Kaggle competition: Google Cloud & NCAA® ML Competition 2020-NCAAM
<SOS> metric: logloss Kaggle data source: google-cloud-ncaa-march-madness-2020-division-1-mens-tournament<count_missing_values>
pd.set_option('max_columns', None)
 plt.style.use('fivethirtyeight')
 %matplotlib inline
py.init_notebook_mode(connected=True)
 warnings.filterwarnings('ignore')
 print("Libraries imported!" )
train_data.isnull().sum()<data_type_conversions>
class BaseModel(object):

 def __init__(self, train_df, test_df, target, features, categoricals=[],
n_splits=3, cv_method="KFold", group=None, task="regression",
parameter_tuning=False, scaler=None, verbose=True):
self.train_df = train_df
self.test_df = test_df
self.target = target
self.features = features
self.n_splits = n_splits
self.categoricals = categoricals
self.cv_method = cv_method
self.group = group
self.task = task
self.parameter_tuning = parameter_tuning
self.scaler = scaler
self.cv = self.get_cv()
self.verbose = verbose
self.params = self.get_params()
self.y_pred, self.score, self.model, self.oof, self.y_val, self.fi_df = self.fit()
def train_model(self, train_set, val_set):
raise NotImplementedError
def get_params(self):
raise NotImplementedError
def convert_dataset(self, x_train, y_train, x_val, y_val):
raise NotImplementedError
def convert_x(self, x):
return x
def calc_metric(self, y_true, y_pred):
if self.task == "classification":
return log_loss(y_true, y_pred)
 elif self.task == "regression":
return np.sqrt(mean_squared_error(y_true, y_pred))
def get_cv(self):
if self.cv_method == "KFold":
cv = KFold(n_splits=self.n_splits, shuffle=True, random_state=42)
 return cv.split(self.train_df)
 elif self.cv_method == "StratifiedKFold":
cv = StratifiedKFold(n_splits=self.n_splits, shuffle=True, random_state=42)
 return cv.split(self.train_df, self.train_df[self.target])
 elif self.cv_method == "TimeSeriesSplit":
cv = TimeSeriesSplit(max_train_size=None, n_splits=self.n_splits)
 return cv.split(self.train_df)
 elif self.cv_method == "GroupKFold":
cv = GroupKFold(n_splits=self.n_splits, shuffle=True, random_state=42)
 return cv.split(self.train_df, self.train_df[self.target], self.group)
 elif self.cv_method == "StratifiedGroupKFold":
cv = StratifiedGroupKFold(n_splits=self.n_splits, shuffle=True, random_state=42)
 return cv.split(self.train_df, self.train_df[self.target], self.group)
 def fit(self):
oof_pred = np.zeros(( self.train_df.shape[0],))
y_vals = np.zeros(( self.train_df.shape[0],))
y_pred = np.zeros(( self.test_df.shape[0],))
if self.group is not None:
if self.group in self.features:
self.features.remove(self.group)
 if self.group in self.categoricals:
self.categoricals.remove(self.group)
 fi = np.zeros(( self.n_splits, len(self.features)))
 if self.scaler is not None:
numerical_features = [f for f in self.features if f not in self.categoricals]
self.train_df[numerical_features] = self.train_df[numerical_features].fillna(self.train_df[numerical_features].median())
 self.test_df[numerical_features] = self.test_df[numerical_features].fillna(self.test_df[numerical_features].median())
 self.train_df[self.categoricals] = self.train_df[self.categoricals].fillna(self.train_df[self.categoricals].mode().iloc[0])
 self.test_df[self.categoricals] = self.test_df[self.categoricals].fillna(self.test_df[self.categoricals].mode().iloc[0])
 if self.scaler == "MinMax":
scaler = MinMaxScaler()
elif self.scaler == "Standard":
scaler = StandardScaler()
df = pd.concat([self.train_df[numerical_features], self.test_df[numerical_features]], ignore_index=True)
 scaler.fit(df[numerical_features])
 x_test = self.test_df.copy()
x_test[numerical_features] = scaler.transform(x_test[numerical_features])
 x_test = [np.absolute(x_test[i])for i in self.categoricals] + [x_test[numerical_features]]
else:
x_test = self.test_df[self.features]
for fold,(train_idx, val_idx)in enumerate(self.cv):
x_train, x_val = self.train_df.loc[train_idx, self.features], self.train_df.loc[val_idx, self.features]
y_train, y_val = self.train_df.loc[train_idx, self.target], self.train_df.loc[val_idx, self.target]
if self.scaler is not None:
x_train[numerical_features] = scaler.transform(x_train[numerical_features])
 x_val[numerical_features] = scaler.transform(x_val[numerical_features])
 x_train = [np.absolute(x_train[i])for i in self.categoricals] + [x_train[numerical_features]]
x_val = [np.absolute(x_val[i])for i in self.categoricals] + [x_val[numerical_features]]
train_set, val_set = self.convert_dataset(x_train, y_train, x_val, y_val)
 model, importance = self.train_model(train_set, val_set)
 fi[fold, :] = importance
conv_x_val = self.convert_x(x_val)
 y_vals[val_idx] = y_val
oof_pred[val_idx] = model.predict(conv_x_val ).reshape(oof_pred[val_idx].shape)
 x_test = self.convert_x(x_test)
 y_pred += model.predict(x_test ).reshape(y_pred.shape)/ self.n_splits
print('Partial score of fold {} is: {}'.format(fold, self.calc_metric(y_val, oof_pred[val_idx])))
 fi_df = pd.DataFrame()
for n in np.arange(self.n_splits):
tmp = pd.DataFrame()
tmp["features"] = self.features
tmp["importance"] = fi[n, :]
tmp["fold"] = n
fi_df = pd.concat([fi_df, tmp], ignore_index=True)
 gfi = fi_df[["features", "importance"]].groupby(["features"] ).mean().reset_index()
fi_df = fi_df.merge(gfi, on="features", how="left", suffixes=('', '_mean'))
loss_score = self.calc_metric(self.train_df[self.target], oof_pred)
 if self.verbose:
print('Our oof loss score is: ', loss_score)
 return y_pred, loss_score, model, oof_pred, y_vals, fi_df
def plot_feature_importance(self, rank_range=[1, 50]):
fig, ax = plt.subplots(1, 1, figsize=(10, 20))
sorted_df = self.fi_df.sort_values(by = "importance_mean", ascending=False ).reset_index().iloc[self.n_splits *(rank_range[0]-1): self.n_splits * rank_range[1]]
sns.barplot(data=sorted_df, x ="importance", y ="features", orient='h')
 ax.set_xlabel("feature importance")
 ax.spines['top'].set_visible(False)
 ax.spines['right'].set_visible(False)
 return sorted_df
train_data['Cabin'] = train_data['Cabin'].fillna("Missing")
 test_data['Cabin'] = test_data['Cabin'].fillna("Missing" )<count_missing_values>
class LgbModel(BaseModel):

 def train_model(self, train_set, val_set):
verbosity = 100 if self.verbose else 0
model = lgb.train(self.params, train_set, num_boost_round = 5000, valid_sets=[train_set, val_set], verbose_eval=verbosity)
 fi = model.feature_importance(importance_type="gain")
 return model, fi
def convert_dataset(self, x_train, y_train, x_val, y_val):
train_set = lgb.Dataset(x_train, y_train, categorical_feature=self.categoricals)
 val_set = lgb.Dataset(x_val, y_val, categorical_feature=self.categoricals)
 return train_set, val_set
def get_params(self):
params = {
'num_leaves': 127,
'min_data_in_leaf': 50,
'max_depth': -1,
'learning_rate': 0.005,
"boosting_type": "gbdt",
"bagging_seed": 11,
"verbosity": -1,
'random_state': 42,
}
if self.task == "regression":
params["objective"] = "regression"
params["metric"] = "rmse"
elif self.task == "classification":
params["objective"] = "binary"
params["metric"] = "binary_logloss"
if self.parameter_tuning == True:
def objective(trial):
train_x, test_x, train_y, test_y = train_test_split(self.train_df[self.features],
self.train_df[self.target],
test_size=0.3, random_state=42)
 dtrain = lgb.Dataset(train_x, train_y, categorical_feature=self.categoricals)
 dtest = lgb.Dataset(test_x, test_y, categorical_feature=self.categoricals)
 hyperparams = {'num_leaves': trial.suggest_int('num_leaves', 24, 1024),
'boosting_type': 'gbdt',
'objective': params["objective"],
'metric': params["metric"],
'max_depth': trial.suggest_int('max_depth', 4, 16),
'min_child_weight': trial.suggest_int('min_child_weight', 1, 20),
'feature_fraction': trial.suggest_uniform('feature_fraction', 0.4, 1.0),
'bagging_fraction': trial.suggest_uniform('bagging_fraction', 0.4, 1.0),
'bagging_freq': trial.suggest_int('bagging_freq', 1, 7),
'min_child_samples': trial.suggest_int('min_child_samples', 5, 100),
'lambda_l1': trial.suggest_loguniform('lambda_l1', 1e-8, 10.0),
'lambda_l2': trial.suggest_loguniform('lambda_l2', 1e-8, 10.0),
'early_stopping_rounds': 100
}
model = lgb.train(hyperparams, dtrain, valid_sets=dtest, verbose_eval=500)
 pred = model.predict(test_x)
 if self.task == "classification":
return log_loss(test_y, pred)
 elif self.task == "regression":
return np.sqrt(mean_squared_error(test_y, pred))
study = optuna.create_study(direction='minimize')
 study.optimize(objective, n_trials=50)
 print('Number of finished trials: {}'.format(len(study.trials)))
 print('Best trial:')
 trial = study.best_trial
print(' Value: {}'.format(trial.value))
print(' Params: ')
 for key, value in trial.params.items() :
print(' {}: {}'.format(key, value))
params = trial.params
params["learning_rate"] = 0.001
plot_optimization_history(study)
 return params
train_data.isnull().sum()<count_missing_values>
class CatbModel(BaseModel):

 def train_model(self, train_set, val_set):
verbosity = 100 if self.verbose else 0
if self.task == "regression":
model = CatBoostRegressor(**self.params)
 elif self.task == "classification":
model = CatBoostClassifier(**self.params)
 model.fit(train_set['X'], train_set['y'], eval_set=(val_set['X'], val_set['y']),
verbose=verbosity, cat_features=self.categoricals)
 return model, model.get_feature_importance()
def convert_dataset(self, x_train, y_train, x_val, y_val):
train_set = {'X': x_train, 'y': y_train}
val_set = {'X': x_val, 'y': y_val}
return train_set, val_set
def get_params(self):
params = { 'task_type': "CPU",
'learning_rate': 0.01,
'iterations': 1000,
'random_seed': 42,
'use_best_model': True
}
if self.task == "regression":
params["loss_function"] = "RMSE"
elif self.task == "classification":
params["loss_function"] = "Logloss"
return params
test_data.isnull().sum()<feature_engineering>
class Mish(Layer):
def __init__(self, **kwargs):
super(Mish, self ).__init__(**kwargs)
 def build(self, input_shape):
super(Mish, self ).build(input_shape)
 def call(self, x):
return x * K.tanh(K.softplus(x))
def compute_output_shape(self, input_shape):
return input_shape
class LayerNormalization(keras.layers.Layer):
def __init__(self,
center=True,
scale=True,
epsilon=None,
gamma_initializer='ones',
beta_initializer='zeros',
gamma_regularizer=None,
beta_regularizer=None,
gamma_constraint=None,
beta_constraint=None,
**kwargs):

 super(LayerNormalization, self ).__init__(**kwargs)
 self.supports_masking = True
self.center = center
self.scale = scale
if epsilon is None:
epsilon = K.epsilon() * K.epsilon()
self.epsilon = epsilon
self.gamma_initializer = keras.initializers.get(gamma_initializer)
 self.beta_initializer = keras.initializers.get(beta_initializer)
 self.gamma_regularizer = keras.regularizers.get(gamma_regularizer)
 self.beta_regularizer = keras.regularizers.get(beta_regularizer)
 self.gamma_constraint = keras.constraints.get(gamma_constraint)
 self.beta_constraint = keras.constraints.get(beta_constraint)
 self.gamma, self.beta = None, None
def get_config(self):
config = {
'center': self.center,
'scale': self.scale,
'epsilon': self.epsilon,
'gamma_initializer': keras.initializers.serialize(self.gamma_initializer),
'beta_initializer': keras.initializers.serialize(self.beta_initializer),
'gamma_regularizer': keras.regularizers.serialize(self.gamma_regularizer),
'beta_regularizer': keras.regularizers.serialize(self.beta_regularizer),
'gamma_constraint': keras.constraints.serialize(self.gamma_constraint),
'beta_constraint': keras.constraints.serialize(self.beta_constraint),
}
base_config = super(LayerNormalization, self ).get_config()
return dict(list(base_config.items())+ list(config.items()))
def compute_output_shape(self, input_shape):
return input_shape
def compute_mask(self, inputs, input_mask=None):
return input_mask
def build(self, input_shape):
shape = input_shape[-1:]
if self.scale:
self.gamma = self.add_weight(
 shape=shape,
initializer=self.gamma_initializer,
regularizer=self.gamma_regularizer,
constraint=self.gamma_constraint,
name='gamma', 
)
 if self.center:
self.beta = self.add_weight(
 shape=shape,
initializer=self.beta_initializer,
regularizer=self.beta_regularizer,
constraint=self.beta_constraint,
name='beta', 
)
 super(LayerNormalization, self ).build(input_shape)
 def call(self, inputs, training=None):
mean = K.mean(inputs, axis=-1, keepdims=True)
 variance = K.mean(K.square(inputs - mean), axis=-1, keepdims=True)
 std = K.sqrt(variance + self.epsilon)
 outputs =(inputs - mean)/ std
if self.scale:
outputs *= self.gamma
if self.center:
outputs += self.beta
return outputs
test_data['Fare'] = test_data['Fare'].median()<count_missing_values>
class NeuralNetworkModel(BaseModel):

 def train_model(self, train_set, val_set):
inputs = []
embeddings = []
embedding_out_dim = self.params['embedding_out_dim']
n_neuron = self.params['hidden_units']
for i in self.categoricals:
input_ = Input(shape=(1,))
embedding = Embedding(int(np.absolute(self.train_df[i] ).max() + 1), embedding_out_dim, input_length=1 )(input_)
 embedding = Reshape(target_shape=(embedding_out_dim,))(embedding)
 inputs.append(input_)
 embeddings.append(embedding)
 input_numeric = Input(shape=(len(self.features)- len(self.categoricals),))
embedding_numeric = Dense(n_neuron )(input_numeric)
 embedding_numeric = Mish()(embedding_numeric)
 inputs.append(input_numeric)
 embeddings.append(embedding_numeric)
 x = Concatenate()(embeddings)
 for i in np.arange(self.params['hidden_layers'] - 1):
x = Dense(n_neuron //(2 *(i+1)) )(x)
 x = Mish()(x)
 x = Dropout(self.params['hidden_dropout'] )(x)
 x = LayerNormalization()(x)
 if self.task == "regression":
out = Dense(1, activation="linear", name = "out" )(x)
 loss = "mse"
elif self.task == "classification":
out = Dense(1, activation='sigmoid', name = 'out' )(x)
 loss = "binary_crossentropy"
model = Model(inputs=inputs, outputs=out)
 model.compile(loss=loss, optimizer=Adam(lr=1e-04, beta_1=0.9, beta_2=0.999, decay=1e-04))
er = EarlyStopping(patience=10, min_delta=1e-4, restore_best_weights=True, monitor='val_loss')
 ReduceLR = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=7, verbose=1, epsilon=1e-4, mode='min')
 model.fit(train_set['X'], train_set['y'], callbacks=[er, ReduceLR],
epochs=self.params['epochs'], batch_size=self.params['batch_size'],
validation_data=[val_set['X'], val_set['y']])
 fi = np.zeros(len(self.features))
return model, fi
def convert_dataset(self, x_train, y_train, x_val, y_val):
train_set = {'X': x_train, 'y': y_train}
val_set = {'X': x_val, 'y': y_val}
return train_set, val_set
def get_params(self):

 params = {
'input_dropout': 0.0,
'hidden_layers': 2,
'hidden_units': 64,
'embedding_out_dim': 4,
'hidden_activation': 'relu',
'hidden_dropout': 0.02,
'batch_norm': 'before_act',
'optimizer': {'type': 'adam', 'lr': 0.001},
'batch_size': 256,
'epochs': 80
}
return params
train_data.isnull().sum()<count_missing_values>
data_dict = {}
for i in glob.glob('/kaggle/input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/MDataFiles_Stage1/*'):
name = i.split('/')[-1].split('.')[0]
if name != 'MTeamSpellings':
data_dict[name] = pd.read_csv(i)
 else:
data_dict[name] = pd.read_csv(i, encoding='cp1252' )
test_data.isnull().sum()<categorify>
data_dict['MNCAATourneySeeds']['Seed'] = data_dict['MNCAATourneySeeds']['Seed'].apply(lambda x: int(x[1:3]))
data_dict[fname].head()
train_data = pd.get_dummies(train_data, columns=["Sex"], drop_first=True)
 train_data = pd.get_dummies(train_data, columns=["Embarked"],drop_first=True)
 train_data['Fare'] = train_data['Fare'].astype(int)
 train_data.loc[train_data.Fare<=7.91,'Fare']=0
train_data.loc[(train_data.Fare>7.91)&(train_data.Fare<=14.454),'Fare']=1
train_data.loc[(train_data.Fare>14.454)&(train_data.Fare<=31),'Fare']=2
train_data.loc[(train_data.Fare>31),'Fare']=3
train_data['Age']=train_data['Age'].astype(int)
 train_data.loc[ train_data['Age'] <= 16, 'Age']= 0
train_data.loc[(train_data['Age'] > 16)&(train_data['Age'] <= 32), 'Age'] = 1
train_data.loc[(train_data['Age'] > 32)&(train_data['Age'] <= 48), 'Age'] = 2
train_data.loc[(train_data['Age'] > 48)&(train_data['Age'] <= 64), 'Age'] = 3
train_data.loc[train_data['Age'] > 64, 'Age'] = 4<categorify>
test = pd.read_csv('.. /input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/MSampleSubmissionStage1_2020.csv')
 print(test.shape)
 test.head()
test_data = pd.get_dummies(test_data, columns=["Sex"], drop_first=True)
 test_data = pd.get_dummies(test_data, columns=["Embarked"],drop_first=True)
 test_data['Fare'] = test_data['Fare'].astype(int)
 test_data.loc[test_data.Fare<=7.91,'Fare']=0
test_data.loc[(test_data.Fare>7.91)&(test_data.Fare<=14.454),'Fare']=1
test_data.loc[(test_data.Fare>14.454)&(test_data.Fare<=31),'Fare']=2
test_data.loc[(test_data.Fare>31),'Fare']=3
test_data['Age']=test_data['Age'].astype(int)
 test_data.loc[ test_data['Age'] <= 16, 'Age']= 0
test_data.loc[(test_data['Age'] > 16)&(test_data['Age'] <= 32), 'Age'] = 1
test_data.loc[(test_data['Age'] > 32)&(test_data['Age'] <= 48), 'Age'] = 2
test_data.loc[(test_data['Age'] > 48)&(test_data['Age'] <= 64), 'Age'] = 3
test_data.loc[test_data['Age'] > 64, 'Age'] = 4<drop_column>
test = test.drop(['Pred'], axis=1)
 test['Season'] = test['ID'].apply(lambda x: int(x.split('_')[0]))
test['WTeamID'] = test['ID'].apply(lambda x: int(x.split('_')[1]))
test['LTeamID'] = test['ID'].apply(lambda x: int(x.split('_')[2]))
test.head()
train_data.drop(['Ticket','Cabin','Name'],axis=1,inplace=True)
 test_data.drop(['Ticket','Cabin','Name'],axis=1,inplace=True )<count_values>
gameCities = pd.merge(data_dict['MGameCities'], data_dict['Cities'], how='left', on=['CityID'])
 cols_to_use = gameCities.columns.difference(train.columns ).tolist() + ["Season", "WTeamID", "LTeamID"]
train = train.merge(gameCities[cols_to_use].drop_duplicates(subset=["Season", "WTeamID", "LTeamID"]),
how="left", on=["Season", "WTeamID", "LTeamID"])
 train.head()
cols_to_use = data_dict["MSeasons"].columns.difference(train.columns ).tolist() + ["Season"]
train = train.merge(data_dict["MSeasons"][cols_to_use].drop_duplicates(subset=["Season"]),
how="left", on=["Season"])
 train.head()
cols_to_use = data_dict["MTeams"].columns.difference(train.columns ).tolist()
train = train.merge(data_dict["MTeams"][cols_to_use].drop_duplicates(subset=["TeamID"]),
how="left", left_on=["WTeamID"], right_on=["TeamID"])
 train.drop(['TeamID'], axis=1, inplace=True)
 train = train.merge(data_dict["MTeams"][cols_to_use].drop_duplicates(subset=["TeamID"]),
how="left", left_on=["LTeamID"], right_on=["TeamID"], suffixes=('_W', '_L'))
train.drop(['TeamID'], axis=1, inplace=True)
 print(train.shape)
 train.head()
train_data.Survived.value_counts() /len(train_data)*100 
<groupby>
cols_to_use = data_dict["MTeamCoaches"].columns.difference(train.columns ).tolist() + ["Season"]
train = train.merge(data_dict["MTeamCoaches"][cols_to_use].drop_duplicates(subset=["Season","TeamID"]),
how="left", left_on=["Season","WTeamID"], right_on=["Season","TeamID"])
 train.drop(['TeamID'], axis=1, inplace=True)
 train = train.merge(data_dict["MTeamCoaches"][cols_to_use].drop_duplicates(subset=["Season","TeamID"]),
how="left", left_on=["Season","LTeamID"], right_on=["Season","TeamID"], suffixes=('_W', '_L'))
train.drop(['TeamID'], axis=1, inplace=True)
 print(train.shape)
 train.head()
train_data.groupby("Survived" ).mean()<groupby>
cols_to_use = data_dict['MNCAATourneySeeds'].columns.difference(train.columns ).tolist() + ['Season']
train = train.merge(data_dict['MNCAATourneySeeds'][cols_to_use].drop_duplicates(subset=["Season","TeamID"]),
how='left', left_on=['Season', 'WTeamID'], right_on=['Season', 'TeamID'])
 train.drop(['TeamID'], axis=1, inplace=True)
 train = train.merge(data_dict['MNCAATourneySeeds'][cols_to_use].drop_duplicates(subset=["Season","TeamID"]),
how='left', left_on=['Season', 'LTeamID'], right_on=['Season', 'TeamID'], suffixes=('_W', '_L'))
train.drop(['TeamID'], axis=1, inplace=True)
 print(train.shape)
 train.head()
train_data.groupby("Sex_male" ).mean()<import_modules>
cols_to_use = gameCities.columns.difference(test.columns ).tolist() + ["Season", "WTeamID", "LTeamID"]
test = test.merge(gameCities[cols_to_use].drop_duplicates(subset=["Season", "WTeamID", "LTeamID"]),
how="left", on=["Season", "WTeamID", "LTeamID"])
 del gameCities
gc.collect()
test.head()
cols_to_use = data_dict["MSeasons"].columns.difference(test.columns ).tolist() + ["Season"]
test = test.merge(data_dict["MSeasons"][cols_to_use].drop_duplicates(subset=["Season"]),
how="left", on=["Season"])
 test.head()
cols_to_use = data_dict["MTeams"].columns.difference(test.columns ).tolist()
test = test.merge(data_dict["MTeams"][cols_to_use].drop_duplicates(subset=["TeamID"]),
how="left", left_on=["WTeamID"], right_on=["TeamID"])
 test.drop(['TeamID'], axis=1, inplace=True)
 test = test.merge(data_dict["MTeams"][cols_to_use].drop_duplicates(subset=["TeamID"]),
how="left", left_on=["LTeamID"], right_on=["TeamID"], suffixes=('_W', '_L'))
test.drop(['TeamID'], axis=1, inplace=True)
 test.head()
cols_to_use = data_dict["MTeamCoaches"].columns.difference(test.columns ).tolist() + ["Season"]
test = test.merge(data_dict["MTeamCoaches"][cols_to_use].drop_duplicates(subset=["Season","TeamID"]),
how="left", left_on=["Season","WTeamID"], right_on=["Season","TeamID"])
 test.drop(['TeamID'], axis=1, inplace=True)
 test = test.merge(data_dict["MTeamCoaches"][cols_to_use].drop_duplicates(subset=["Season","TeamID"]),
how="left", left_on=["Season","LTeamID"], right_on=["Season","TeamID"], suffixes=('_W', '_L'))
test.drop(['TeamID'], axis=1, inplace=True)
 cols_to_use = data_dict['MNCAATourneySeeds'].columns.difference(test.columns ).tolist() + ['Season']
test = test.merge(data_dict['MNCAATourneySeeds'][cols_to_use].drop_duplicates(subset=["Season","TeamID"]),
how='left', left_on=['Season', 'WTeamID'], right_on=['Season', 'TeamID'])
 test.drop(['TeamID'], axis=1, inplace=True)
 test = test.merge(data_dict['MNCAATourneySeeds'][cols_to_use].drop_duplicates(subset=["Season","TeamID"]),
how='left', left_on=['Season', 'LTeamID'], right_on=['Season', 'TeamID'], suffixes=('_W', '_L'))
test.drop(['TeamID'], axis=1, inplace=True)
 print(test.shape)
 test.head()
from sklearn.metrics import accuracy_score, log_loss
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
from sklearn import metrics<split>
not_exist_in_test = [c for c in train.columns.values.tolist() if c not in test.columns.values.tolist() ]
print(not_exist_in_test)
 train = train.drop(not_exist_in_test, axis=1)
 train.head()
X = train_data.drop(['Survived'], axis=1)
 y = train_data["Survived"]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.22, random_state = 5 )<train_model>
team_win_score = regularSeason.groupby(['Season', 'WTeamID'] ).agg({'WScore':['sum', 'count', 'var']} ).reset_index()
team_win_score.columns = [' '.join(col ).strip() for col in team_win_score.columns.values]
team_loss_score = regularSeason.groupby(['Season', 'LTeamID'] ).agg({'LScore':['sum', 'count', 'var']} ).reset_index()
team_loss_score.columns = [' '.join(col ).strip() for col in team_loss_score.columns.values]
del regularSeason
gc.collect()
logReg = LogisticRegression()
logReg.fit(X_train,y_train )<predict_on_test>
train = pd.merge(train, team_win_score, how='left', left_on=['Season', 'WTeamID'], right_on=['Season', 'WTeamID'])
 train = pd.merge(train, team_loss_score, how='left', left_on=['Season', 'LTeamID'], right_on=['Season', 'LTeamID'])
 train = pd.merge(train, team_loss_score, how='left', left_on=['Season', 'WTeamID'], right_on=['Season', 'LTeamID'])
 train = pd.merge(train, team_win_score, how='left', left_on=['Season', 'LTeamID_x'], right_on=['Season', 'WTeamID'])
 train.drop(['LTeamID_y', 'WTeamID_y'], axis=1, inplace=True)
 train.head()
logReg_predict = logReg.predict(X_test)
 logReg_score = logReg.score(X_test,y_test)
 print("Logistic Regression Score :",logReg_score )<compute_test_metric>
test = pd.merge(test, team_win_score, how='left', left_on=['Season', 'WTeamID'], right_on=['Season', 'WTeamID'])
 test = pd.merge(test, team_loss_score, how='left', left_on=['Season', 'LTeamID'], right_on=['Season', 'LTeamID'])
 test = pd.merge(test, team_loss_score, how='left', left_on=['Season', 'WTeamID'], right_on=['Season', 'LTeamID'])
 test = pd.merge(test, team_win_score, how='left', left_on=['Season', 'LTeamID_x'], right_on=['Season', 'WTeamID'])
 test.drop(['LTeamID_y', 'WTeamID_y'], axis=1, inplace=True)
 test.head()
print("Accuracy Score of Logistic Regression Model:")
 print(metrics.accuracy_score(y_test,logReg_predict))
print("
","Classification Report:")
 print(metrics.classification_report(y_test,logReg_predict),'
' )<train_model>
def preprocess(df):
df['x_score'] = df['WScore sum_x'] + df['LScore sum_y']
df['y_score'] = df['WScore sum_y'] + df['LScore sum_x']
df['x_count'] = df['WScore count_x'] + df['LScore count_y']
df['y_count'] = df['WScore count_y'] + df['WScore count_x']
df['x_var'] = df['WScore var_x'] + df['LScore var_x']
df['y_var'] = df['WScore var_y'] + df['LScore var_y']
return df
train = preprocess(train)
 test = preprocess(test )
SVC_model = SVC(probability=True)
 SVC_model.fit(X_train,y_train )<predict_on_test>
train_win = train.copy()
train_los = train.copy()
train_win = train_win[['Seed_W', 'Seed_L', 'TeamName_W', 'TeamName_L', 'CoachName_W', 'CoachName_L',
'x_score', 'y_score', 'x_count', 'y_count', 'x_var', 'y_var']]
train_los = train_los[['Seed_L', 'Seed_W', 'TeamName_L', 'TeamName_W', 'CoachName_L', 'CoachName_W',
'y_score', 'x_score', 'x_count', 'y_count', 'x_var', 'y_var']]
train_win.columns = ['Seed_1', 'Seed_2', 'TeamName_1', 'TeamName_2', 'CoachName_1', 'CoachName_2',
'Score_1', 'Score_2', 'Count_1', 'Count_2', 'Var_1', 'Var_2']
train_los.columns = ['Seed_1', 'Seed_2', 'TeamName_1', 'TeamName_2', 'CoachName_1', 'CoachName_2',
'Score_1', 'Score_2', 'Count_1', 'Count_2', 'Var_1', 'Var_2']
test = test[['ID', 'Seed_W', 'Seed_L', 'TeamName_W', 'TeamName_L', 'CoachName_W', 'CoachName_L',
'x_score', 'y_score', 'x_count', 'y_count', 'x_var', 'y_var']]
test.columns = ['ID', 'Seed_1', 'Seed_2', 'TeamName_1', 'TeamName_2', 'CoachName_1', 'CoachName_2',
'Score_1', 'Score_2', 'Count_1', 'Count_2', 'Var_1', 'Var_2']
SVC_predict = SVC_model.predict(X_test)
 SVC_score = SVC_model.score(X_test,y_test)
 print("Support Vector Classifier Score :",SVC_score )<compute_test_metric>
def feature_engineering(df):
df['Seed_diff'] = df['Seed_1'] - df['Seed_2']
df['Score_diff'] = df['Score_1'] - df['Score_2']
df['Count_diff'] = df['Count_1'] - df['Count_2']
df['Var_diff'] = df['Var_1'] - df['Var_2']
df['Mean_score1'] = df['Score_1'] / df['Count_1']
df['Mean_score2'] = df['Score_2'] / df['Count_2']
df['Mean_score_diff'] = df['Mean_score1'] - df['Mean_score2']
df['FanoFactor_1'] = df['Var_1'] / df['Mean_score1']
df['FanoFactor_2'] = df['Var_2'] / df['Mean_score2']
return df
train_win = feature_engineering(train_win)
 train_los = feature_engineering(train_los)
 test = feature_engineering(test )
print("Accuracy Score of Support Vector Classifier SVC Model:")
 print(metrics.accuracy_score(y_test,SVC_predict))
print("
","Classification Report:")
 print(metrics.classification_report(y_test,SVC_predict),'
' )<train_model>
data = pd.concat(( train_win, train_los)).reset_index(drop=True)
 print(data.shape)
 data.head()
decisionTreeModel = DecisionTreeClassifier(max_leaf_nodes=17, random_state=0)
 decisionTreeModel.fit(X_train, y_train )<predict_on_test>
categoricals = ["CoachName_1", "CoachName_2", "TeamName_1", "TeamName_2"]
for c in categoricals:
le = LabelEncoder()
data[c] = data[c].fillna("NaN")
 data[c] = le.fit_transform(data[c])
 test[c] = le.transform(test[c])
 data.head()
decisionTree_predict = decisionTreeModel.predict(X_test)
 decisionTree_score = decisionTreeModel.score(X_test,y_test)
 print("Decision Tree Classifier Score :",decisionTree_score )<compute_test_metric>
target = 'result'
features = data.columns.values.tolist()
features.remove(target )
print("Accuracy Score of Decision Tree Classifier Model:")
 print(metrics.accuracy_score(y_test,decisionTree_predict))
print("
","Classification Report:")
 print(metrics.classification_report(y_test,decisionTree_predict),'
' )<train_model>
nn = NeuralNetworkModel(data, test, target, features, categoricals=categoricals, n_splits=10,
cv_method="StratifiedKFold", group=None, task="classification", scaler="MinMax", verbose=True )
Random_forest = RandomForestClassifier(n_estimators=17)
 Random_forest.fit(X_train,y_train )<predict_on_test>
lgbm = LgbModel(data, test, target, features, categoricals=categoricals, n_splits=10,
cv_method="StratifiedKFold", group=None, task="classification", scaler=None, verbose=True )
randomForest_predict = Random_forest.predict(X_test)
 randomForest_score = Random_forest.score(X_test,y_test)
 print("Random Forest Score :",randomForest_score )<compute_test_metric>
catb = CatbModel(data, test, target, features, categoricals=categoricals, n_splits=10,
cv_method="StratifiedKFold", group=None, task="classification", scaler=None, verbose=True )
print("Accuracy Score of Random Forest Classifier Model:")
 print(metrics.accuracy_score(y_test,randomForest_predict))
print("
","Classification Report:")
 print(metrics.classification_report(y_test,randomForest_predict),'
' )<train_model>
submission_df = pd.read_csv('.. /input/google-cloud-ncaa-march-madness-2020-division-1-mens-tournament/MSampleSubmissionStage1_2020.csv')
 submission_df['Pred'] = test_preds
submission_df.head()
<predict_on_test><EOS> 
submission_df.to_csv('submission.csv', index=False )