kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
10,161,774 | df_train_original = pd.read_csv('.. /input/exam-for-students20200129/train.csv', index_col=0)
df_test_original = pd.read_csv('.. /input/exam-for-students20200129/test.csv',index_col=0)
target = 'ConvertedSalary'
df_country_original = pd.read_csv('.. /input/exam-for-students20200129/country_info.csv')
df_train_origin... | train_df['Cabin'] = train_df['Cabin'].fillna('X')
test_df['Cabin']=test_df['Cabin'].fillna('X' ) | Titanic - Machine Learning from Disaster |
10,161,774 | df_train = df_train_original.copy()
df_test = df_test_original.copy()
df_country = df_country_original.copy()
df_test_with_targetcol = df_test_original.copy()
df_test_with_targetcol[target] = -1
df_all = pd.concat([df_train, df_test_with_targetcol], axis=0 )<merge> | 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 ) | Titanic - Machine Learning from Disaster |
10,161,774 | df_GDP = df_country[['Country', 'GDP($ per capita)']]
df_train = df_train.merge(df_GDP, on = ['Country'], how = 'left')
df_test = df_test.merge(df_GDP, on = ['Country'], how = 'left')
df_all = df_all.merge(df_GDP, on = ['Country'], how = 'left' )<count_values> | train_df['Fare'] = pd.to_numeric(train_df['Fare'] ) | Titanic - Machine Learning from Disaster |
10,161,774 | c = 'Country'
df_train[c].value_counts()<count_values> | label = {'male':1, 'female':0}
train_df.replace({'Sex':label}, inplace = True)
test_df.replace({'Sex':label}, inplace = True ) | Titanic - Machine Learning from Disaster |
10,161,774 | df_test[c].value_counts()
<filter> | 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 ) | Titanic - Machine Learning from Disaster |
10,161,774 | df_train[df_train['Country'] == 'Germany']
df_train[df_train['Country'] == 'France']<count_values> | for data in df:
data['IsAlone'] = 0
data.loc[data['FamilySize'] == 1, 'IsAlone'] = 1
train_df[['IsAlone', 'Survived']].groupby(['IsAlone'], as_index=False ).mean() | Titanic - Machine Learning from Disaster |
10,161,774 | for col in df_all.columns:
print(df_all[col].value_counts())
print('____')
<define_variables> | 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 ) | Titanic - Machine Learning from Disaster |
10,161,774 | first_log = True<concatenate> | X = train_df.drop('Survived', axis = 1)
y = train_df['Survived'] | Titanic - Machine Learning from Disaster |
10,161,774 | y_train = df_train[target]
X_train = df_train.drop([target], axis=1)
X_test = df_test
X_all = pd.concat([X_train, X_test], axis = 0)
X_train = X_train[y_train.isnull() ==False]
y_train = y_train[y_train.isnull() ==False]
if(first_log == True):
y_train = y_train.apply(np.log1p )<create_dataframe> | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 123 ) | Titanic - Machine Learning from Disaster |
10,161,774 | TXT_train_DevType = X_train.DevType.copy()
TXT_test_DevType = X_test.DevType.copy()
TXT_train_CommunicationTools = X_train.CommunicationTools.copy()
TXT_test_CommunicationTools = X_test.CommunicationTools.copy()
TXT_train_FrameworkWorkedWith = X_train.FrameworkWorkedWith.copy()
TXT_test_FrameworkWorkedWith = X_test.Fra... | from sklearn.metrics import accuracy_score | Titanic - Machine Learning from Disaster |
10,161,774 | X_train['missing_sum'] = X_train.isnull().sum(axis=1)
X_test['missing_sum'] = X_test.isnull().sum(axis=1 )<data_type_conversions> | 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('Tes... | Titanic - Machine Learning from Disaster |
10,161,774 | X_train['DevType'].fillna('
X_test['DevType'].fillna('
X_train['CommunicationTools'].fillna('
X_test['CommunicationTools'].fillna('
X_train['FrameworkWorkedWith'].fillna('
X_test['FrameworkWorkedWith'].fillna('<categorify> | 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_... | Titanic - Machine Learning from Disaster |
10,161,774 | def mapping(map_col, mapping):
X_train[map_col]=X_train[map_col].map(mapping)
X_test[map_col]=X_test[map_col].map(mapping )<define_variables> | 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_ | Titanic - Machine Learning from Disaster |
10,161,774 | Age_mapping = {'Under 18 years old':18, '18 - 24 years old':24, '25 - 34 years old':34, '35 - 44 years old':44,
'45 - 54 years old':54, '55 - 64 years old':64, '65 years or older':65}
CompanySize_mapping ={'Fewer than 10 employees':10, '10 to 19 employees':19,
'20 to 99 employees':99, '100 to 499 employees':499, '500 t... | 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, ... | Titanic - Machine Learning from Disaster |
10,161,774 | mapping('Age', Age_mapping)
mapping('CompanySize', CompanySize_mapping)
mapping('Exercise', Exercise_mapping)
mapping('HoursComputer', HoursComputer_mapping)
mapping('HoursOutside', HoursOutside_mapping)
mapping('LastNewJob', LastNewJob_mapping)
mapping('SkipMeals', SkipMeals_mapping)
mapping('StackOverflowJobsR... | 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.sc... | Titanic - Machine Learning from Disaster |
10,161,774 | count_col = ['DevType', 'CommunicationTools', 'FrameworkWorkedWith']
for i in count_col:
X_train[i + '_count'] = X_train[i].apply(lambda x: len(re.split('\s*;\s*', x)))
X_test[i + '_count'] = X_test[i].apply(lambda x: len(re.split('\s*;\s*', x)) )<feature_engineering> | 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_... | Titanic - Machine Learning from Disaster |
10,161,774 | X_train['Github'] = X_train['CommunicationTools'].str.contains('Github')
X_test['Github'] = X_test['CommunicationTools'].str.contains('Github')
X_train['Full-stack'] = X_train['DevType'].str.contains('Full-stack')
X_test['Full-stack'] = X_test['DevType'].str.contains('Full-stack')
X_train['DataScientist'] = X_train... | 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_te... | Titanic - Machine Learning from Disaster |
10,161,774 | del X_train['Country']
del X_test['Country']
del X_train['DevType']
del X_test['DevType']
del X_train['CommunicationTools']
del X_test['CommunicationTools']
del X_train['FrameworkWorkedWith']
del X_test['FrameworkWorkedWith']
del X_train['MilitaryUS']
del X_test['MilitaryUS']
del X_train['SurveyTooLong']
del X_test['Su... | 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_tes... | Titanic - Machine Learning from Disaster |
10,161,774 | X_train['AssessJob_null'] = X_train['AssessJob1'].isnull().astype(int)
X_test['AssessJob_null'] = X_test['AssessJob1'].isnull().astype(int)
X_train['AssessBenefits_null'] = X_train['AssessBenefits1'].isnull().astype(int)
X_test['AssessBenefits_null'] = X_train['AssessBenefits1'].isnull().astype(int)
X_train['JobCon... | 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_ | Titanic - Machine Learning from Disaster |
10,161,774 | col_yj_transform = ['GDP($ per capita)']
for col in col_yj_transform:
pt = PowerTransformer(method='yeo-johnson')
reshape_train = X_train[col].values.reshape(-1,1)
reshape_test = X_test[col].values.reshape(-1,1)
pt.fit(reshape_train)
X_train[col] = pt.transform(reshape_train)
X_test[col] = pt.transform(reshape_tes... | 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)
... | Titanic - Machine Learning from Disaster |
10,161,774 | cats = []
for col in X_train.columns:
if(X_train[col].dtype == 'object'):
cats.append(col)
print(col, X_train[col].nunique() )<categorify> | 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_t... | Titanic - Machine Learning from Disaster |
10,161,774 | ordinal_col = cats
ordinal_encoder = OrdinalEncoder(cols = ordinal_col)
X_train = ordinal_encoder.fit_transform(X_train)
X_test = ordinal_encoder.transform(X_test )<train_model> | test_df['Fare'] = pd.to_numeric(test_df['Fare'] ) | Titanic - Machine Learning from Disaster |
10,161,774 | X_train.fillna(-9999, inplace = True)
X_test.fillna(-9999, inplace = True )<count_values> | test_df['Survived'] = rf.predict(test_df.drop(['PassengerId'], axis = 1))
test_df[['PassengerId', 'Survived']].to_csv('MySubmission.csv', index = False ) | Titanic - Machine Learning from Disaster |
9,761,238 | for col in X_train.columns:
print(X_train[col].value_counts())
print('____' )<split> | train_data = pd.read_csv("/kaggle/input/titanic/train.csv")
train_data.head() | Titanic - Machine Learning from Disaster |
9,761,238 | %%time
num_split = 5
num_iter = 3
stop_round = 50
scores = []
y_pred_cva = np.zeros(len(X_test))
scores = []
for h in range(num_iter):
kf = KFold(n_splits=num_split, random_state=h, shuffle=True)
for i,(train_ix, test_ix)in tqdm(enumerate(kf.split(X_train, y_train))):
X_train_, y_train_ = X_train.values[train_ix], y_t... | test_data = pd.read_csv("/kaggle/input/titanic/test.csv")
test_data.head() | Titanic - Machine Learning from Disaster |
9,761,238 | y_pred_exp = np.exp(y_pred_cva)- 1
y_pred_exp<load_from_csv> | pip install lofo-importance | Titanic - Machine Learning from Disaster |
9,761,238 | submission = pd.read_csv('.. /input/exam-for-students20200129/sample_submission.csv', index_col=0)
submission.ConvertedSalary = y_pred_exp<save_to_csv> | %matplotlib inline | Titanic - Machine Learning from Disaster |
9,761,238 | submission.to_csv('submission.csv')
submission<set_options> | 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 | Titanic - Machine Learning from Disaster |
9,761,238 | %matplotlib inline
plt.style.use('ggplot')
pd.set_option('display.max_columns', 500)
pd.set_option('display.max_rows', 20 )<import_modules> | 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 ) | Titanic - Machine Learning from Disaster |
9,761,238 | from sklearn.preprocessing import StandardScaler
from sklearn.metrics import roc_auc_score
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import StratifiedKFold, KFold
from sklearn.feature_extraction.text import TfidfVectorizer
from category_encoders import OrdinalEncoder, OneHotEncoder, Ta... | 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]} | Titanic - Machine Learning from Disaster |
9,761,238 | df_train = pd.read_csv('.. /input/exam-for-students20200129/train.csv', index_col=0)
<load_from_csv> | cv_model = GridSearchCV(model , params, cv=10, verbose=2 , n_jobs=-1 ).fit(X , y ) | Titanic - Machine Learning from Disaster |
9,761,238 | df_test = pd.read_csv('.. /input/exam-for-students20200129/test.csv', index_col=0)
<define_variables> | cv_model.best_params_ | Titanic - Machine Learning from Disaster |
9,761,238 | tgt_col = 'ConvertedSalary'<data_type_conversions> | model_tuned = lgb.LGBMClassifier(max_depth= 10, feature_fraction=0.1, learning_rate=0.1,
num_leaves= 16,
subsample= 0.2 ).fit(X , y ) | Titanic - Machine Learning from Disaster |
9,761,238 | df_train2 = df_train.copy()
df_test2 = df_test.copy()
df_train2['missing_amnt'] = df_train2.isnull().sum(axis=1)
df_test2['missing_amnt'] = df_test2.isnull().sum(axis=1 )<feature_engineering> | output = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
output.to_csv('my_submission.csv', index=False)
print("Your submission was successfully saved!" ) | Titanic - Machine Learning from Disaster |
9,287,401 | df_train2[tgt_col] = np.log1p(df_train2[tgt_col] )<prepare_x_and_y> | train = pd.read_csv(".. /input/titanic/train.csv")
test = pd.read_csv(".. /input/titanic/test.csv" ) | Titanic - Machine Learning from Disaster |
9,287,401 | y_train = df_train2[tgt_col]
X_train = df_train2.drop([tgt_col], axis=1)
X_test = df_test2<normalization> | data_size_train = train.memory_usage().sum() / 1024 / 1024
print("Data memory size: %.2f MB" % data_size_train ) | Titanic - Machine Learning from Disaster |
9,287,401 | num_cols = []
for col in X_train.columns:
if X_train[col].dtype == 'int64' or X_train[col].dtype == 'float64':
num_cols.append(col)
scaler = StandardScaler()
scaler.fit(X_train[num_cols])
X_train[num_cols] = scaler.transform(X_train[num_cols])
X_test[num_cols] = scaler.transform(X_test[num_cols] )<categorify> | data_size_test = test.memory_usage().sum() / 1024 / 1024
print("Data memory size: %.2f MB" % data_size_test ) | Titanic - Machine Learning from Disaster |
9,287,401 | def student_enc(x):
if x == 'No':
return 3
elif x == 'Yes, part-time':
return 2
elif x == 'Yes, full-time':
return 1<feature_engineering> | 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("-... | Titanic - Machine Learning from Disaster |
9,287,401 | X_train['Student'] = X_train['Student'].apply(student_enc)
X_test['Student'] = X_test['Student'].apply(student_enc )<categorify> | 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() ) | Titanic - Machine Learning from Disaster |
9,287,401 | def company_size_enc(x):
if x == 'Fewer than 10 employees':
return 1
elif x == '10 to 19 employees':
return 2
elif x == '20 to 99 employees':
return 3
elif x == '100 to 499 employees':
return 4
elif x == '500 to 999 employees':
return 5
elif x == '1,000 to 4,999 employees':
return 6
elif x == '5,000 to 9,999 employees'... | 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() ) | Titanic - Machine Learning from Disaster |
9,287,401 | X_train['CompanySize'] = X_train['CompanySize'].apply(company_size_enc)
X_test['CompanySize'] = X_test['CompanySize'].apply(company_size_enc )<categorify> | train["FamilySize"] = train["SibSp"] + train["Parch"]
test["FamilySize"] = test["SibSp"] + test["Parch"] | Titanic - Machine Learning from Disaster |
9,287,401 | def years_coding_enc(x):
if x == '0-2 years':
return 1
elif x == '3-5 years':
return 2
elif x == '6-8 years':
return 3
elif x == '9-11 years':
return 4
elif x == '12-14 years':
return 5
elif x == '15-17 years':
return 6
elif x == '18-20 years':
return 7
elif x == '21-23 years':
return 8
elif x == '24-26 years':
return ... | 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... | Titanic - Machine Learning from Disaster |
9,287,401 | X_train['YearsCoding'] = X_train['YearsCoding'].apply(years_coding_enc)
X_test['YearsCoding'] = X_test['YearsCoding'].apply(years_coding_enc )<categorify> | 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"
te... | Titanic - Machine Learning from Disaster |
9,287,401 | def years_coding_prof_enc(x):
if x == '0-2 years':
return 1
elif x == '3-5 years':
return 2
elif x == '6-8 years':
return 3
elif x == '9-11 years':
return 4
elif x == '12-14 years':
return 5
elif x == '15-17 years':
return 6
elif x == '18-20 years':
return 7
elif x == '21-23 years':
return 8
elif x == '24-26 years':
re... | train = train.drop(["SibSp", "Parch"], axis = 1)
test = test.drop(["SibSp", "Parch"], axis = 1 ) | Titanic - Machine Learning from Disaster |
9,287,401 | X_train['YearsCodingProf'] = X_train['YearsCodingProf'].apply(years_coding_prof_enc)
X_test['YearsCodingProf'] = X_test['YearsCodingProf'].apply(years_coding_prof_enc )<categorify> | train = pd.get_dummies(train, columns = ["Sex", "Embarked", "NameTitle", "AgeGroup", "FareGroup"])
test = pd.get_dummies(test, columns = ["Sex", "Embarked", "NameTitle", "AgeGroup", "FareGroup"] ) | Titanic - Machine Learning from Disaster |
9,287,401 | def job_satisfaction_enc(x):
if x == 'Extremely dissatisfied':
return 1
elif x == 'Moderately dissatisfied':
return 2
elif x == 'Slightly dissatisfied':
return 3
elif x == 'Neither satisfied nor dissatisfied':
return 4
elif x == 'Slightly satisfied':
return 5
elif x == 'Moderately satisfied':
return 6
elif x == 'Extrem... | 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 ) | Titanic - Machine Learning from Disaster |
9,287,401 | X_train['JobSatisfaction'] = X_train['JobSatisfaction'].apply(job_satisfaction_enc)
X_test['JobSatisfaction'] = X_test['JobSatisfaction'].apply(job_satisfaction_enc )<categorify> | X = train.drop(["PassengerId", "Survived"], axis = 1)
Y = train["Survived"] | Titanic - Machine Learning from Disaster |
9,287,401 | def career_satisfaction_enc(x):
if x == 'Extremely dissatisfied':
return 1
elif x == 'Moderately dissatisfied':
return 2
elif x == 'Slightly dissatisfied':
return 3
elif x == 'Neither satisfied nor dissatisfied':
return 4
elif x == 'Slightly satisfied':
return 5
elif x == 'Moderately satisfied':
return 6
elif x == 'Ext... | 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 xgb... | Titanic - Machine Learning from Disaster |
9,287,401 | X_train['CareerSatisfaction'] = X_train['CareerSatisfaction'].apply(career_satisfaction_enc)
X_test['CareerSatisfaction'] = X_test['CareerSatisfaction'].apply(career_satisfaction_enc )<categorify> | 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 = Stratifie... | Titanic - Machine Learning from Disaster |
9,287,401 | def age_enc(x):
if x == 'Under 18 years old':
return 1
elif x == '18 - 24 years old':
return 2
elif x == '25 - 34 years old':
return 3
elif x == '35 - 44 years old':
return 4
elif x == '45 - 54 years old':
return 5
elif x == '55 - 64 years old':
return 6
elif x == '65 years or older':
return 7<feature_engineering> | pipe_xgb = Pipeline([
("scaler", StandardScaler()),
("pca", PCA()),
("xgb", XGBClassifier())])
params_xgb = {
"pca__n_components" : [2, 6, 10, 18],
"xgb__n_estimators" : [300, 500, 700],
"xgb__learning_rate" : [0.005, 0.1],
"xgb__max_depth" : [5, 7],
"xgb__max_features" : [3, 5],
"xgb__gamma" : [0.5, 0.6, 0.7]}
xgb... | Titanic - Machine Learning from Disaster |
9,287,401 | X_train['Age'] = X_train['Age'].apply(age_enc)
X_test['Age'] = X_test['Age'].apply(age_enc )<categorify> | pipe_boost = Pipeline([
("scaler", StandardScaler()),
("pca", PCA()),
("boost", GradientBoostingClassifier())])
params_boost = {
"pca__n_components" : [2, 6, 10, 18],
"boost__n_estimators" : [300, 500, 700],
"boost__learning_rate" : [0.005, 0.1],
"boost__max_depth" : [3, 5],
"boost__max_features" : [3, 5]}
boost_sc... | Titanic - Machine Learning from Disaster |
9,287,401 | def edu_enc(x):
if x == 'They never completed any formal education':
return 1
elif x == 'Primary/elementary school':
return 2
elif x == 'Secondary school(e.g.American high school, German Realschule or Gymnasium, etc.) ':
return 3
elif x == 'Some college/university study without earning a degree':
return 4
elif x == 'Ba... | pipe_xt = Pipeline([
("scaler", StandardScaler()),
("pca", PCA()),
("xt", ExtraTreesClassifier(criterion = "gini",
max_features = "auto")) ])
params_xt = {
"pca__n_components" : [2, 6, 10, 18],
"xt__n_estimators" : [300, 500, 700],
"xt__max_depth" : [5, 7, 9]}
xt_score, grid_xt = model_builder(X, Y, pipe_xt, params... | Titanic - Machine Learning from Disaster |
9,287,401 | cat_cols = []
for col in X_train.columns:
if X_train[col].dtype == 'object':
cat_cols.append(col)
<categorify> | pipe_knn = Pipeline([
("scaler", StandardScaler()),
("knn", KNeighborsClassifier(algorithm = "auto")) ])
params_knn = {
"knn__n_neighbors" : [2, 3, 5, 7, 9],
"knn__leaf_size" : [10, 20, 30, 40]}
knn_score, grid_knn = model_builder(X, Y, pipe_knn, params_knn, 10, "KNN")
Y_predicted_knn = grid_knn.predict_proba(X)[:,... | Titanic - Machine Learning from Disaster |
9,287,401 | for i in cat_cols:
summary = X_train[i].value_counts() / len(X_train)
X_train[i] = X_train[i].map(summary)
X_test[i] = X_test[i].map(summary )<feature_engineering> | all_results = [xgb_score, boost_score, log_score, rf_score,
svm_score, bag_score, xt_score, knn_score, cat_score, gbm_score]
result_names = ["XGBoost",
"Gradient Boosting",
"Logistic Regression",
"Random Forest",
"SVM",
"Bagging Classifier",
"Extra Trees Classifier",
"KNN",
"CatBoost",
"LightGBM"]
fig = plt.figure(figs... | Titanic - Machine Learning from Disaster |
9,287,401 | X_train['Country_LastNewJob'] = X_train['Country'] * X_train['LastNewJob']
X_train['Country_YearsCodingProf'] = X_train['Country'] * X_train['YearsCodingProf']
X_train['LastNewJob_YearsCodingProf'] = X_train['LastNewJob'] * X_train['YearsCodingProf']<feature_engineering> | %%capture
!pip install keras-tuner | Titanic - Machine Learning from Disaster |
9,287,401 | X_test['Country_LastNewJob'] = X_test['Country'] * X_test['LastNewJob']
X_test['Country_YearsCodingProf'] = X_test['Country'] * X_test['YearsCodingProf']
X_test['LastNewJob_YearsCodingProf'] = X_test['LastNewJob'] * X_test['YearsCodingProf']<prepare_x_and_y> | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from kerastuner.tuners import RandomSearch | Titanic - Machine Learning from Disaster |
9,287,401 | X_train.fillna(X_train.median() , inplace=True)
X_test.fillna(X_train.median() , inplace=True )<split> | X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
random_state = 0,
test_size = 0.25 ) | Titanic - Machine Learning from Disaster |
9,287,401 | scores = []
kf = KFold(n_splits=5, random_state=71, shuffle=True)
for i,(train_ix, test_ix)in enumerate(tqdm(kf.split(X_train, y_train))):
X_train_, y_train_ = X_train.values[train_ix], y_train.values[train_ix]
X_val, y_val = X_train.values[test_ix], y_train.values[test_ix]
model = lgb.LGBMRegressor(boosting_type='gbd... | %%capture
def tune_model(hp):
nn = Sequential()
nn.add(Dense(hp.Choice("units_1", [512, 1024, 2048]), activation = "relu", input_shape =(18,)))
nn.add(Dropout(hp.Choice("dropout_1", [0.0, 0.15, 0.2, 0.25])))
nn.add(Dense(hp.Choice("units_2", [128, 256, 512]), activation = "relu"))
nn.add(Dropout(hp.Choice("dropout_2"... | Titanic - Machine Learning from Disaster |
9,287,401 | model.fit(X_train, y_train)
y_pred = model.predict(X_test )<save_to_csv> | print("Best Params: " + str(tuner.oracle.get_best_trials(num_trials = 1)[0].hyperparameters.values))
nn = tuner.get_best_models() [0]
print("")
print("Test score: " + str(nn.evaluate(X_test, Y_test)))
print("")
print("Train score: " + str(nn.evaluate(X_train, Y_train)) ) | Titanic - Machine Learning from Disaster |
9,287,401 | submission = pd.read_csv('.. /input/exam-for-students20200129/sample_submission.csv', index_col=0)
submission[tgt_col] = y_pred
submission[tgt_col] = np.exp(submission[tgt_col])- 1
submission.to_csv('submission.csv')
<import_modules> | Y_predicted_nn = nn.predict(test.drop(["PassengerId"], axis = 1))
Y_predicted_nn =(Y_predicted_nn.ravel() > 0.5 ).astype(int ) | Titanic - Machine Learning from Disaster |
9,287,401 | from sklearn.feature_extraction.text import TfidfVectorizer
from nltk.corpus import stopwords
from nltk import word_tokenize
import pandas as pd
import numpy as np
import re
from sklearn.model_selection import train_test_split
from collections import defaultdict
from sklearn.linear_model import LogisticRegression
from ... | from sklearn.metrics import roc_curve, roc_auc_score | Titanic - Machine Learning from Disaster |
9,287,401 | with open('.. /input/train.json')as f:
raw_train = json.load(f)
with open('.. /input/test.json')as f:
raw_test = json.load(f )<string_transform> | log_fpr, log_tpr, log_treshholds = roc_curve(Y, Y_predicted_log)
boost_fpr, boost_tpr, boost_treshholds = roc_curve(Y, Y_predicted_boost)
svm_fpr, svm_tpr, svm_treshholds = roc_curve(Y, Y_predicted_svm)
rf_fpr, rf_tpr, rf_treshholds = roc_curve(Y, Y_predicted_rf)
xgb_fpr, xgb_tpr, xgb_treshholds = roc_curve(Y, Y_pr... | Titanic - Machine Learning from Disaster |
9,287,401 | def ru_token(string):
return [i for i in word_tokenize(string)if re.match(r'[\u0400-\u04ffа́]+$', i)]<init_hyperparams> | stack = StackingClassifier(estimators = [("XGBoost", grid_xgb),
("GradientBoosting", grid_boost),
("RandomForest", grid_rf),
("Logistic Regression", grid_log),
("SVM", grid_svm),
("Extra Trees Classifier", grid_xt),
("KNeighborsClassifier", grid_knn),
("CatBoost", grid_cat),
("LightGBM", grid_gbm)], n_jobs = -1... | Titanic - Machine Learning from Disaster |
9,287,401 | params = {}
params['tokenizer'] = ru_token
params['stop_words'] = stopwords.words('russian')
params['ngram_range'] =(1, 3)
params['min_df'] = 3<feature_engineering> | submission = pd.DataFrame({"PassengerId" : test.PassengerId,
"Survived" : Y_predicted_stack})
submission.to_csv("submission_clf.csv", index = False ) | Titanic - Machine Learning from Disaster |
9,287,401 | tfidf = TfidfVectorizer(**params )<train_model> | submission_nn = pd.DataFrame({"PassengerId" : test.PassengerId,
"Survived" : Y_predicted_nn})
submission_nn.to_csv("submission_nn.csv", index = False ) | Titanic - Machine Learning from Disaster |
9,287,401 | tfidf.fit([i['text'] for i in raw_train + raw_test] )<split> | train = pd.read_csv(".. /input/titanic/train.csv")
test = pd.read_csv(".. /input/titanic/test.csv" ) | Titanic - Machine Learning from Disaster |
9,287,401 | train = {}
val = {}
tmp = defaultdict(list)
for e in raw_train:
tmp[e['sentiment']].append(e['text'])
for l in tmp:
train[l], val[l] = train_test_split(tmp[l], test_size=0.2, random_state=2018 )<statistical_test> | data_size_train = train.memory_usage().sum() / 1024 / 1024
print("Data memory size: %.2f MB" % data_size_train ) | Titanic - Machine Learning from Disaster |
9,287,401 | def upsampling_align(some_dict, random_state=2018):
rand = np.random.RandomState(random_state)
upper = max([len(some_dict[l])for l in some_dict])
print('upper bound: {}'.format(upper))
tmp = {}
for l in some_dict:
if len(some_dict[l])< upper:
repeat_time = int(upper/len(some_dict[l]))
remainder = upper % len(some_dic... | data_size_test = test.memory_usage().sum() / 1024 / 1024
print("Data memory size: %.2f MB" % data_size_test ) | Titanic - Machine Learning from Disaster |
9,287,401 | btrain = upsampling_align(train )<init_hyperparams> | 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("-... | Titanic - Machine Learning from Disaster |
9,287,401 | m_params = {}
m_params['solver'] = 'lbfgs'
m_params['multi_class'] = 'multinomial'<choose_model_class> | 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() ) | Titanic - Machine Learning from Disaster |
9,287,401 | softmax = LogisticRegression(**m_params )<categorify> | 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() ) | Titanic - Machine Learning from Disaster |
9,287,401 | train_x = [j for i in sorted(btrain.keys())for j in btrain[i]]
train_y = [i for i in sorted(btrain.keys())for j in btrain[i]]
softmax.fit(tfidf.transform(train_x), train_y )<define_variables> | train["FamilySize"] = train["SibSp"] + train["Parch"]
test["FamilySize"] = test["SibSp"] + test["Parch"] | Titanic - Machine Learning from Disaster |
9,287,401 | test_x = [j for i in sorted(val.keys())for j in val[i]]
true = [i for i in sorted(val.keys())for j in val[i]]<predict_on_test> | 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... | Titanic - Machine Learning from Disaster |
9,287,401 | pred = softmax.predict(tfidf.transform(test_x))<compute_test_metric> | 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"
te... | Titanic - Machine Learning from Disaster |
9,287,401 | accuracy_score(true, pred )<categorify> | train = train.drop(["SibSp", "Parch"], axis = 1)
test = test.drop(["SibSp", "Parch"], axis = 1 ) | Titanic - Machine Learning from Disaster |
9,287,401 | lab = LabelEncoder()
c_true = lab.fit_transform(true)
c_pred = lab.transform(pred)
print(classification_report(c_true, c_pred, target_names=lab.classes_, digits=5))<concatenate> | train = pd.get_dummies(train, columns = ["Sex", "Embarked", "NameTitle", "AgeGroup", "FareGroup"])
test = pd.get_dummies(test, columns = ["Sex", "Embarked", "NameTitle", "AgeGroup", "FareGroup"] ) | Titanic - Machine Learning from Disaster |
9,287,401 | bval = upsampling_align(val )<categorify> | 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 ) | Titanic - Machine Learning from Disaster |
9,287,401 | b_test_x = [j for i in sorted(bval.keys())for j in bval[i]]
b_true = [i for i in sorted(bval.keys())for j in bval[i]]
b_pred = softmax.predict(tfidf.transform(b_test_x))
lab = LabelEncoder()
c_true = lab.fit_transform(b_true)
c_pred = lab.transform(b_pred)
print(classification_report(c_true, c_pred, target_names=lab.... | X = train.drop(["PassengerId", "Survived"], axis = 1)
Y = train["Survived"] | Titanic - Machine Learning from Disaster |
9,287,401 | sub_pred = softmax.predict(tfidf.transform([i['text'] for i in raw_test]))
sub_df = pd.DataFrame()
sub_df['id'] = [i['id'] for i in raw_test]
sub_df['sentiment'] = sub_pred<save_to_csv> | 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 xgb... | Titanic - Machine Learning from Disaster |
9,287,401 | sub_df.to_csv('softmax_reg.csv', index=False )<load_from_csv> | 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 = Stratifie... | Titanic - Machine Learning from Disaster |
9,287,401 | df_train = pd.read_csv('/kaggle/input/dma-fall19/yelp_train.csv')
df_test = pd.read_csv('/kaggle/input/dma-fall19/yelp_test.csv' )<correct_missing_values> | pipe_xgb = Pipeline([
("scaler", StandardScaler()),
("pca", PCA()),
("xgb", XGBClassifier())])
params_xgb = {
"pca__n_components" : [2, 6, 10, 18],
"xgb__n_estimators" : [300, 500, 700],
"xgb__learning_rate" : [0.005, 0.1],
"xgb__max_depth" : [5, 7],
"xgb__max_features" : [3, 5],
"xgb__gamma" : [0.5, 0.6, 0.7]}
xgb... | Titanic - Machine Learning from Disaster |
9,287,401 | df_train = df_train.dropna()<prepare_x_and_y> | pipe_boost = Pipeline([
("scaler", StandardScaler()),
("pca", PCA()),
("boost", GradientBoostingClassifier())])
params_boost = {
"pca__n_components" : [2, 6, 10, 18],
"boost__n_estimators" : [300, 500, 700],
"boost__learning_rate" : [0.005, 0.1],
"boost__max_depth" : [3, 5],
"boost__max_features" : [3, 5]}
boost_sc... | Titanic - Machine Learning from Disaster |
9,287,401 | x_train = df_train[['cool', 'funny', 'useful', 'user_average_stars',
'user_review_count', 'business_latitude', 'business_longitude',
'business_review_count', 'business_average_stars']]
x_test = df_test[['cool', 'funny', 'useful', 'user_average_stars',
'user_review_count', 'business_latitude', 'business_longitude',
'bus... | pipe_xt = Pipeline([
("scaler", StandardScaler()),
("pca", PCA()),
("xt", ExtraTreesClassifier(criterion = "gini",
max_features = "auto")) ])
params_xt = {
"pca__n_components" : [2, 6, 10, 18],
"xt__n_estimators" : [300, 500, 700],
"xt__max_depth" : [5, 7, 9]}
xt_score, grid_xt = model_builder(X, Y, pipe_xt, params... | Titanic - Machine Learning from Disaster |
9,287,401 | clf = DecisionTreeClassifier()
clf.fit(x_train, y_train)
accuracy_score(clf.predict(x_train), y_train )<predict_on_test> | pipe_knn = Pipeline([
("scaler", StandardScaler()),
("knn", KNeighborsClassifier(algorithm = "auto")) ])
params_knn = {
"knn__n_neighbors" : [2, 3, 5, 7, 9],
"knn__leaf_size" : [10, 20, 30, 40]}
knn_score, grid_knn = model_builder(X, Y, pipe_knn, params_knn, 10, "KNN")
Y_predicted_knn = grid_knn.predict_proba(X)[:,... | Titanic - Machine Learning from Disaster |
9,287,401 | clf.predict(x_test )<predict_on_test> | all_results = [xgb_score, boost_score, log_score, rf_score,
svm_score, bag_score, xt_score, knn_score, cat_score, gbm_score]
result_names = ["XGBoost",
"Gradient Boosting",
"Logistic Regression",
"Random Forest",
"SVM",
"Bagging Classifier",
"Extra Trees Classifier",
"KNN",
"CatBoost",
"LightGBM"]
fig = plt.figure(figs... | Titanic - Machine Learning from Disaster |
9,287,401 | submission = pd.DataFrame(index=df_test.review_id)
submission['is_good_rating'] = clf.predict(x_test )<save_to_csv> | %%capture
!pip install keras-tuner | Titanic - Machine Learning from Disaster |
9,287,401 | submission.reset_index().to_csv('submission.csv', index=False )<load_from_csv> | import tensorflow as tf
from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout
from kerastuner.tuners import RandomSearch | Titanic - Machine Learning from Disaster |
9,287,401 | train_df = pd.read_csv('.. /input/train.csv')
test_df = pd.read_csv('.. /input/test.csv' )<create_dataframe> | X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
random_state = 0,
test_size = 0.25 ) | Titanic - Machine Learning from Disaster |
9,287,401 | submission_pd = pd.DataFrame(test_df.Id )<feature_engineering> | %%capture
def tune_model(hp):
nn = Sequential()
nn.add(Dense(hp.Choice("units_1", [512, 1024, 2048]), activation = "relu", input_shape =(18,)))
nn.add(Dropout(hp.Choice("dropout_1", [0.0, 0.15, 0.2, 0.25])))
nn.add(Dense(hp.Choice("units_2", [128, 256, 512]), activation = "relu"))
nn.add(Dropout(hp.Choice("dropout_2"... | Titanic - Machine Learning from Disaster |
9,287,401 | submission_pd['SalePrice'] = ybar<save_to_csv> | print("Best Params: " + str(tuner.oracle.get_best_trials(num_trials = 1)[0].hyperparameters.values))
nn = tuner.get_best_models() [0]
print("")
print("Test score: " + str(nn.evaluate(X_test, Y_test)))
print("")
print("Train score: " + str(nn.evaluate(X_train, Y_train)) ) | Titanic - Machine Learning from Disaster |
9,287,401 | submission_pd.to_csv('submission0.csv' )<save_to_csv> | Y_predicted_nn = nn.predict(test.drop(["PassengerId"], axis = 1))
Y_predicted_nn =(Y_predicted_nn.ravel() > 0.5 ).astype(int ) | Titanic - Machine Learning from Disaster |
9,287,401 | submission_pd.to_csv('submission.csv', index=False )<install_modules> | from sklearn.metrics import roc_curve, roc_auc_score | Titanic - Machine Learning from Disaster |
9,287,401 | !pip install -q albumentations
!pip install -q pretrainedmodels
!pip install -q kekas
!pip install -q adabound<set_options> | log_fpr, log_tpr, log_treshholds = roc_curve(Y, Y_predicted_log)
boost_fpr, boost_tpr, boost_treshholds = roc_curve(Y, Y_predicted_boost)
svm_fpr, svm_tpr, svm_treshholds = roc_curve(Y, Y_predicted_svm)
rf_fpr, rf_tpr, rf_treshholds = roc_curve(Y, Y_predicted_rf)
xgb_fpr, xgb_tpr, xgb_treshholds = roc_curve(Y, Y_pr... | Titanic - Machine Learning from Disaster |
9,287,401 | %matplotlib inline
train_on_gpu = True
Blur, OpticalDistortion, GridDistortion, HueSaturationValue, Flip, VerticalFlip
print(os.listdir(".. /input/"))
sample_sub = pd.read_csv('.. /input/sample_submission.csv')
<prepare_output> | stack = StackingClassifier(estimators = [("XGBoost", grid_xgb),
("GradientBoosting", grid_boost),
("RandomForest", grid_rf),
("Logistic Regression", grid_log),
("SVM", grid_svm),
("Extra Trees Classifier", grid_xt),
("KNeighborsClassifier", grid_knn),
("CatBoost", grid_cat),
("LightGBM", grid_gbm)], n_jobs = -1... | Titanic - Machine Learning from Disaster |
9,287,401 | train_folder = '.. /input/plates/plates/train/'
test_folder = '.. /input/plates/plates/test/'
cleaned = 'cleaned'
dirty = 'dirty'
train_clean = os.listdir(train_folder + cleaned)
train_dirty = os.listdir(train_folder + dirty)
test_files = os.listdir(test_folder)
train_df = pd.concat([pd.DataFrame(train_clean, column... | submission = pd.DataFrame({"PassengerId" : test.PassengerId,
"Survived" : Y_predicted_stack})
submission.to_csv("submission_clf.csv", index = False ) | Titanic - Machine Learning from Disaster |
9,287,401 | <init_hyperparams><EOS> | submission_nn = pd.DataFrame({"PassengerId" : test.PassengerId,
"Survived" : Y_predicted_nn})
submission_nn.to_csv("submission_nn.csv", index = False ) | Titanic - Machine Learning from Disaster |
8,659,445 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<split> | warnings.filterwarnings('ignore')
| Titanic - Machine Learning from Disaster |
8,659,445 | train, valid = train_test_split(train_df, stratify=train_df.label, test_size=0.15 )<load_pretrained> | train = pd.read_csv(".. /input/titanic/train.csv")
test = pd.read_csv(".. /input/titanic/test.csv" ) | Titanic - Machine Learning from Disaster |
8,659,445 | train_tfms, val_tfms = get_transforms("image", 224, 0.5)
train_dk = DataKek(df=train, reader_fn=reader_train_fn, transforms=train_tfms)
val_dk = DataKek(df=valid, reader_fn=reader_train_fn, transforms=val_tfms)
test_dk = DataKek(df=test_df, reader_fn=reader_test_fn, transforms=val_tfms)
batch_size = 2
workers = 8
t... | def detect_outliers(dataframe, n, features):
outliers_indices = []
for feature in features:
Quart1 = dataframe[feature].quantile(0.25)
Quart3 = dataframe[feature].quantile(0.75)
upper_outlier_threshold = Quart3 +(Quart3 - Quart1)* 1.5
lower_outlier_threshold = Quart1 -(Quart3 - Quart1)* 1.5
feature_outliers_list = da... | Titanic - Machine Learning from Disaster |
8,659,445 | dataowner = DataOwner(train_dl, val_dl, None)
BCEmodel = Net(num_classes=1)
CEmodel = Net(num_classes=2)
CEcriterion = nn.CrossEntropyLoss()
BCEcriterion = nn.BCEWithLogitsLoss()<compute_train_metric> | outliers = detect_outliers(train, 2, ["Age", "SibSp", "Fare", "Parch"])
train.loc[outliers] | Titanic - Machine Learning from Disaster |
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