full_data, stat_funcs,
target_column='pri... | NAN_VALUE = 0.0
for d in mixture_value_cols:
train[d] = train[d].fillna(value=NAN_VALUE)
test[d] = test[d].fillna(value=NAN_VALUE ) | Tabular Playground Series - Jan 2021 |
14,593,174 | park_listings = full_data[full_data[['description', 'features']].apply(lambda x: 'park' in x[0] or 'park' in x[1], axis=1)][['latitude', 'longitude']]
park_n_clusters = 25
kms = KMeans(n_clusters=park_n_clusters)
kms.fit(park_listings)
park_dist_data = pd.DataFrame(kms.transform(full_data[['latitude', 'longitude']]),... | SCALE = True
if SCALE:
train, test = st_scale(train, test, FT_COLS)
for f in FT_COLS:
train[f] = np.clip(train[f], -2, 2)
test[f] = np.clip(test[f], -2, 2)
SCALE_DISTS = True
if SCALE_DISTS:
for d in mixture_value_cols:
TEMP_MAX = max(np.abs(train[d] ).max() ,
np.abs(test[d] ).max())
train[d] = train[d] / TEMP_MAX
... | Tabular Playground Series - Jan 2021 |
14,593,174 | subway_listings = full_data[full_data[['description', 'features']].apply(lambda x: 'subway' in x[0] or 'subway' in x[1], axis=1)][['latitude', 'longitude']]
subway_n_clusters = 400
kms = KMeans(n_clusters=subway_n_clusters)
kms.fit(subway_listings)
subway_dist_data = pd.DataFrame(kms.transform(full_data[['latitude', ... | train['outlier_filter'] = np.where(train[TARGET]<4, True, False)
print(' | Tabular Playground Series - Jan 2021 |
14,593,174 | nlp = spacy.load("en_core_web_sm")
def seq_to_token(seq, nlp=nlp):
doc = nlp(str(seq ).lower())
tokens = [token.text for token in doc if not(token.is_space | token.is_stop|token.like_num)]
return tokens
def tokens_to_vec(tokens, model, vec_size=10):
if len(tokens)==0:
return np.zeors(vec_size)
else:
return np.array(... | print('CV Benchmark - Random Guess of Median')
cv_bm = np.sqrt(mse(train[TARGET], np.full(( train[TARGET].shape), train[TARGET].median())))
cv_bm | Tabular Playground Series - Jan 2021 |
14,593,174 | building_model = FastText(size=10, window=3, min_count=1, workers=16)
building_model.build_vocab(sentences=building_by_mgr)
building_model.train(sentences=building_by_mgr.values, total_examples=len(building_by_mgr.values), epochs=5)
building_model['8a8b08e08888819a3e745005a8cd0408']<categorify> | def run_training(model, train_df, test_df,sample_submission, fold_col,
orig_features, mixture_val_cols,mixture_label_cols, target_col,
benchmark,
outlier_col=None, nn=False, epochs=10,batch_size=32, verbose=False,
dense=70, dout=0.15, dense_reg = 0.000001,act='elu'):
FOLD_VALUES = sorted([x for x in train_df[fold_col].... | Tabular Playground Series - Jan 2021 |
14,593,174 | building_emb = full_data['building_id'].apply(lambda x:building_model[x] ).values
building_emb = np.array([e.reshape(1,-1)for e in building_emb] ).reshape(-1,10)
building_emb<train_on_grid> | def keras_model(ft_orig, mixture_values, mixture_labels, n_layer=3,bnorm=True,dout=0.2,
dense=20,act='elu', dense_reg = 0.000001, descend_fraction = 0.9):
input1 = L.Input(shape=(len(ft_orig)) , name='input_orig')
input1_do = L.Dropout(0.1 )(input1)
XA = L.Dense(dense, activation=act, activity_regularizer=tf.keras.re... | Tabular Playground Series - Jan 2021 |
14,593,174 | manager_by_building = full_data.groupby('building_id')['manager_id'].apply(list)
manager_model = FastText(size=10, window=3, min_count=1, workers=16)
manager_model.build_vocab(sentences=manager_by_building)
manager_model.train(sentences=manager_by_building.values,
total_examples=len(manager_by_building.values), epoc... | oof, test_predictions, fold_errors = run_training(model, train, test,submission, 'fold', FT_COLS,
mixture_value_cols, mixture_title_cols, TARGET, cv_bm,
outlier_col='outlier_filter',
epochs=30,
batch_size=256,
verbose=False,
dense=70,
dout=0.15,
dense_reg = 0.000001,
act='elu', ) | Tabular Playground Series - Jan 2021 |
14,593,174 | manager_by_building = full_data.groupby('building_id')['manager_id'].apply(list)
manager_model = FastText(size=10, window=3, min_count=1, workers=16)
manager_model.build_vocab(sentences=manager_by_building)
manager_model.train(sentences=manager_by_building.values,
total_examples=len(manager_by_building.values), epoc... | print('Save Out of Fold Predictions')
oof = pd.DataFrame(columns=['oof_prediction'], index=train['id'], data=oof + TARGET_MEAN)
oof.to_csv('oof_predictions.csv', index=True)
oof.head(10 ) | Tabular Playground Series - Jan 2021 |
14,593,174 | desc_vec = np.vstack(( pickle.load(open('/kaggle/input/text-featurizer/train_desc_vec_lg.pkl', 'rb')) ,
pickle.load(open('/kaggle/input/text-featurizer/test_desc_vec_lg.pkl', 'rb'))))
<load_from_csv> | print('fold errors', fold_errors)
print('fold error std', np.array(fold_errors ).std() ) | Tabular Playground Series - Jan 2021 |
14,593,174 | <concatenate><EOS> | submission['target'] = test_predictions + TARGET_MEAN
submission.to_csv('submission.csv', index=False)
submission.head(5 ) | Tabular Playground Series - Jan 2021 |
14,401,709 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<train_model> | !pip install -q -U git+https://github.com/mljar/mljar-supervised.git@master
!pip install -q -U matplotlib==3.1.3 | Tabular Playground Series - Jan 2021 |
14,401,709 | sparse.save_npz('train_x.npz', train_x)
sparse.save_npz('test_x.npz', test_x)
<import_modules> | import pandas as pd
from supervised import AutoML | Tabular Playground Series - Jan 2021 |
14,401,709 | stemmer = PorterStemmer()
<load_from_csv> | train_data = pd.read_csv('.. /input/tabular-playground-series-jan-2021/train.csv')
test_data = pd.read_csv('.. /input/tabular-playground-series-jan-2021/test.csv')
x_cols = [f"cont{i}" for i in range(1,15)] | Tabular Playground Series - Jan 2021 |
14,401,709 | train = pd.read_csv("/kaggle/input/home-depot-product-search-relevance/train.csv.zip", encoding="ISO-8859-1")
test = pd.read_csv("/kaggle/input/home-depot-product-search-relevance/test.csv.zip", encoding="ISO-8859-1")
attr = pd.read_csv('/kaggle/input/home-depot-product-search-relevance/attributes.csv.zip')
desc = p... | automl = AutoML(mode="Explain")
automl.fit(train_data[x_cols], train_data["target"] ) | Tabular Playground Series - Jan 2021 |
14,401,709 | display(train.head(10))
print('Số lượng product_uid khác nhau trong tập train:', train.product_uid.nunique() )<count_unique_values> | automl = AutoML(mode="Compete", total_time_limit=4*3600)
automl.fit(train_data[x_cols], train_data["target"] ) | Tabular Playground Series - Jan 2021 |
14,401,709 | <count_unique_values><EOS> | preds = automl.predict(test_data)
sub = pd.DataFrame({"id":test_data.id, "target":preds})
sub.to_csv('submission.csv', index=False ) | Tabular Playground Series - Jan 2021 |
14,464,696 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<set_options> | import os
import numpy as np
import pandas as pd | Tabular Playground Series - Jan 2021 |
14,464,696 | pd.set_option('display.max_columns', 50 )<define_variables> | train = pd.read_csv(".. /input/tabular-playground-series-jan-2021/train.csv")
test = pd.read_csv(".. /input/tabular-playground-series-jan-2021/test.csv")
train.drop([170514], axis=0, inplace=True)
X = np.array(train.drop(["id", "target"], axis=1))
X_test = np.array(test.drop("id", axis=1))
y = np.array(train["target... | Tabular Playground Series - Jan 2021 |
14,464,696 | google_dict={
'steele stake': 'steel stake',
'gas mowe': 'gas mower',
'metal plate cover gcfi': 'metal plate cover gfci',
'lawn sprkinler': 'lawn sprinkler',
'ourdoor patio tile': 'outdoor patio tile',
'6 teir shelving': '6 tier shelving',
'storage shelve': 'storage shelf',
'American Standard Bone round toliet': 'Ameri... | SEED = 1380 | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all = pd.concat([train, test], axis=0, ignore_index=True )<merge> | kf1 = KFold(n_splits=10, shuffle=True, random_state=SEED)
kf2 = KFold(n_splits=10, shuffle=True, random_state=SEED+SEED ) | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all = pd.merge(data_all, desc, how = 'left', on = 'product_uid')
data_all = pd.merge(data_all, brand, how = 'left', on = 'product_uid' )<feature_engineering> | params_xgb = {
"booster": "gbtree",
"objective": "reg:squarederror",
"eval_metric": "rmse",
"max_depth": 15,
"eta": 0.0065,
"gamma": 0.005346636874993822,
"colsample_bytree": 0.5,
"subsample": 0.7,
"min_child_weight": 257,
"alpha": 0.01563,
"lambda": 0.003,
"tree_method": "hist",
"seed": SEED
} | Tabular Playground Series - Jan 2021 |
14,464,696 | attr_stripped = attr
attr_stripped['name'] = attr_stripped['name'].astype(str)
attr_stripped['name'] = attr_stripped['name'].apply(lambda s: re.sub(r"Bullet([0-9]+)", "", s))
attr_stripped['attribute'] = attr_stripped['name'] + " " + attr_stripped['value']
attr_test = attr_stripped.groupby('product_uid' ).agg({'attrib... | d_test = xgb.DMatrix(X_test ) | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all = pd.merge(data_all, attr_test, how = 'left', on = 'product_uid' )<categorify> | y_test_xgb = pd.DataFrame()
for tr_id, vl_id in kf1.split(X, y):
print("===========================================================")
X_train, X_val = X[tr_id, :], X[vl_id, :]
y_train, y_val = y[tr_id], y[vl_id]
d_train = xgb.DMatrix(X_train, y_train)
d_val = xgb.DMatrix(X_val, y_val)
model = xgb.train(params=params... | Tabular Playground Series - Jan 2021 |
14,464,696 | def string_edit(s):
if isinstance(s, str):
s = re.sub(r"(\w)\.( [A-Z])", r"\1 \2", s)
s = s.lower()
s = s.replace(" "," ")
s = s.replace(",","")
s = s.replace("$"," ")
s = s.replace("?"," ")
s = s.replace("-"," ")
s = s.replace("//","/")
s = s.replace(".. ",".")
s = s.replace(" / "," ")
s = s.replace(" \\ "," ... | pred_xgb = y_test_xgb | Tabular Playground Series - Jan 2021 |
14,464,696 | def str_stemmer(s):
return " ".join([stemmer.stem(word)for word in s.lower().split() ] )<string_transform> | y_test_xgb = y_test_xgb.mean(axis=1)
y_test_xgb = np.array(y_test_xgb ) | Tabular Playground Series - Jan 2021 |
14,464,696 | def str_common_word(str1, str2):
return sum(int(str2.find(word)>=0)for word in str1.split() )<feature_engineering> | pred_xgb_pseudo = pd.DataFrame()
for tr_id, vl_id in kf2.split(X_xgb, y_xgb):
print("========================================================================")
X_train, X_val = X_xgb[tr_id, :], X_xgb[vl_id, :]
y_train, y_val = y_xgb[tr_id], y_xgb[vl_id]
d_train = xgb.DMatrix(X_train, y_train)
d_val = xgb.DMatrix(X_va... | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all['search_term']=data_all['search_term'].map(lambda x: google_dict[x] if x in google_dict.keys() else x )<feature_engineering> | params_lgb = {
"task": "train",
"boosting_type": "gbdt",
"objective": "regression",
"metric": "rmse",
"learning_rate": 0.0057,
"num_leaves": 256,
"bagging_fraction": 0.8206341150202605,
"feature_fraction": 0.5,
"min_data_in_leaf": 100,
"lambda_l1": 1.074622455507616e-05,
"lambda_l2": 2.0521330798729704e-06,
"min_data_p... | Tabular Playground Series - Jan 2021 |
14,464,696 | start_time = time.time()
data_all['search_term'] = pd.Series(data_all['search_term'].map(lambda x:str_stemmer(str(x))))
data_all['product_title'] = pd.Series(data_all['product_title'].map(lambda x:str_stemmer(str(x))))
data_all['product_description'] = pd.Series(data_all['product_description'].map(lambda x:str_stemmer(... | y_test_lgb = pd.DataFrame()
for tr_id, vl_id in kf1.split(X, y):
print("======================================================================")
X_train, X_val = X[tr_id, :], X[vl_id, :]
y_train, y_val = y[tr_id], y[vl_id]
lgb_train = lgb.Dataset(X_train, label=y_train)
lgb_val = lgb.Dataset(X_val, label=y_val, refer... | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all['len_of_query'] = data_all['search_term'].map(lambda x:len(str(x ).split())).astype(np.int64 )<data_type_conversions> | pred_lgb = y_test_lgb | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all['len_of_brand'] = data_all['brand'].map(lambda x:len(str(x ).split())).astype(np.int64 )<data_type_conversions> | y_test_lgb = y_test_lgb.mean(axis=1)
y_test_lgb = np.array(y_test_lgb ) | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all['len_of_attribute'] = data_all['attribute'].map(lambda x:len(str(x ).split())).astype(np.int64 )<feature_engineering> | pred_lgb_pseudo = pd.DataFrame()
for tr_id, vl_id in kf2.split(X_lgb, y_lgb):
print("======================================================================")
X_train, X_val = X_lgb[tr_id, :], X_lgb[vl_id, :]
y_train, y_val = y_lgb[tr_id], y_lgb[vl_id]
lgb_train = lgb.Dataset(X_train, label=y_train)
lgb_val = lgb.Data... | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all['product_info'] = data_all['search_term']+'\t'+data_all['product_title']+'\t'+data_all['product_description']+'\t'+data_all['attribute']<feature_engineering> | pred = pd.concat([pred_xgb, pred_lgb], axis=1)
pred = pred.mean(axis=1)
pred_pseudo = pd.concat([pred_xgb_pseudo, pred_lgb_pseudo], axis=1)
pred_pseudo = pred_pseudo.mean(axis=1 ) | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all['word_in_title'] = data_all['product_info'].map(lambda x:str_common_word(str(x ).split('\t')[0],str(x ).split('\t')[1]))<feature_engineering> | sample_sub = pd.read_csv(".. /input/tabular-playground-series-jan-2021/sample_submission.csv")
sub = sample_sub.copy()
sub_pseudo = sample_sub.copy() | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all['word_in_description'] = data_all['product_info'].map(lambda x:str_common_word(str(x ).split('\t')[0],str(x ).split('\t')[2]))<feature_engineering> | sub["target"] = pred
sub | Tabular Playground Series - Jan 2021 |
14,464,696 | data_all['word_in_attributes'] = data_all['product_info'].map(lambda x:str_common_word(str(x ).split('\t')[0],str(x ).split('\t')[3]))<feature_engineering> | sub_pseudo["target"] = pred_pseudo
sub_pseudo | Tabular Playground Series - Jan 2021 |
14,464,696 | <feature_engineering><EOS> | sub.to_csv("submission.csv", index=False)
sub_pseudo.to_csv("submission_pseudo.csv", index=False ) | Tabular Playground Series - Jan 2021 |
14,498,987 | <SOS> metric: RMSE Kaggle data source: tabular-playground-series-jan-2021<feature_engineering> | %matplotlib inline
sns.set_style('whitegrid')
plt.style.use('seaborn-deep')
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams['font.serif'] = 'Ubuntu'
plt.rcParams['font.monospace'] = 'Ubuntu Mono'
plt.rcParams['font.size'] = 10
plt.rcParams['axes.labelsize'] = 12
plt.rcParams['axes.titlesize'] = 12
plt.rcParam... | Tabular Playground Series - Jan 2021 |
14,498,987 | data_all['ratio_title'] = data_all['word_in_title']/data_all['len_of_query']<feature_engineering> | base = '/kaggle/input/tabular-playground-series-jan-2021/'
data_file = base + "train.csv"
df = pd.read_csv(data_file)
df.head() | Tabular Playground Series - Jan 2021 |
14,498,987 | data_all['ratio_description'] = data_all['word_in_description']/data_all['len_of_query']<feature_engineering> | data_file = base + "test.csv"
df_test = pd.read_csv(data_file)
df_test.head() | Tabular Playground Series - Jan 2021 |
14,498,987 | data_all['ratio_attributes'] = data_all['word_in_attributes']/data_all['len_of_query']<feature_engineering> | df.drop('id', inplace=True, axis=1 ) | Tabular Playground Series - Jan 2021 |
14,498,987 | data_all['last_word_title_match'] = data_all['product_info'].map(lambda x:str_common_word(str(x ).split('\t')[0].split(" ")[-1],str(x ).split('\t')[1]))<feature_engineering> | print(df.shape)
df.drop_duplicates(inplace=True)
print(df.shape ) | Tabular Playground Series - Jan 2021 |
14,498,987 | data_all['last_word_description_match'] = data_all['product_info'].map(lambda x:str_common_word(str(x ).split('\t')[0].split(" ")[-1],str(x ).split('\t')[2]))<feature_engineering> | df.isna().any().sum() | Tabular Playground Series - Jan 2021 |
14,498,987 | data_all['first_word_title_match'] = data_all['product_info'].map(lambda x:str_common_word(str(x ).split('\t')[0].split(" ")[0],str(x ).split('\t')[1]))<feature_engineering> | skewed_features = df.apply(lambda x: x.skew() ).sort_values(ascending=False)
skewed_features | Tabular Playground Series - Jan 2021 |
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