Kaggle competition: PUBG Finish Placement Prediction (Kernels Only)
<SOS> metric: MAE Kaggle data source: pubg-finish-placement-prediction-(kernels-only)<categorify>

 gc.enable()
for ds in [df_train, df_test]:
ds['Title'] = [x.split(',')[1].split('.')[0].strip() for x in ds['Name']]
ds['Title'] = ds['Title'].replace(to_replace=['Mlle', 'Ms'], value='Miss')
 ds['Title'] = ds['Title'].replace(to_replace='Mme', value='Mrs')
 ds['Title'] = ds['Title'].apply(lambda i: i if i in ['Mr', 'Mrs', 'Miss', 'Master'] else 'Rare')
 print(df_train['Title'].value_counts() + df_test['Title'].value_counts())
 print(df_train[['Title', 'Survived']].groupby('Title', as_index=True ).mean().sort_values('Survived', ascending=False))
lb_encoder.fit(df_train['Title'])
 df_train, df_test = [x.join(pd.DataFrame(lb_encoder.transform(x['Title']), columns=lb_encoder.classes_)) for x in [df_train, df_test]]
df_train, df_test = [x.drop(['Name', 'Title'], axis=1)for x in [df_train, df_test]]<categorify>
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('.. /input/test_V2.csv')
 test_idx = df.Id
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 = np.array(df.groupby(['matchId','groupId'])[target].agg('mean'), dtype=np.float64)
 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
feature_names = list(df_out.columns)
 del df, df_out, agg, agg_rank
gc.collect()
return X, y, feature_names, test_idx
pclass_cols = ['UpperClass', 'MiddleClass', 'LowerClass']
lb_encoder.fit(df_train['Pclass'])
 df_test = df_test.join(pd.DataFrame(lb_encoder.transform(df_test['Pclass']), columns=pclass_cols))
df_train = df_train.join(pd.DataFrame(lb_encoder.transform(df_train['Pclass']), columns=pclass_cols))
df_test.drop('Pclass', axis=1, inplace=True)
 df_train.drop('Pclass', axis=1, inplace=True )<data_type_conversions>
x_train, y_train, train_columns, _ = feature_engineering(True,False )
for ds in [df_train, df_test]:
df_family =(ds['Parch'] + ds['SibSp'] + 1 ).astype(int)
 ds.drop(['Parch', 'SibSp'], axis=1, inplace=True)
 ds['IsAlone'] = df_family.map(lambda x: 1 if x == 1 else 0)
 ds['SmallFamily'] = df_family.map(lambda x: 1 if 2 <= x <= 4 else 0)
 ds['LargeFamily'] = df_family.map(lambda x: 1 if x >= 5 else 0 )<data_type_conversions>
x_test, _, _ , test_idx = feature_engineering(False,True )
for ds in [df_train, df_test]:
ds['IsFemale'] =(ds['Sex'] == 'female' ).astype(int)
 ds.drop('Sex', axis=1, inplace=True)
 g = sns.FacetGrid(df_train, col="IsFemale" ).map(plt.hist, "Survived" )<drop_column>
warnings.filterwarnings("ignore")
 color = sns.color_palette()
test_passenger_ids = np.array(df_test['PassengerId'])
 df_test.drop(['PassengerId', 'Ticket'], axis=1, inplace=True)
 df_train.drop(['PassengerId', 'Ticket'], axis=1, inplace=True )<prepare_x_and_y>
train_index = round(int(x_train.shape[0]*0.9))
dev_X = x_train[:train_index]
val_X = x_train[train_index:]
dev_y = y_train[:train_index]
val_y = y_train[train_index:]
gc.collect() ;
def run_lgb(train_X, train_y, val_X, val_y, x_test):
params = {"objective" : "regression", "metric" : "mae", 'n_estimators':20000, 'early_stopping_rounds':100,
"num_leaves" : 31, "learning_rate" : 0.05, "bagging_fraction" : 0.9,
"bagging_seed" : 0, "num_threads" : 4,"colsample_bytree" : 0.7
}
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, model
pred_test, model = run_lgb(dev_X, dev_y, val_X, val_y, x_test )
<train_on_grid><EOS> 
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 )