Kaggle competition: Riiid Answer Correctness Prediction
<SOS> metric: AUC Kaggle data source: riiid-answer-correctness-prediction<create_dataframe>
!pip install.. /input/python-datatable/datatable-0.11.0-cp37-cp37m-manylinux2010_x86_64.whl > /dev/null 2>&1
pd.DataFrame({'Train':train_df.isnull().sum() , 'Test':test_df.isnull().sum() } ).transpose()<train_model>
_ = np.seterr(divide='ignore', invalid='ignore' )
print('Missing Values in Age column: ',177/len(train_df['Age'])*100)
 print('Missing Values in Cabin column: ',687/len(train_df['Cabin'])*100)
 print('Missing Values in Embarked column: ',2/len(train_df['Embarked'])*100 )<feature_engineering>
data_types_dict = {
'timestamp': 'int64',
'user_id': 'int32',
'content_id': 'int16',
'content_type_id':'int8',
'task_container_id': 'int16',
'answered_correctly': 'int8',
'prior_question_elapsed_time': 'float32',
'prior_question_had_explanation': 'bool'
}
target = 'answered_correctly'
train_df["Age"] = train_df["Age"].fillna(-0.5)
 test_df["Age"] = test_df["Age"].fillna(-0.5)
 bins = [-1, 0, 5, 12, 18, 24, 35, 60, np.inf]
labels = ['Unknown', 'Baby', 'Child', 'Teenager', 'Student', 'Young Adult', 'Adult', 'Senior']
train_df['AgeGroup'] = pd.cut(train_df["Age"], bins, labels = labels)
 test_df['AgeGroup'] = pd.cut(test_df["Age"], bins, labels = labels)
<categorify>
train_df = dt.fread('.. /input/riiid-test-answer-prediction/train.csv', columns=set(data_types_dict.keys())).to_pandas()
mr_age = train_df[train_df["Title"] == 1]["AgeGroup"].mode()
miss_age = train_df[train_df["Title"] == 2]["AgeGroup"].mode()
mrs_age = train_df[train_df["Title"] == 3]["AgeGroup"].mode()
master_age = train_df[train_df["Title"] == 4]["AgeGroup"].mode()
rare_age = train_df[train_df["Title"] == 5]["AgeGroup"].mode()
age_title_mapping = {1: "Young Adult", 2: "Student", 3: "Adult", 4: "Baby", 5: "Adult"}
for x in range(len(train_df["AgeGroup"])) :
if train_df["AgeGroup"][x] == "Unknown":
train_df["AgeGroup"][x] = age_title_mapping[train_df["Title"][x]]
for x in range(len(test_df["AgeGroup"])) :
if test_df["AgeGroup"][x] == "Unknown":
test_df["AgeGroup"][x] = age_title_mapping[test_df["Title"][x]]<data_type_conversions>
lectures_df = pd.read_csv('/kaggle/input/riiid-test-answer-prediction/lectures.csv' )
age_mapping = {'Baby': 1, 'Child': 2, 'Teenager': 3, 'Student': 4, 'Young Adult': 5, 'Adult': 6, 'Senior': 7}
train_df['AgeGroup'] = train_df['AgeGroup'].map(age_mapping ).astype(int)
 test_df['AgeGroup'] = test_df['AgeGroup'].map(age_mapping ).astype(int )<categorify>
lectures_df['type_of'] = lectures_df['type_of'].replace('solving question', 'solving_question')
 lectures_df = pd.get_dummies(lectures_df, columns=['part', 'type_of'])
 part_lectures_columns = [column for column in lectures_df.columns if column.startswith('part')]
types_of_lectures_columns = [column for column in lectures_df.columns if column.startswith('type_of_')]
train_df['Embarked'].fillna('S', inplace = True)
 label = {'S':1, 'C':2, 'Q':3}
train_df.replace({'Embarked':label}, inplace = True)
 test_df.replace({'Embarked':label}, inplace = True )<data_type_conversions>
train_lectures = train_df[train_df.content_type_id == True].merge(lectures_df, left_on='content_id', right_on='lecture_id', how='left' )
train_df['Cabin'] = train_df['Cabin'].fillna('X')
 test_df['Cabin']=test_df['Cabin'].fillna('X' )<feature_engineering>
user_lecture_stats_part = train_lectures.groupby('user_id',as_index = False)[part_lectures_columns + types_of_lectures_columns].sum()
for data in df:
data['Cabin'] = data['Cabin'].map(lambda x: re.compile("([a-zA-Z]+)" ).search(x ).group())
 category = {'A':1, 'B':2, 'C':3, 'D':4, 'E':5, 'F':6, 'G':7, 'X':8, 'T':9}
for data in df:
data['Cabin'] = data['Cabin'].map(category )<data_type_conversions>
lecturedata_types_dict = {
'user_id': 'int32',
'part_1': 'int8',
'part_2': 'int8',
'part_3': 'int8',
'part_4': 'int8',
'part_5': 'int8',
'part_6': 'int8',
'part_7': 'int8',
'type_of_concept': 'int8',
'type_of_intention': 'int8',
'type_of_solving_question': 'int8',
'type_of_starter': 'int8'
}
user_lecture_stats_part = user_lecture_stats_part.astype(lecturedata_types_dict )
train_df['Fare'] = pd.to_numeric(train_df['Fare'] )<categorify>
for column in user_lecture_stats_part.columns:
if(column !='user_id'):
user_lecture_stats_part[column] =(user_lecture_stats_part[column] > 0 ).astype('int8' )
label = {'male':1, 'female':0}
train_df.replace({'Sex':label}, inplace = True)
 test_df.replace({'Sex':label}, inplace = True )<sort_values>
train_lectures[train_lectures.user_id==5382]
for data in df:
data['FamilySize'] = data['SibSp'] + data['Parch'] + 1
train_df[['FamilySize', 'Survived']].groupby(['FamilySize'], as_index=False ).mean().sort_values(by='Survived', ascending=False )<feature_engineering>
user_lecture_stats_part[user_lecture_stats_part.user_id==5382]
for data in df:
data['IsAlone'] = 0
data.loc[data['FamilySize'] == 1, 'IsAlone'] = 1
train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False ).mean()<drop_column>
del(train_lectures )
train_df.drop(['PassengerId', 'Name', 'Ticket', 'Age', 'SibSp', 'Parch', 'FamilySize'], axis = 1, inplace = True)
 test_df.drop(['Name', 'Ticket', 'Age', 'SibSp', 'Parch', 'FamilySize'], axis = 1, inplace = True )<prepare_x_and_y>
cum = train_df.groupby('user_id')['content_type_id'].agg(['cumsum', 'cumcount'])
 train_df['user_lecture_cumsum'] = cum['cumsum']
train_df['user_lecture_lv'] = cum['cumsum'] / cum['cumcount']
train_df.user_lecture_lv=train_df.user_lecture_lv.astype('float16')
 train_df.user_lecture_cumsum=train_df.user_lecture_cumsum.astype('int8')
 user_lecture_agg = train_df.groupby('user_id')['content_type_id'].agg(['sum', 'count'] )
X = train_df.drop('Survived', axis = 1)
 y = train_df['Survived']<split>
train_df['prior_question_had_explanation'].fillna(False, inplace=True)
 train_df = train_df.astype(data_types_dict)
 train_df = train_df[train_df[target] != -1].reset_index(drop=True)
 content_explation_agg=train_df[["content_id","prior_question_had_explanation",target]].groupby(["content_id","prior_question_had_explanation"])[target].agg(['mean'])
 content_explation_agg=content_explation_agg.unstack()
content_explation_agg=content_explation_agg.reset_index()
content_explation_agg.columns = ['content_id', 'content_explation_false_mean','content_explation_true_mean']
content_explation_agg.content_id=content_explation_agg.content_id.astype('int16')
 content_explation_agg.content_explation_false_mean=content_explation_agg.content_explation_false_mean.astype('float16')
 content_explation_agg.content_explation_true_mean=content_explation_agg.content_explation_true_mean.astype('float16')
 prior_question_elapsed_time_mean=train_df['prior_question_elapsed_time'].mean()
train_df['prior_question_elapsed_time'].fillna(prior_question_elapsed_time_mean, inplace=True )
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 123 )<import_modules>
max_timestamp_u = train_df[['user_id','timestamp']].groupby(['user_id'] ).agg(['max'] ).reset_index()
max_timestamp_u.columns = ['user_id', 'max_time_stamp']
from sklearn.metrics import accuracy_score<train_model>
train_df['lagtime'] = train_df.groupby('user_id')['timestamp'].shift()
train_df['lagtime']=train_df['timestamp']-train_df['lagtime']
lagtime_mean=train_df['lagtime'].mean()
train_df['lagtime'].fillna(lagtime_mean, inplace=True)
 train_df.lagtime=train_df.lagtime.astype('int32')
lr = LogisticRegression()
lr.fit(X_train, y_train)
 y_pred = lr.predict(X_test)
 print('Classification Report:
', classification_report(y_pred, y_test))
lr_train_acc = round(lr.score(X_train, y_train)* 100, 2)
 print('Training Accuracy: ', lr_train_acc)
 lr_test_acc = round(lr.score(X_test, y_test)* 100, 2)
 print('Testing Accuracy: ', lr_test_acc )<train_model>
lagtime_agg = train_df.groupby('user_id')['lagtime'].agg(['mean'])
 train_df['lagtime_mean'] = train_df['user_id'].map(lagtime_agg['mean'])
 train_df.lagtime_mean=train_df.lagtime_mean.astype('int32' )
knn = KNeighborsClassifier(n_neighbors = 3)
 knn.fit(X_train, y_train)
 y_pred = knn.predict(X_test)
 print('Classification Report:
', classification_report(y_pred, y_test))
knn_train_acc = round(knn.score(X_train, y_train)* 100, 2)
 print('Training Accuracy: ', knn_train_acc)
 knn_test_acc = round(knn.score(X_test, y_test)* 100, 2)
 print('Testing Accuracy: ', knn_test_acc )<train_on_grid>
train_df[['user_id','prior_question_elapsed_time']].groupby(['user_id'] ).head()
svc = SVC()
param_grid = {'C': [0.01, 0.1, 1 ,10 , 100], 'kernel':['linear', 'rbf'], 'gamma':[0.1, 1, 10, 100]}
gcv = GridSearchCV(estimator = svc, param_grid = param_grid, cv = 5, n_jobs=-1, refit=True)
 gcv.fit(X_train, y_train)
 gcv.best_params_<compute_train_metric>
user_prior_question_elapsed_time = train_df[['user_id','prior_question_elapsed_time']].groupby(['user_id'] ).tail(1)
 user_prior_question_elapsed_time.columns = ['user_id', 'prior_question_elapsed_time']
svc = SVC(C = 10, gamma = 0.1, kernel = 'rbf')
 svc.fit(X_train,y_train)
 y_pred = svc.predict(X_test)
 print('Classification Report:
', classification_report(y_pred, y_test))
svc_train_acc = round(svc.score(X_train, y_train)* 100, 2)
 print('Training Accuracy: ', svc_train_acc)
 svc_test_acc = round(svc.score(X_test, y_test)* 100, 2)
 print('Testing Accuracy: ', svc_test_acc )<train_model>
train_df['delta_prior_question_elapsed_time'] = train_df.groupby('user_id')['prior_question_elapsed_time'].shift()
train_df['delta_prior_question_elapsed_time']=train_df['prior_question_elapsed_time']-train_df['delta_prior_question_elapsed_time']
train_df['delta_prior_question_elapsed_time'].fillna(0, inplace=True)
dt = DecisionTreeClassifier(max_depth = 6, min_samples_leaf = 2)
 dt.fit(X_train, y_train)
 y_pred = dt.predict(X_test)
 print('Classification Report:
', classification_report(y_pred, y_test))
dt_train_acc = round(dt.score(X_train, y_train)* 100, 2)
 print('Training Accuracy: ', dt_train_acc)
 dt_test_acc = round(dt.score(X_test, y_test)* 100, 2)
 print('Testing Accuracy: ', dt_test_acc )<train_on_grid>
train_df.delta_prior_question_elapsed_time=train_df.delta_prior_question_elapsed_time.astype('int32' )
rf = RandomForestClassifier()
param_grid = {'max_depth': [2, 4, 5, 6, 7, 8], 'criterion':['gini', 'entropy'], 'min_samples_leaf':[1, 2 ,4 ,6], 'max_features':['auto', 'log2'], 'n_estimators':[100,150,200]}
gcv = GridSearchCV(estimator=rf, param_grid=param_grid, cv = 5, n_jobs = -1)
 gcv.fit(X_train, y_train)
 gcv.best_params_<train_model>
train_df['timestamp']=train_df['timestamp']/(1000*3600)
 train_df.timestamp=train_df.timestamp.astype('int16')
rf = RandomForestClassifier(max_depth = 8, min_samples_leaf = 6, n_estimators = 150)
 rf.fit(X_train, y_train)
 y_pred = rf.predict(X_test)
 print('Classification Report:
', classification_report(y_pred, y_test))
rf_train_acc = round(rf.score(X_train, y_train)* 100, 2)
 print('Training Accuracy: ', rf_train_acc)
 rf_test_acc = round(rf.score(X_test, y_test)* 100, 2)
 print('Testing Accuracy: ', rf_test_acc )<compute_test_metric>
train_df['lag'] = train_df.groupby('user_id')[target].shift()
adb = AdaBoostClassifier(rf, n_estimators = 200)
 adb.fit(X_train, y_train)
 y_pred = adb.predict(X_test)
 print('Classification Report:
', classification_report(y_pred, y_test))
adb_train_acc = round(adb.score(X_train, y_train)* 100, 2)
 print('Training Accuracy: ', adb_train_acc)
 adb_test_acc = round(adb.score(X_test, y_test)* 100, 2)
 print('Testing Accuracy: ', adb_test_acc )<train_on_grid>
cum = train_df.groupby('user_id')['lag'].agg(['cumsum', 'cumcount'])
 train_df['user_correctness'] = cum['cumsum'] / cum['cumcount']
train_df['user_correct_cumsum'] = cum['cumsum']
train_df['user_correct_cumcount'] = cum['cumcount']
train_df.drop(columns=['lag'], inplace=True)
 train_df['user_correct_cumsum'].fillna(0, inplace=True)
 train_df.user_correctness=train_df.user_correctness.astype('float16')
 train_df.user_correct_cumcount=train_df.user_correct_cumcount.astype('int16')
 train_df.user_correct_cumsum=train_df.user_correct_cumsum.astype('int16' )
gdb = GradientBoostingClassifier()
params = {'learning_rate':[0.01,0.1,1,10],'n_estimators':[100,150,200,300],'subsample':[0.6,0.8,1.0],'max_depth':[2,3,4,6],'min_samples_leaf':[1,2,4,6]}
gcv = GridSearchCV(estimator=gdb, param_grid=params, cv=5, n_jobs=-1)
 gcv.fit(X_train, y_train)
 gcv.best_params_<train_model>
train_df.prior_question_had_explanation=train_df.prior_question_had_explanation.astype('int8')
 train_df['lag'] = train_df.groupby('user_id')['prior_question_had_explanation'].shift()
gdb = GradientBoostingClassifier(max_depth = 2, n_estimators = 300, subsample = 0.8)
 gdb.fit(X_train, y_train)
 y_pred = gdb.predict(X_test)
 print('Classification Report:
', classification_report(y_pred, y_test))
gdb_train_acc = round(gdb.score(X_train, y_train)* 100, 2)
 print('Training Accuracy: ', gdb_train_acc)
 gdb_test_acc = round(gdb.score(X_test, y_test)* 100, 2)
 print('Testing Accuracy: ', gdb_test_acc )<train_model>
cum = train_df.groupby('user_id')['lag'].agg(['cumsum', 'cumcount'])
 train_df['explanation_mean'] = cum['cumsum'] / cum['cumcount']
train_df['explanation_cumsum'] = cum['cumsum']
train_df.drop(columns=['lag'], inplace=True)
 train_df['explanation_mean'].fillna(0, inplace=True)
 train_df['explanation_cumsum'].fillna(0, inplace=True)
 train_df.explanation_mean=train_df.explanation_mean.astype('float16')
 train_df.explanation_cumsum=train_df.explanation_cumsum.astype('int16' )
xgbc = XGBClassifier(max_depth = 4)
 xgbc.fit(X_train, y_train)
 y_pred = xgbc.predict(X_test)
 print('Classification Report:
', classification_report(y_pred, y_test))
xgbc_train_acc = round(xgbc.score(X_train, y_train)* 100, 2)
 print('Training Accuracy: ', xgbc_train_acc)
 xgbc_test_acc = round(xgbc.score(X_test, y_test)* 100, 2)
 print('Testing Accuracy: ', xgbc_test_acc )<data_type_conversions>
del cum
gc.collect()
test_df['Fare'] = pd.to_numeric(test_df['Fare'] )<save_to_csv>
train_df["attempt_no"] = 1
train_df.attempt_no=train_df.attempt_no.astype('int8')
 train_df["attempt_no"] = train_df[["user_id","content_id",'attempt_no']].groupby(["user_id","content_id"])["attempt_no"].cumsum()
test_df['Survived'] = rf.predict(test_df.drop(['PassengerId'], axis = 1))
test_df[['PassengerId', 'Survived']].to_csv('MySubmission.csv', index = False )<load_from_csv>
explanation_agg = train_df.groupby('user_id')['prior_question_had_explanation'].agg(['sum', 'count'])
 explanation_agg=explanation_agg.astype('int16')
train_data = pd.read_csv("/kaggle/input/titanic/train.csv")
 train_data.head()<load_from_csv>
user_agg = train_df.groupby('user_id')[target].agg(['sum', 'count'])
 content_agg = train_df.groupby('content_id')[target].agg(['sum', 'count','var'])
 task_container_agg = train_df.groupby('task_container_id')[target].agg(['sum', 'count','var'])
test_data = pd.read_csv("/kaggle/input/titanic/test.csv")
 test_data.head()<install_modules>
user_agg=user_agg.astype('int16')
 content_agg=content_agg.astype('float32')
 task_container_agg=task_container_agg.astype('float32' )
pip install lofo-importance<set_options>
attempt_no_agg=train_df.groupby(["user_id","content_id"])["attempt_no"].agg(['sum'])
 attempt_no_agg=attempt_no_agg.astype('int8')
%matplotlib inline<train_on_grid>
train_df['content_count'] = train_df['content_id'].map(content_agg['count'] ).astype('int32')
 train_df['content_sum'] = train_df['content_id'].map(content_agg['sum'] ).astype('int32')
 train_df['content_correctness'] = train_df['content_id'].map(content_agg['sum'] / content_agg['count'])
 train_df.content_correctness=train_df.content_correctness.astype('float16')
 train_df['task_container_sum'] = train_df['task_container_id'].map(task_container_agg['sum'] ).astype('int32')
 train_df['task_container_std'] = train_df['task_container_id'].map(task_container_agg['var'] ).astype('float16')
 train_df['task_container_correctness'] = train_df['task_container_id'].map(task_container_agg['sum'] / task_container_agg['count'])
 train_df.task_container_correctness=train_df.task_container_correctness.astype('float16' )
lr = lgb.LGBMClassifier()
lr.fit(X_test[["Pclass", "Sex", "SibSp", "Parch" ,"Age", "Fare" , "Embarked"]], X_test["Survived"])
 fi = FLOFOImportance(lr, X_test, ["Pclass", "Sex", "SibSp", "Parch" ,"Age", "Fare" , "Embarked"], 'Survived', scoring="neg_mean_absolute_error")
 importances = fi.get_importance()
importances<categorify>
questions_df = pd.read_csv(
 '.. /input/riiid-test-answer-prediction/questions.csv',
usecols=[0, 1,3,4],
dtype={'question_id': 'int16','bundle_id': 'int16', 'part': 'int8','tags': 'str'} 
)
 questions_df['part_bundle_id']=questions_df['part']*100000+questions_df['bundle_id']
questions_df.part_bundle_id=questions_df.part_bundle_id.astype('int32')
 tag = questions_df["tags"].str.split(" ", n = 10, expand = True)
 tag.columns = ['tags1','tags2','tags3','tags4','tags5','tags6']
tag.fillna(0, inplace=True)
 tag = tag.astype('int16')
 questions_df = pd.concat([questions_df,tag],axis=1 ).drop(['tags'],axis=1 )
y = train_data["Survived"]
features = ["Pclass", "Sex", "SibSp", "Parch" ,"Age", "Fare" , "Embarked"]
X = pd.get_dummies(train_data[features])
 X_test = pd.get_dummies(test_data[features])
 model = lgb.LGBMClassifier().fit(X, y )<define_search_space>
questions_df.rename(columns={'question_id':'content_id'}, inplace=True )
params = {"num_leaves": [16,12,8,4],
"learning_rate": [0.4, 0.1, 0.8, 0.05],
"max_depth":[10,8,6,4],
"feature_fraction": [0.1, 0.3, 0.5, 0.8],
"subsample": [0.2, 0.5, 0.75]}<train_on_grid>
questions_df['content_correctness'] = questions_df['content_id'].map(content_agg['sum'] / content_agg['count'])
 questions_df.content_correctness=questions_df.content_correctness.astype('float16')
 questions_df['content_correctness_std'] = questions_df['content_id'].map(content_agg['var'])
 questions_df.content_correctness_std=questions_df.content_correctness_std.astype('float16' )
cv_model = GridSearchCV(model , params, cv=10, verbose=2 , n_jobs=-1 ).fit(X , y )<find_best_params>
part_agg = questions_df.groupby('part')['content_correctness'].agg(['mean', 'var'])
 questions_df['part_correctness_mean'] = questions_df['part'].map(part_agg['mean'])
 questions_df['part_correctness_std'] = questions_df['part'].map(part_agg['var'])
 questions_df.part_correctness_mean=questions_df.part_correctness_mean.astype('float16')
 questions_df.part_correctness_std=questions_df.part_correctness_std.astype('float16' )
cv_model.best_params_<train_model>
bundle_agg = questions_df.groupby('bundle_id')['content_correctness'].agg(['mean'])
 questions_df['bundle_correctness'] = questions_df['bundle_id'].map(bundle_agg['mean'])
 questions_df.bundle_correctness=questions_df.bundle_correctness.astype('float16' )
model_tuned = lgb.LGBMClassifier(max_depth= 10, feature_fraction=0.1, learning_rate=0.1,
num_leaves= 16,
subsample= 0.2 ).fit(X , y )<save_to_csv>
tags1_agg = questions_df.groupby('tags1')['content_correctness'].agg(['mean', 'var'])
 questions_df['tags1_correctness_mean'] = questions_df['tags1'].map(tags1_agg['mean'])
 questions_df['tags1_correctness_std'] = questions_df['tags1'].map(tags1_agg['var'])
 questions_df.tags1_correctness_mean=questions_df.tags1_correctness_mean.astype('float16')
 questions_df.tags1_correctness_std=questions_df.tags1_correctness_std.astype('float16' )
output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
 output.to_csv('my_submission.csv', index=False)
 print("Your submission was successfully saved!" )<load_from_csv>
questions_df.drop(columns=['content_correctness'], inplace=True )
train = pd.read_csv(".. /input/titanic/train.csv")
 test = pd.read_csv(".. /input/titanic/test.csv" )<train_model>
questions_df = pd.merge(questions_df, content_explation_agg, on='content_id', how='left',right_index=True )
data_size_train = train.memory_usage().sum() / 1024 / 1024
print("Data memory size: %.2f MB" % data_size_train )<train_model>
del bundle_agg
del part_agg
del tags1_agg
gc.collect()
data_size_test = test.memory_usage().sum() / 1024 / 1024
print("Data memory size: %.2f MB" % data_size_test )<feature_engineering>
train_df['user_correctness'].fillna(1, inplace=True)
 train_df['attempt_no'].fillna(1, inplace=True)
 train_df.fillna(0, inplace=True )
train["NameTitle"] = train["Name"].str.split(", ", expand = True)[1].str.split(".", expand = True)[0]
test["NameTitle"] = test["Name"].str.split(", ", expand = True)[1].str.split(".", expand = True)[0]
train = train.drop("Name", axis = 1)
 test = test.drop("Name", axis = 1)
 print(train["NameTitle"].unique())
 print("------------------------" * 3)
 print(test["NameTitle"].unique() )<count_values>
MAX_SEQ = 160
skills = train_df["content_id"].unique()
n_skill = len(skills)
 print("number skills", len(skills))
group = train_df[['user_id', 'content_id', 'answered_correctly']].groupby('user_id' ).apply(lambda r:(
 r['content_id'].values,
r['answered_correctly'].values))
for user_id in group.index:
q, qa = group[user_id]
if len(q)>MAX_SEQ:
group[user_id] =(q[-MAX_SEQ:],qa[-MAX_SEQ:])
 pickle.dump(group, open("group.pkl", "wb"))
del group
gc.collect()
min_titles =(train["NameTitle"].value_counts() < 10)
 train["NameTitle"] = train["NameTitle"].apply(lambda x: "Misc" if min_titles.loc[x] == True else x)
 print(train["NameTitle"].unique() )<count_values>
features = [
'lagtime',
'lagtime_mean',
'user_lecture_cumsum',
'user_lecture_lv',
'prior_question_elapsed_time',
'delta_prior_question_elapsed_time',
'user_correctness',
'user_correct_cumcount',
'user_correct_cumsum',
'content_correctness',
'content_explation_false_mean',
'content_explation_true_mean',
'content_count',
'content_sum',
'task_container_correctness',
'bundle_correctness',
'attempt_no',
'part',
'part_correctness_mean',
'tags1',
'tags1_correctness_mean',
'bundle_id',
'explanation_mean',
'explanation_cumsum',
'prior_question_had_explanation',
]
categorical_columns= [
'part',
'tags1',
'bundle_id',
'prior_question_had_explanation',
]
min_titles =(test["NameTitle"].value_counts() < 10)
 test["NameTitle"] = test["NameTitle"].apply(lambda x: "Misc" if min_titles.loc[x] == True else x)
 print(test["NameTitle"].unique() )<feature_engineering>
flag_lgbm=True
clfs = list()
params = {
'num_leaves': 350,
'max_bin':700,
'min_child_weight': 0.03454472573214212,
'feature_fraction': 0.58,
'bagging_fraction': 0.58,
'objective': 'binary',
'max_depth': -1,
'learning_rate': 0.05,
"boosting_type": "gbdt",
"bagging_seed": 11,
"metric": 'auc',
"verbosity": -1,
'reg_alpha': 0.3899927210061127,
'reg_lambda': 0.6485237330340494,
'random_state': 47
}
trains=list()
valids=list()
num=1
for i in range(0,num):
train_df_clf=train_df.sample(n=20000*1000)
 print('sample end')
 del train_df
users=train_df_clf['user_id'].drop_duplicates()
users=users.sample(frac=0.025)
 users_df=pd.DataFrame()
users_df['user_id']=users.values
valid_df_newuser = pd.merge(train_df_clf, users_df, on=['user_id'], how='inner',right_index=True)
 del users_df
del users
gc.collect()
train_df_clf.drop(valid_df_newuser.index, inplace=True)
 train_df_clf = pd.merge(train_df_clf, questions_df, on='content_id', how='left',right_index=True)
 valid_df_newuser = pd.merge(valid_df_newuser, questions_df, on='content_id', how='left',right_index=True)
 valid_df=train_df_clf.sample(frac=0.09)
 train_df_clf.drop(valid_df.index, inplace=True)
 valid_df = valid_df.append(valid_df_newuser)
 del valid_df_newuser
gc.collect()
trains.append(train_df_clf)
 valids.append(valid_df)
 print('valid_df length：',len(valid_df))
train["FamilySize"] = train["SibSp"] + train["Parch"]
test["FamilySize"] = test["SibSp"] + test["Parch"]<feature_engineering>
del train_df_clf
del valid_df
gc.collect()
train["AgeGroup"] = ""
train.loc[train["Age"] < 21, "AgeGroup"] = "under 21"
train.loc[train["Age"] >= 21, "AgeGroup"] = "21-65"
train.loc[train["Age"] > 65, "AgeGroup"] = "65+"
test["AgeGroup"] = ""
test.loc[test["Age"] < 21, "AgeGroup"] = "under 21"
test.loc[test["Age"] >= 21, "AgeGroup"] = "21-65"
test.loc[test["Age"] > 65, "AgeGroup"] = "65+"
train = train.drop("Age", axis = 1)
 test = test.drop("Age", axis = 1 )<feature_engineering>
for i in range(0,num):
tr_data = lgb.Dataset(trains[i][features], label=trains[i][target])
 va_data = lgb.Dataset(valids[i][features], label=valids[i][target])
 del trains
del valids
gc.collect()
model = lgb.train(
 params,
tr_data,
num_boost_round=5000,
valid_sets=[tr_data, va_data],
early_stopping_rounds=50,
feature_name=features,
categorical_feature=categorical_columns,
verbose_eval=50 
)
 clfs.append(model)
 lgb.plot_importance(model, importance_type='gain')
 plt.show()
del tr_data
del va_data
gc.collect()
train["FareGroup"] = ""
train.loc[train["Fare"] < 170, "FareGroup"] = "0-170"
train.loc[train["Fare"] >= 170, "FareGroup"] = "170-340"
train.loc[train["Fare"] > 340, "FareGroup"] = "340+"
test["FareGroup"] = ""
test.loc[test["Fare"] < 170, "FareGroup"] = "0-170"
test.loc[test["Fare"] >= 170, "FareGroup"] = "170-340"
test.loc[test["Fare"] > 340, "FareGroup"] = "340+"
train = train.drop("Fare", axis = 1)
 test = test.drop("Fare", axis = 1 )<drop_column>
user_sum_dict = user_agg['sum'].astype('int16' ).to_dict(defaultdict(int))
user_count_dict = user_agg['count'].astype('int16' ).to_dict(defaultdict(int))
content_sum_dict = content_agg['sum'].astype('int32' ).to_dict(defaultdict(int))
content_count_dict = content_agg['count'].astype('int32' ).to_dict(defaultdict(int))
del user_agg
del content_agg
gc.collect()
task_container_sum_dict = task_container_agg['sum'].astype('int32' ).to_dict(defaultdict(int))
task_container_count_dict = task_container_agg['count'].astype('int32' ).to_dict(defaultdict(int))
task_container_std_dict = task_container_agg['var'].astype('float16' ).to_dict(defaultdict(int))
explanation_sum_dict = explanation_agg['sum'].astype('int16' ).to_dict(defaultdict(int))
explanation_count_dict = explanation_agg['count'].astype('int16' ).to_dict(defaultdict(int))
del task_container_agg
del explanation_agg
gc.collect()
train = train.drop(["SibSp", "Parch"], axis = 1)
 test = test.drop(["SibSp", "Parch"], axis = 1 )<categorify>
user_lecture_sum_dict = user_lecture_agg['sum'].astype('int16' ).to_dict(defaultdict(int))
user_lecture_count_dict = user_lecture_agg['count'].astype('int16' ).to_dict(defaultdict(int))
lagtime_mean_dict = lagtime_agg['mean'].astype('int32' ).to_dict(defaultdict(int))
del user_lecture_agg
del lagtime_agg
gc.collect()
train = pd.get_dummies(train, columns = ["Sex", "Embarked", "NameTitle", "AgeGroup", "FareGroup"])
 test = pd.get_dummies(test, columns = ["Sex", "Embarked", "NameTitle", "AgeGroup", "FareGroup"] )<data_type_conversions>
attempt_no_agg=attempt_no_agg[attempt_no_agg['sum'] >1]
attempt_no_sum_dict = attempt_no_agg['sum'].to_dict(defaultdict(int))
del attempt_no_agg
gc.collect()
upper = corr.where(np.triu(np.ones(corr.shape), k = 1 ).astype(np.bool))
to_drop = [column for column in upper.columns if any(upper[column] > 0.90)]
print(to_drop )<prepare_x_and_y>
max_timestamp_u_dict=max_timestamp_u.set_index('user_id' ).to_dict()
user_prior_question_elapsed_time_dict=user_prior_question_elapsed_time.set_index('user_id' ).to_dict()
del max_timestamp_u
del user_prior_question_elapsed_time
gc.collect()
X = train.drop(["PassengerId", "Survived"], axis = 1)
 Y = train["Survived"]<import_modules>
def get_max_attempt(user_id,content_id):
k =(user_id,content_id)
 if k in attempt_no_sum_dict.keys() :
attempt_no_sum_dict[k]+=1
return attempt_no_sum_dict[k]
attempt_no_sum_dict[k] = 1
return attempt_no_sum_dict[k]
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, BaggingClassifier, ExtraTreesClassifier, StackingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from catboost import CatBoostClassifier
from xgboost import XGBClassifier
import lightgbm as lgb<set_options>
class FFN(nn.Module):
def __init__(self, state_size=200):
super(FFN, self ).__init__()
self.state_size = state_size
self.lr1 = nn.Linear(state_size, state_size)
 self.relu = nn.ReLU()
self.lr2 = nn.Linear(state_size, state_size)
 self.dropout = nn.Dropout(0.2)
 def forward(self, x):
x = self.lr1(x)
 x = self.relu(x)
 x = self.lr2(x)
 return self.dropout(x)
 def future_mask(seq_length):
future_mask = np.triu(np.ones(( seq_length, seq_length)) , k=1 ).astype('bool')
 return torch.from_numpy(future_mask)
 class SAKTModel(nn.Module):
def __init__(self, n_skill, max_seq=MAX_SEQ, embed_dim=128):
super(SAKTModel, self ).__init__()
self.n_skill = n_skill
self.embed_dim = embed_dim
self.embedding = nn.Embedding(2*n_skill+1, embed_dim)
 self.pos_embedding = nn.Embedding(max_seq-1, embed_dim)
 self.e_embedding = nn.Embedding(n_skill+1, embed_dim)
 self.multi_att = nn.MultiheadAttention(embed_dim=embed_dim, num_heads=8, dropout=0.2)
 self.dropout = nn.Dropout(0.2)
 self.layer_normal = nn.LayerNorm(embed_dim)
 self.ffn = FFN(embed_dim)
 self.pred = nn.Linear(embed_dim, 1)
 def forward(self, x, question_ids):
device = x.device
x = self.embedding(x)
 pos_id = torch.arange(x.size(1)).unsqueeze(0 ).to(device)
 pos_x = self.pos_embedding(pos_id)
 x = x + pos_x
e = self.e_embedding(question_ids)
 x = x.permute(1, 0, 2)
 e = e.permute(1, 0, 2)
 att_mask = future_mask(x.size(0)).to(device)
 att_output, att_weight = self.multi_att(e, x, x, attn_mask=att_mask)
 att_output = self.layer_normal(att_output + e)
 att_output = att_output.permute(1, 0, 2)
 x = self.ffn(att_output)
 x = self.layer_normal(x + att_output)
 x = self.pred(x)
 return x.squeeze(-1), att_weight
class TestDataset(Dataset):
def __init__(self, samples, test_df, skills, max_seq=MAX_SEQ):
super(TestDataset, self ).__init__()
self.samples = samples
self.user_ids = [x for x in test_df["user_id"].unique() ]
self.test_df = test_df
self.skills = skills
self.n_skill = len(skills)
 self.max_seq = max_seq
def __len__(self):
return self.test_df.shape[0]
def __getitem__(self, index):
test_info = self.test_df.iloc[index]
user_id = test_info["user_id"]
target_id = test_info["content_id"]
q = np.zeros(self.max_seq, dtype=int)
 qa = np.zeros(self.max_seq, dtype=int)
 if user_id in self.samples.index:
q_, qa_ = self.samples[user_id]
seq_len = len(q_)
 if seq_len >= self.max_seq:
q = q_[-self.max_seq:]
qa = qa_[-self.max_seq:]
else:
q[-seq_len:] = q_
qa[-seq_len:] = qa_
x = np.zeros(self.max_seq-1, dtype=int)
 x = q[1:].copy()
x +=(qa[1:] == 1)* self.n_skill
questions = np.append(q[2:], [target_id])
 return x, questions
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 SAKT_model = SAKTModel(n_skill, embed_dim=128)
 try:
SAKT_model.load_state_dict(torch.load(".. /input/sakt-with-randomization-state-updates/SAKT-HDKIM.pt"))
except:
SAKT_model.load_state_dict(torch.load(".. /input/sakt-with-randomization-state-updates/SAKT-HDKIM.pt", map_location='cpu'))
SAKT_model.to(device)
 SAKT_model.eval()
group = pickle.load(open("group.pkl", "rb"))
print(psutil.virtual_memory().percent)
style.use("seaborn-whitegrid")
 %matplotlib inline
%config InlineBackend.figure_format = "retina"
def model_builder(X, Y, pipeline, params, kfolds, classifier_name, data_split = False):

 if data_split == True:
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
random_state = 0,
test_size = 0.25)
 cv = StratifiedKFold(n_splits = kfolds)
 start = time.time()
grid = GridSearchCV(pipeline, params, cv = cv, verbose = 0, n_jobs = -1)
 grid = grid.fit(X_train, Y_train)
 print("Best Params:")
 print("")
 print(grid.best_params_)
 grid = grid.best_estimator_
classifier_score = cross_val_score(grid, X_test, Y_test, cv = cv, n_jobs = -1)
 Y_predicted = grid.predict(X_test)
 stop = time.time()
print("")
 print("
print("Cross-Validated-Score: " + str(round(classifier_score.mean() , 6)))
 print("
print("")
 print("Training time: " + str(round(stop - start, 2)) + "s")
 train_size, train_score, test_score = learning_curve(grid, X_train, Y_train, cv = cv, n_jobs = -1)
 print("")
 print("Learning Curve:")
 plt.figure(figsize =(12,6))
plt.plot(train_size, np.mean(train_score, axis = 1), label = "Train scores")
 plt.plot(train_size, np.mean(test_score, axis = 1), label = "Test scores")
 plt.title(classifier_name)
 plt.legend()
plt.show()
return X_train, X_test, Y_train, Y_test, classifier_score, grid
if data_split == False:
cv = StratifiedKFold(n_splits = kfolds)
 start = time.time()
grid = GridSearchCV(pipeline, params, cv = cv, verbose = 0, n_jobs = -1)
 grid = grid.fit(X, Y)
 print("Best Params:")
 print("")
 print(grid.best_params_)
 grid = grid.best_estimator_
classifier_score = cross_val_score(grid, X, Y, cv = cv, n_jobs = -1)
 Y_predicted = grid.predict(X)
 stop = time.time()
print("")
 print("
print("Cross-Validated-Score: " + str(round(classifier_score.mean() , 6)))
 print("
print("")
 print("Training time: " + str(round(stop - start, 2)) + "s")
 train_size, train_score, test_score = learning_curve(grid, X, Y, cv = cv, n_jobs = -1)
 print("")
 print("Learning Curve:")
 plt.figure(figsize =(12,6))
plt.plot(train_size, np.mean(train_score, axis = 1), label = "Train scores")
 plt.plot(train_size, np.mean(test_score, axis = 1), label = "Test scores")
 plt.title(classifier_name)
 plt.legend()
plt.show()
return classifier_score, grid<find_best_model_class>
iter_test = env.iter_test()
prior_test_df = None
<find_best_params><EOS> 
%%time
for(test_df, sample_prediction_df)in iter_test:
if(prior_test_df is not None)&(psutil.virtual_memory().percent<90):
print(psutil.virtual_memory().percent)
 prior_test_df[target] = eval(test_df['prior_group_answers_correct'].iloc[0])
 prior_test_df = prior_test_df[prior_test_df[target] != -1].reset_index(drop=True)
 prior_test_df['prior_question_had_explanation'].fillna(False, inplace=True)
 prior_test_df.prior_question_had_explanation=prior_test_df.prior_question_had_explanation.astype('int8')
 prev_group = prior_test_df[['user_id', 'content_id', 'answered_correctly']].groupby('user_id' ).apply(lambda r:(
 r['content_id'].values,
r['answered_correctly'].values))
for prev_user_id in prev_group.index:
prev_group_content = prev_group[prev_user_id][0]
prev_group_ac = prev_group[prev_user_id][1]
if prev_user_id in group.index:
group[prev_user_id] =(np.append(group[prev_user_id][0],prev_group_content),
np.append(group[prev_user_id][1],prev_group_ac))
else:
group[prev_user_id] =(prev_group_content,prev_group_ac)
 if len(group[prev_user_id][0])>MAX_SEQ:
new_group_content = group[prev_user_id][0][-MAX_SEQ:]
new_group_ac = group[prev_user_id][1][-MAX_SEQ:]
group[prev_user_id] =(new_group_content,new_group_ac)
 user_ids = prior_test_df['user_id'].values
content_ids = prior_test_df['content_id'].values
task_container_ids = prior_test_df['task_container_id'].values
prior_question_had_explanations = prior_test_df['prior_question_had_explanation'].values
targets = prior_test_df[target].values
for user_id, content_id,prior_question_had_explanation,task_container_id,answered_correctly in zip(user_ids, content_ids, prior_question_had_explanations,task_container_ids,targets):
user_sum_dict[user_id] += answered_correctly
user_count_dict[user_id] += 1
explanation_sum_dict[user_id] += prior_question_had_explanation
explanation_count_dict[user_id] += 1
prior_test_df = test_df.copy()
lecture_test_df = test_df[test_df['content_type_id'] == 1].reset_index(drop=True)
 for i,(user_id,content_type_id, content_id)in enumerate(zip(lecture_test_df['user_id'].values,lecture_test_df['content_type_id'].values,lecture_test_df['content_id'].values)) :
user_lecture_sum_dict[user_id] += content_type_id
user_lecture_count_dict[user_id] += 1
if(len(user_lecture_stats_part[user_lecture_stats_part.user_id==user_id])==0):
user_lecture_stats_part = user_lecture_stats_part.append([{'user_id':user_id}], ignore_index=True)
 user_lecture_stats_part.fillna(0, inplace=True)
 user_lecture_stats_part.loc[user_lecture_stats_part.user_id==user_id,part_lectures_columns + types_of_lectures_columns]+=lectures_df[lectures_df.lecture_id==content_id][part_lectures_columns + types_of_lectures_columns].values
else:
user_lecture_stats_part.loc[user_lecture_stats_part.user_id==user_id,part_lectures_columns + types_of_lectures_columns]+=lectures_df[lectures_df.lecture_id==content_id][part_lectures_columns + types_of_lectures_columns].values
test_df = test_df[test_df['content_type_id'] == 0].reset_index(drop=True)
 test_dataset = TestDataset(group, test_df, skills)
 test_dataloader = DataLoader(test_dataset, batch_size=51200, shuffle=False)
 SAKT_outs = []
for item in test_dataloader:
x = item[0].to(device ).long()
target_id = item[1].to(device ).long()
with torch.no_grad() :
output, att_weight = SAKT_model(x, target_id)
 output = torch.sigmoid(output)
 output = output[:, -1]
SAKT_outs.extend(output.view(-1 ).data.cpu().numpy())
 test_df['prior_question_had_explanation'].fillna(False, inplace=True)
 test_df.prior_question_had_explanation=test_df.prior_question_had_explanation.astype('int8')
 test_df['prior_question_elapsed_time'].fillna(prior_question_elapsed_time_mean, inplace=True)
 user_lecture_sum = np.zeros(len(test_df), dtype=np.int16)
 user_lecture_count = np.zeros(len(test_df), dtype=np.int16)
 user_sum = np.zeros(len(test_df), dtype=np.int16)
 user_count = np.zeros(len(test_df), dtype=np.int16)
 content_sum = np.zeros(len(test_df), dtype=np.int32)
 content_count = np.zeros(len(test_df), dtype=np.int32)
 task_container_sum = np.zeros(len(test_df), dtype=np.int32)
 task_container_count = np.zeros(len(test_df), dtype=np.int32)
 task_container_std = np.zeros(len(test_df), dtype=np.float16)
 content_task_mean = np.zeros(len(test_df), dtype=np.float16)
 explanation_sum = np.zeros(len(test_df), dtype=np.int32)
 explanation_count = np.zeros(len(test_df), dtype=np.int32)
 delta_prior_question_elapsed_time = np.zeros(len(test_df), dtype=np.int32)
 attempt_no_count = np.zeros(len(test_df), dtype=np.int16)
 lagtime = np.zeros(len(test_df), dtype=np.int32)
 lagtime_mean = np.zeros(len(test_df), dtype=np.int32)
 for i,(user_id,prior_question_had_explanation,content_type_id,prior_question_elapsed_time,timestamp, content_id,task_container_id)in enumerate(zip(test_df['user_id'].values,test_df['prior_question_had_explanation'].values,test_df['content_type_id'].values,test_df['prior_question_elapsed_time'].values,test_df['timestamp'].values, test_df['content_id'].values, test_df['task_container_id'].values)) :
user_lecture_sum_dict[user_id] += content_type_id
user_lecture_count_dict[user_id] += 1
user_lecture_sum[i] = user_lecture_sum_dict[user_id]
user_lecture_count[i] = user_lecture_count_dict[user_id]
user_sum[i] = user_sum_dict[user_id]
user_count[i] = user_count_dict[user_id]
content_sum[i] = content_sum_dict[content_id]
content_count[i] = content_count_dict[content_id]
task_container_sum[i] = task_container_sum_dict[task_container_id]
task_container_count[i] = task_container_count_dict[task_container_id]
task_container_std[i]=task_container_std_dict[task_container_id]
explanation_sum[i] = explanation_sum_dict[user_id]
explanation_count[i] = explanation_count_dict[user_id]
if user_id in max_timestamp_u_dict['max_time_stamp'].keys() :
lagtime[i]=timestamp-max_timestamp_u_dict['max_time_stamp'][user_id]
max_timestamp_u_dict['max_time_stamp'][user_id]=timestamp
lagtime_mean[i]=(lagtime_mean_dict[user_id]+lagtime[i])/2
else:
lagtime[i]=0
max_timestamp_u_dict['max_time_stamp'].update({user_id:timestamp})
 lagtime_mean_dict.update({user_id:timestamp})
 lagtime_mean[i]=(lagtime_mean_dict[user_id]+lagtime[i])/2
if user_id in user_prior_question_elapsed_time_dict['prior_question_elapsed_time'].keys() :
delta_prior_question_elapsed_time[i]=prior_question_elapsed_time-user_prior_question_elapsed_time_dict['prior_question_elapsed_time'][user_id]
user_prior_question_elapsed_time_dict['prior_question_elapsed_time'][user_id]=prior_question_elapsed_time
else:
delta_prior_question_elapsed_time[i]=0
user_prior_question_elapsed_time_dict['prior_question_elapsed_time'].update({user_id:prior_question_elapsed_time})
 test_df=test_df.merge(questions_df.loc[questions_df.index.isin(test_df['content_id'])],
how='left', on='content_id', right_index=True)
 test_df['user_lecture_lv'] = user_lecture_sum / user_lecture_count
test_df['user_lecture_cumsum'] = user_lecture_sum
test_df['user_correctness'] = user_sum / user_count
test_df['user_correct_cumcount'] =user_count
test_df['user_correct_cumsum'] =user_sum
test_df['content_correctness'] = content_sum / content_count
test_df['content_count'] = content_count
test_df['content_sum'] = content_sum
test_df['task_container_correctness'] = task_container_sum / task_container_count
test_df['task_container_sum'] = task_container_sum
test_df['task_container_std'] = task_container_std
test_df['explanation_mean'] = explanation_sum / explanation_count
test_df['explanation_cumsum'] = explanation_sum
test_df['delta_prior_question_elapsed_time'] = delta_prior_question_elapsed_time
test_df["attempt_no"] = test_df[["user_id", "content_id"]].apply(lambda row: get_max_attempt(row["user_id"], row["content_id"]), axis=1)
 test_df["lagtime"]=lagtime
test_df["lagtime_mean"]=lagtime_mean
test_df['user_correctness'].fillna(1, inplace=True)
 test_df['attempt_no'].fillna(1, inplace=True)
 test_df.fillna(0, inplace=True)
 test_df['timestamp']=test_df['timestamp']/(1000*3600)
 test_df.timestamp=test_df.timestamp.astype('int16')
 sub_preds = np.zeros(test_df.shape[0])
 for i, model in enumerate(clfs, 1):
test_preds = model.predict(test_df[features])
 sub_preds += test_preds
lgbm_final = sub_preds / len(clfs)
 test_df[target] = np.array(SAKT_outs)* 0.5 + lgbm_final * 0.5
env.predict(test_df[['row_id', target]] )