Kaggle competition: PUBG Finish Placement Prediction (Kernels Only)
<SOS> metric: MAE Kaggle data source: pubg-finish-placement-prediction-(kernels-only)<save_to_csv>
warnings.filterwarnings("ignore")
 gc.enable()
print(os.listdir(".. /input"))
lasso = make_pipeline(RobustScaler() , Lasso(alpha = 0.00025, random_state = 1234))
lasso.fit(final_X_train, final_y_train)
 lasso_preds = np.expm1(lasso.predict(final_X_test))
sub = pd.DataFrame()
sub['Id'] = test_id
sub['SalePrice'] = lasso_preds 
<save_to_csv>
def feature_engineering(is_train=True,debug=True):
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('.. /input/test_V2.csv')
 print("remove some columns")
 target = 'winPlacePerc'
print("Adding Features")
 df['headshotrate'] = df['kills']/df['headshotKills']
df['killStreakrate'] = df['killStreaks']/df['kills']
df['healthitems'] = df['heals'] + df['boosts']
df['totalDistance'] = df['rideDistance'] + df["walkDistance"] + df["swimDistance"]
df['killPlace_over_maxPlace'] = df['killPlace'] / df['maxPlace']
df['headshotKills_over_kills'] = df['headshotKills'] / df['kills']
df['distance_over_weapons'] = df['totalDistance'] / df['weaponsAcquired']
df['walkDistance_over_heals'] = df['walkDistance'] / df['heals']
df['walkDistance_over_kills'] = df['walkDistance'] / df['kills']
df['killsPerWalkDistance'] = df['kills'] / df['walkDistance']
df["skill"] = df["headshotKills"] + df["roadKills"]
df[df == np.Inf] = np.NaN
df[df == np.NINF] = np.NaN
print("Removing Na's From DF")
 df.fillna(0, inplace=True)
 features = list(df.columns)
 features.remove("Id")
 features.remove("matchId")
 features.remove("groupId")
 features.remove("matchType")
 y = None
if is_train:
print("get target")
 y = df.groupby(['matchId','groupId'])[target].agg('mean')
 features.remove(target)
 print("get group mean feature")
 agg = df.groupby(['matchId','groupId'])[features].agg('mean')
 agg_rank = agg.groupby('matchId')[features].rank(pct=True ).reset_index()
if is_train: df_out = agg.reset_index() [['matchId','groupId']]
else: df_out = df[['matchId','groupId']]
df_out = df_out.merge(agg.reset_index() , suffixes=["", ""], how='left', on=['matchId', 'groupId'])
 df_out = df_out.merge(agg_rank, suffixes=["_mean", "_mean_rank"], how='left', on=['matchId', 'groupId'])
 print("get group max feature")
 agg = df.groupby(['matchId','groupId'])[features].agg('max')
 agg_rank = agg.groupby('matchId')[features].rank(pct=True ).reset_index()
df_out = df_out.merge(agg.reset_index() , suffixes=["", ""], how='left', on=['matchId', 'groupId'])
 df_out = df_out.merge(agg_rank, suffixes=["_max", "_max_rank"], how='left', on=['matchId', 'groupId'])
 print("get group min feature")
 agg = df.groupby(['matchId','groupId'])[features].agg('min')
 agg_rank = agg.groupby('matchId')[features].rank(pct=True ).reset_index()
df_out = df_out.merge(agg.reset_index() , suffixes=["", ""], how='left', on=['matchId', 'groupId'])
 df_out = df_out.merge(agg_rank, suffixes=["_min", "_min_rank"], how='left', on=['matchId', 'groupId'])
 print("get group size feature")
 agg = df.groupby(['matchId','groupId'] ).size().reset_index(name='group_size')
 df_out = df_out.merge(agg, how='left', on=['matchId', 'groupId'])
 print("get match mean feature")
 agg = df.groupby(['matchId'])[features].agg('mean' ).reset_index()
df_out = df_out.merge(agg, suffixes=["", "_match_mean"], how='left', on=['matchId'])
 print("get match size feature")
 agg = df.groupby(['matchId'] ).size().reset_index(name='match_size')
 df_out = df_out.merge(agg, how='left', on=['matchId'])
 df_out.drop(["matchId", "groupId"], axis=1, inplace=True)
 X = df_out
del df, df_out, agg, agg_rank
gc.collect()
return X, y
ridge = make_pipeline(RobustScaler() , Ridge(alpha = 6, random_state = 1234))
ridge.fit(final_X_train, final_y_train)
 ridge_preds = np.expm1(ridge.predict(final_X_test))
sub = pd.DataFrame()
sub['Id'] = test_id
sub['SalePrice'] = ridge_preds 
<train_on_grid>
x_train, y_train = feature_engineering(True,False)
 x_test, _ = feature_engineering(False,True )
xg_X_train, xg_X_test, xg_y_train, xg_y_test = train_test_split(final_X_train, final_y_train, test_size=0.33, random_state=1234)
 xg_model = XGBRegressor(n_estimators=100, seed = 1234)
 param_dict = {'max_depth': [3,4,5],
'min_child_weight': [2,3,4],
'learning_rate': [0.05, 0.1,0.15],
'gamma': [0.0, 0.1, 0.2]
}
start = time()
grid_search = GridSearchCV(xg_model, param_dict)
 grid_search.fit(xg_X_train, xg_y_train)
 print("GridSearch took %.2f seconds to complete." %(time() -start))
display(grid_search.best_params_ )<train_model>
x_train = reduce_mem_usage(x_train)
 x_test = reduce_mem_usage(x_test )
xg_model = XGBRegressor(n_estimators = 1000,
learning_rate = 0.1,
max_depth = 4,
min_child_weight = 4,
gamma = 0,
seed = 1234)
 start = time()
xg_model.fit(xg_X_train, xg_y_train)
 xg_preds = xg_model.predict(xg_X_test)
 print("Model took %.2f seconds to complete." %(time() -start))
print("RMSE: %.4f" % sqrt(mean_squared_error(xg_y_test, xg_preds)) )<train_model>
def post_rst(pred_test):
df_sub = pd.read_csv(".. /input/sample_submission_V2.csv")
 df_test = pd.read_csv(".. /input/test_V2.csv")
 df_sub['winPlacePerc'] = pred_test
df_sub = df_sub.merge(df_test[["Id", "matchId", "groupId", "maxPlace", "numGroups"]], on="Id", how="left")
 df_sub_group = df_sub.groupby(["matchId", "groupId"] ).first().reset_index()
df_sub_group["rank"] = df_sub_group.groupby(["matchId"])["winPlacePerc"].rank()
df_sub_group = df_sub_group.merge(
 df_sub_group.groupby("matchId")["rank"].max().to_frame("max_rank" ).reset_index() ,
on="matchId", how="left")
 df_sub_group["adjusted_perc"] =(df_sub_group["rank"] - 1)/(df_sub_group["numGroups"] - 1)
 df_sub = df_sub.merge(df_sub_group[["adjusted_perc", "matchId", "groupId"]], on=["matchId", "groupId"], how="left")
 df_sub["winPlacePerc"] = df_sub["adjusted_perc"]
df_sub.loc[df_sub.maxPlace == 0, "winPlacePerc"] = 0
df_sub.loc[df_sub.maxPlace == 1, "winPlacePerc"] = 1
subset = df_sub.loc[df_sub.maxPlace > 1]
gap = 1.0 /(subset.maxPlace.values - 1)
 new_perc = np.around(subset.winPlacePerc.values / gap)* gap
df_sub.loc[df_sub.maxPlace > 1, "winPlacePerc"] = new_perc
df_sub.loc[(df_sub.maxPlace > 1)&(df_sub.numGroups == 1), "winPlacePerc"] = 0
assert df_sub["winPlacePerc"].isnull().sum() == 0
df_sub[["Id", "winPlacePerc"]].to_csv("submission_adjusted.csv", index=False )
xg_model = XGBRegressor(n_estimators = 1000,
learning_rate = 0.1,
max_depth = 4,
min_child_weight = 4,
gamma = 0,
seed = 1234)
 xg_model.fit(final_X_train, final_y_train)
 xg_preds = np.expm1(xg_model.predict(final_X_test))<create_dataframe>
def nn_model(input_shape=x_train.shape[1], hidden_size=64):
model = Sequential()
model.add(Dense(hidden_size, input_dim=input_shape, kernel_initializer='normal'))
model.add(LeakyReLU(0.1))
model.add(Dense(hidden_size, kernel_initializer='normal'))
model.add(LeakyReLU(0.1))
model.add(Dense(hidden_size, kernel_initializer='normal'))
model.add(LeakyReLU(0.1))
model.add(Dense(hidden_size, kernel_initializer='normal'))
model.add(LeakyReLU(0.1))
model.add(Dense(1, kernel_initializer='normal'))
model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mae'])
 return model
sub = pd.DataFrame()
sub['Id'] = test_id
sub['SalePrice'] = xg_preds 
<train_model>
def train_val_split(x_train, y_train):
train_index = round(int(x_train.shape[0]*0.8))
dev_X = x_train[:train_index]
val_X = x_train[train_index:]
dev_y = y_train[:train_index]
val_y = y_train[train_index:]
del x_train, y_train
gc.collect() ;
return dev_X, val_X, dev_y, val_y
lasso = make_pipeline(RobustScaler() , Lasso(alpha = 0.00025, random_state = 1234))
lasso.fit(final_X_train, final_y_train)
 lasso_preds = np.expm1(lasso.predict(final_X_test))
ridge = make_pipeline(RobustScaler() , Ridge(alpha = 6, random_state = 1234))
ridge.fit(final_X_train, final_y_train)
 ridge_preds = np.expm1(ridge.predict(final_X_test))
xg_model = XGBRegressor(n_estimators = 1000,
learning_rate = 0.1,
max_depth = 4,
min_child_weight = 4,
gamma = 0,
seed = 1234)
 xg_model.fit(final_X_train, final_y_train)
 xg_preds = np.expm1(xg_model.predict(final_X_test))
weights = [0.5, 0.25, 0.25]
sub = pd.DataFrame()
sub['Id'] = test_id
sub['SalePrice'] =(ridge_preds*weights[0])+(lasso_preds*weights[1])+(xg_preds*weights[2])
 sub.to_csv('Ensemble Submission.csv',index=False )<init_hyperparams>
dev_X, val_X, dev_y, val_y = train_val_split(x_train, y_train)
 def run_lgb(train_X, train_y, val_X, val_y, x_test):
params = {"objective" : "regression", "metric" : "mae", 'n_estimators':5000, 'early_stopping_rounds':200,
"num_leaves" : 150, "learning_rate" : 0.05, "bagging_fraction" : 0.5,
"bagging_seed" : 0, "num_threads" : 4,"colsample_bytree" : 0.5
}
lgtrain = lgb.Dataset(train_X, label=train_y)
 lgval = lgb.Dataset(val_X, label=val_y)
 model = lgb.train(params, lgtrain, valid_sets=[lgtrain, lgval], early_stopping_rounds=200, verbose_eval=1000)
 pred_test_y = model.predict(x_test, num_iteration=model.best_iteration)
 return pred_test_y
pred_test_lgb = run_lgb(dev_X, dev_y, val_X, val_y, x_test)
 pred_test_lgb = pred_test_lgb.reshape(-1,1)
 del dev_X, val_X, dev_y, val_y
gc.collect()
lgb_num_leaves_max = 255
lgb_in_leaf = 50
lgb_lr = 0.0001
lgb_bagging = 7
xgb_max_depth = 20
xgb_min_child_weight = 75
xgb_lr = 0.0005
xgb_num_boost_round_max = 4000
w_lgb = 0.6
w_xgb = 0.3
w_logreg = 1 - w_lgb - w_xgb
w_logreg<set_options>
np.random.seed(42)
 std_scaler = StandardScaler().fit(x_train)
 x_train = std_scaler.transform(x_train)
 x_test = std_scaler.transform(x_test)
 mlp_model = nn_model(x_train.shape[1], hidden_size=64)
 mlp_model.fit(x_train, y_train, batch_size=128, epochs=8, verbose=1, validation_split=0.1, shuffle=True)
 pred_test_mlp = mlp_model.predict(x_test)
 del mlp_model
gc.collect()
warnings.filterwarnings("ignore" )<load_from_csv>
del x_train, y_train, x_test
gc.collect()
<feature_engineering><EOS> 
pred_test =(pred_test_mlp + pred_test_lgb)/ 2.0