kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
12,734,089 | Counter(X_train['AdoptionSpeed'] )<count_values> | df_num = training[['Age','SibSp', 'Parch', 'Fare']]
df_cat = training[['Survived', 'Pclass','Sex','Ticket', 'Cabin', 'Embarked']] | Titanic - Machine Learning from Disaster |
12,734,089 | Counter(X_train['AdoptionSpeed'] )<predict_on_test> | pd.pivot_table(training, index = 'Survived', values = ['Age','Fare','SibSp','Parch'] ) | Titanic - Machine Learning from Disaster |
12,734,089 | coeffs[0] = 1.69
coeffs[1] = 2.2
coeffs[3] = 2.9
print('True Counter',Counter(X_train['AdoptionSpeed']))
train_pred_keras = eval_predict(y_pred=oof_train_keras, coeffs=list(coeffs)).astype(int)
print('Train pred Counter',Counter(train_pred_keras))
test_pred_keras = eval_predict(y_pred=adoptions_keras/nfolds, coeffs=li... | for i in ['Pclass', 'Sex', 'Embarked']:
print(pd.pivot_table(training, index = 'Survived', columns = i, values = 'Ticket', aggfunc = 'count'), '
' ) | Titanic - Machine Learning from Disaster |
12,734,089 | train_stack = np.vstack([train_pred_keras, train_predictions_lgb] ).transpose()
test_stack = np.vstack([test_pred_keras, test_predictions_lgb] ).transpose()<import_modules> | df_cat.Cabin
training['cabin_multiple'] = training.Cabin.apply(lambda x: 0 if pd.isna(x)else len(x.split(' ')))
training['cabin_multiple'].value_counts()
| Titanic - Machine Learning from Disaster |
12,734,089 | from sklearn.linear_model import Ridge<prepare_x_and_y> | pd.pivot_table(training, index = 'Survived', columns = 'cabin_multiple', values = 'Ticket', aggfunc = 'count' ) | Titanic - Machine Learning from Disaster |
12,734,089 | folds = StratifiedKFold(n_splits=10, shuffle=True, random_state=15)
oof = np.zeros(train_stack.shape[0])
predictions = np.zeros(test_stack.shape[0])
qwk_scores = []
for fold_,(trn_idx, val_idx)in enumerate(folds.split(train_stack, X_train['AdoptionSpeed'].values)) :
trn_data, trn_y = train_stack[trn_idx], X_train['A... | training['cabin_adv'] = training.Cabin.apply(lambda x : str(x)[0] ) | Titanic - Machine Learning from Disaster |
12,734,089 | pred_final = eval_predict(y_pred=predictions, coeffs=list(coeffs)).astype(int )<count_values> | print(training.cabin_adv.value_counts())
pd.pivot_table(training, index='Survived', columns='cabin_adv', values='Name', aggfunc='count' ) | Titanic - Machine Learning from Disaster |
12,734,089 | Counter(pred_final )<count_values> | training['numeric_tickets'] = training.Ticket.apply(lambda x : 1 if x.isnumeric() else 0)
training['ticket_letters'] = training.Ticket.apply(lambda x : ''.join(x.split(' ')[:-1] ).replace('.','' ).replace('/','' ).lower()
if len(x.split(' ')[:-1])> 0 else 0)
print(training['numeric_tickets'].value_counts() ) | Titanic - Machine Learning from Disaster |
12,734,089 | Counter(test_pred_keras )<count_values> | pd.pivot_table(training, index='Survived', columns='numeric_tickets', values='Name', aggfunc='count' ) | Titanic - Machine Learning from Disaster |
12,734,089 | Counter(test_predictions_lgb )<save_to_csv> | training['ticket_letters'].value_counts() | Titanic - Machine Learning from Disaster |
12,734,089 | submission = pd.DataFrame({'PetID': test['PetID'].values, 'AdoptionSpeed': pred_final.astype(np.int32)})
submission.head()
submission.to_csv('submission.csv', index=False )<import_modules> | training.Name.head(50)
training['name_title'] = training.Name.apply(lambda x : x.split(',')[1].split('.')[0].strip())
training['name_title'].value_counts() | Titanic - Machine Learning from Disaster |
12,734,089 | def kappa(y_true, y_pred):
return cohen_kappa_score(y_true, y_pred, weights='quadratic')
def warn(*args, **kwargs):
pass
warnings.warn = warn
%matplotlib inline
pd.options.display.max_rows = 128
pd.options.display.max_columns = 128<set_options> | all_data['cabin_multiple'] = all_data.Cabin.apply(lambda x: 0 if pd.isna(x)else len(x.split(' ')))
all_data['cabin_adv'] = all_data.Cabin.apply(lambda x : str(x)[0])
all_data['numeric_tickets'] = all_data.Ticket.apply(lambda x : 1 if x.isnumeric() else 0)
all_data['ticket_letters'] = all_data.Ticket.apply(lambda x :... | Titanic - Machine Learning from Disaster |
12,734,089 | plt.rcParams['figure.figsize'] =(12, 9 )<load_from_csv> | Scale = StandardScaler()
all_dummies_scaled = all_dummies.copy()
all_dummies_scaled[['Age', 'SibSp', 'Parch', 'norm_fare']] = Scale.fit_transform(all_dummies_scaled[['Age', 'SibSp', 'Parch', 'norm_fare']])
x_train_scaled = all_dummies_scaled[all_dummies_scaled.train_test == 1].drop(['train_test'], axis = 1)
x_test_sc... | Titanic - Machine Learning from Disaster |
12,734,089 | train = pd.read_csv('.. /input/petfinder-adoption-prediction/train/train.csv')
test = pd.read_csv('.. /input/petfinder-adoption-prediction/test/test.csv')
sample_submission = pd.read_csv('.. /input/petfinder-adoption-prediction/test/sample_submission.csv' )<load_from_csv> | from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.linear_model import LogisticRegression
from sklearn import tree
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.svm import SVC | Titanic - Machine Learning from Disaster |
12,734,089 | labels_breed = pd.read_csv('.. /input/petfinder-adoption-prediction/breed_labels.csv')
labels_state = pd.read_csv('.. /input/petfinder-adoption-prediction/color_labels.csv')
labels_color = pd.read_csv('.. /input/petfinder-adoption-prediction/state_labels.csv' )<load_from_csv> | classifiers = {} | Titanic - Machine Learning from Disaster |
12,734,089 | train_img = pd.read_csv(".. /input/extract-image-features-from-pretrained-nn/train_img_features.csv")
test_img = pd.read_csv(".. /input/extract-image-features-from-pretrained-nn/test_img_features.csv")
train_img.rename(columns=lambda i: f"img_{i}" ,inplace=True)
test_img.rename(columns=lambda i: f"img_{i}" ,inplace=... | gnb = GaussianNB()
cv = cross_validate(gnb, x_train, y_train, cv = 5, return_train_score = True, return_estimator = True)
classifiers['GaussianNBNotScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
')
print(cv['test_score'].mean() ) | Titanic - Machine Learning from Disaster |
12,734,089 | with open('.. /input/cat-and-dog-breeds-parameters/rating.json', 'r')as f:
ratings = json.load(f )<feature_engineering> | gnb = GaussianNB()
cv = cross_validate(gnb, x_train_scaled, y_train, cv = 5, return_train_score = True, return_estimator = True)
classifiers['GaussianNBScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
')
print(cv['test_score'].mean... | Titanic - Machine Learning from Disaster |
12,734,089 | breed_id = {}
for id,name in zip(labels_breed.BreedID,labels_breed.BreedName):
breed_id[id] = name<create_dataframe> | lr = LogisticRegression(max_iter = 2000)
cv = cross_validate(lr, x_train, y_train, cv = 5, return_train_score = True, return_estimator = True)
classifiers['LogisticRegressionNotScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
')
p... | Titanic - Machine Learning from Disaster |
12,734,089 | breed_ratings = json.load(open(".. /input/cat-and-dog-breeds-parameters/rating.json",'r'))
species_keys = list(breed_ratings.keys())
breed_ratings_dict = {**breed_ratings['cat_breeds']}
breed_ratings_dict = {**breed_ratings_dict,**breed_ratings['dog_breeds']}
breed_score_df = pd.DataFrame(breed_ratings_dict ).T.reset_... | lr = LogisticRegression(max_iter = 2000)
cv = cross_validate(lr, x_train_scaled, y_train, cv = 5, return_train_score = True, return_estimator = True)
classifiers['LogisticRegressionScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
'... | Titanic - Machine Learning from Disaster |
12,734,089 | train_image_files = sorted(glob.glob('.. /input/petfinder-adoption-prediction/train_images/*.jpg'))
train_metadata_files = sorted(glob.glob('.. /input/petfinder-adoption-prediction/train_metadata/*.json'))
train_sentiment_files = sorted(glob.glob('.. /input/petfinder-adoption-prediction/train_sentiment/*.json'))
print(... | dt = tree.DecisionTreeClassifier(random_state = 10)
cv = cross_validate(dt,x_train,y_train,cv=5, return_train_score = True, return_estimator = True)
classifiers['DecisionTreeNotScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
')
p... | Titanic - Machine Learning from Disaster |
12,734,089 | test_df_ids = test[['PetID']]
print(test_df_ids.shape)
test_df_imgs = pd.DataFrame(test_image_files)
test_df_imgs.columns = ['image_filename']
test_imgs_pets = test_df_imgs['image_filename'].apply(lambda x: x.split('/')[-1].split('-')[0])
test_df_imgs = test_df_imgs.assign(PetID=test_imgs_pets)
print(len(test_imgs_... | dt = tree.DecisionTreeClassifier(random_state = 10)
cv = cross_validate(dt,x_train_scaled,y_train,cv=5, return_train_score = True, return_estimator = True)
classifiers['DecisionTreeScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
'... | Titanic - Machine Learning from Disaster |
12,734,089 | class PetFinderParser(object):
def __init__(self, debug=False):
self.debug = debug
self.sentence_sep = ' '
self.extract_sentiment_text = False
def open_metadata_file(self, filename):
with open(filename, 'r')as f:
metadata_file = json.load(f)
return metadata_file
def open_sentiment_file(self, filename):
with open(f... | knn = KNeighborsClassifier()
cv = cross_validate(knn,x_train,y_train,cv=5, return_train_score = True, return_estimator = True)
classifiers['KNeighborsNotScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
')
print(cv['test_score'].mea... | Titanic - Machine Learning from Disaster |
12,734,089 | def impact_coding(data, feature, target='y'):
n_folds = 20
n_inner_folds = 10
impact_coded = pd.Series()
oof_default_mean = data[target].mean()
kf = KFold(n_splits=n_folds, shuffle=True)
oof_mean_cv = pd.DataFrame()
split = 0
for infold, oof in kf.split(data[feature]):
impact_coded_cv = pd.Series()
kf_inner = KFold(... | knn = KNeighborsClassifier()
cv = cross_validate(knn,x_train_scaled,y_train,cv=5, return_train_score = True, return_estimator = True)
classifiers['KNeighborsScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
')
print(cv['test_score']... | Titanic - Machine Learning from Disaster |
12,734,089 | aggregates = ['mean', 'sum', 'var']
train_metadata_desc = train_dfs_metadata.groupby(['PetID'])['metadata_annots_top_desc'].unique()
train_metadata_desc = train_metadata_desc.reset_index()
train_metadata_desc[
'metadata_annots_top_desc'] = train_metadata_desc[
'metadata_annots_top_desc'].apply(lambda x: ' '.join(x))
pr... | rf = RandomForestClassifier(random_state = 10)
cv = cross_validate(rf,x_train,y_train,cv=5, return_train_score = True, return_estimator = True)
classifiers['RandomForestNotScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
')
print(... | Titanic - Machine Learning from Disaster |
12,734,089 | train_proc = train.copy()
train_proc = train_proc.merge(
train_sentiment_gr, how='left', on='PetID')
train_proc = train_proc.merge(
train_metadata_gr, how='left', on='PetID')
train_proc = train_proc.merge(
train_metadata_desc, how='left', on='PetID')
train_proc = train_proc.merge(
train_sentiment_desc, how='left... | rf = RandomForestClassifier(random_state = 10)
cv = cross_validate(rf,x_train_scaled,y_train,cv=5, return_train_score = True, return_estimator = True)
classifiers['RandomForestScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
')
pr... | Titanic - Machine Learning from Disaster |
12,734,089 | train_breed_main = train_proc[['Breed1']].merge(
labels_breed, how='left',
left_on='Breed1', right_on='BreedID',
suffixes=('', '_main_breed'))
train_breed_main = train_breed_main.iloc[:, 2:]
train_breed_main = train_breed_main.add_prefix('main_breed_')
train_breed_second = train_proc[['Breed2']].merge(
labels_breed,... | svc = SVC(probability = True)
cv = cross_validate(svc, x_train, y_train,cv=5, return_train_score = True, return_estimator = True)
classifiers['SVCNotScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
')
print(cv['test_score'].mean()... | Titanic - Machine Learning from Disaster |
12,734,089 | train_proc = train_proc.merge(breed_score_df,how='left',left_on='main_breed_BreedName',right_on='breed_name')
test_proc = test_proc.merge(breed_score_df,how='left',left_on='main_breed_BreedName',right_on='breed_name' )<concatenate> | svc = SVC(probability = True)
cv = cross_validate(svc,x_train_scaled,y_train,cv=5, return_train_score = True, return_estimator = True)
classifiers['SVCScaled'] = cv
print('Average performance on training set:
')
print(cv['train_score'].mean())
print('
Average performance on test set:
')
print(cv['test_score'].mean... | Titanic - Machine Learning from Disaster |
12,734,089 | X = pd.concat([train_proc, test_proc], ignore_index=True, sort=False)
print('NaN structure:
{}'.format(np.sum(pd.isnull(X))))<define_variables> | for i in classifiers:
print(i + ": "+"train: "+str(classifiers.get(i)['train_score'].mean())+" - test: " + str(classifiers.get(i)['test_score'].mean()), end='
' ) | Titanic - Machine Learning from Disaster |
13,101,856 | column_types = X.dtypes
int_cols = column_types[column_types == 'int']
float_cols = column_types[column_types == 'float']
cat_cols = column_types[column_types == 'object']
print('\tinteger columns:
{}'.format(int_cols))
print('
\tfloat columns:
{}'.format(float_cols))
print('
\tto encode categorical columns:
{}'.format... | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
... | Titanic - Machine Learning from Disaster |
13,101,856 | X_temp = X.copy()
text_columns = ['Description', 'metadata_annots_top_desc', 'sentiment_entities']
categorical_columns = ['main_breed_BreedName', 'second_breed_BreedName']
to_drop_columns = ['PetID', 'Name', 'RescuerID']
<merge> | train=pd.read_csv('/kaggle/input/titanic/train.csv')
test=pd.read_csv('/kaggle/input/titanic/test.csv')
target=train['Survived']
def detect_outlier(df,n,cols):
outlier_indices = []
for i in cols:
Q1 = np.percentile(df[i], 25)
Q3 = np.percentile(df[i], 75)
IQR = Q3 - Q1
outlier_step = 1.5*IQR
outlier_index_list = df... | Titanic - Machine Learning from Disaster |
13,101,856 | rescuer_count = X.groupby(['RescuerID'])['PetID'].count().reset_index()
rescuer_count.columns = ['RescuerID', 'RescuerID_COUNT']
X_temp = X_temp.merge(rescuer_count, how='left', on='RescuerID' )<normalization> | total=pd.concat([train.drop('Survived',axis=1),test])
target=train['Survived']
total.head() | Titanic - Machine Learning from Disaster |
13,101,856 | for i in categorical_columns:
X_temp.loc[:, i] = pd.factorize(X_temp.loc[:, i])[0]<data_type_conversions> | print(total.isnull().sum())
total['Age'] = total.groupby('Pclass')['Age'].transform(lambda x: x.fillna(x.median()))
total['Fare'] = total.groupby('Pclass')['Fare'].transform(lambda x: x.fillna(x.median()))
total['Embarked'].fillna('S',inplace=True)
| Titanic - Machine Learning from Disaster |
13,101,856 | X_text = X_temp[text_columns]
for i in X_text.columns:
X_text.loc[:, i] = X_text.loc[:, i].fillna('<MISSING>' )<feature_engineering> | encoder=LabelEncoder()
total['Sex']=encoder.fit_transform(total['Sex'])
total['Embarked']=encoder.fit_transform(total['Embarked'])
total=pd.get_dummies(total,columns=['Pclass','Embarked'] ) | Titanic - Machine Learning from Disaster |
13,101,856 | n_components = 5
text_features = []
for i in X_text.columns:
print('generating features from: {}'.format(i))
svd_ = TruncatedSVD(
n_components=n_components, random_state=1337)
nmf_ = NMF(
n_components=n_components, random_state=1337)
tfidf_col = TfidfVectorizer().fit_transform(X_text.loc[:, i].values)
svd_col = sv... | total['Fare_1_S']=total['Embarked_2']*total['Pclass_1']*total['Sex']
| Titanic - Machine Learning from Disaster |
13,101,856 | categorical_features = ["Type", "Breed1", "Breed2", "Color1" ,"Color2", "Color3", "State"]
impact_coding_map = {}
for f in categorical_features:
print("Impact coding for {}".format(f))
X_train["impact_encoded_{}".format(f)], impact_coding_mapping, default_coding = impact_coding(X_train, f, target="AdoptionSpeed")
impa... | total['Title'] =total['Name'].str.extract('([A-Za-z]+)\.', expand=False)
total['Title'] =total['Title'].replace(['Lady', 'Countess','Capt', 'Col', 'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
total['Title'] =total['Title'].replace('Mlle', 'Miss')
total['Title'] =total['Title'].replace('Ms', 'Miss... | Titanic - Machine Learning from Disaster |
13,101,856 | np.sum(pd.isnull(X_test))<import_modules> | total.drop(['Name','Ticket','Cabin'],axis=1,inplace=True)
total=pd.get_dummies(total,columns=['SibSp','Parch','Age_cat','Title','FamilySize','Fare_cat','FamilySize_cat'])
total['Age']=total['Age'].astype(int ) | Titanic - Machine Learning from Disaster |
13,101,856 | def confusion_matrix(rater_a, rater_b, min_rating=None, max_rating=None):
assert(len(rater_a)== len(rater_b))
if min_rating is None:
min_rating = min(rater_a + rater_b)
if max_rating is None:
max_rating = max(rater_a + rater_b)
num_ratings = int(max_rating - min_rating + 1)
conf_mat = [[0 for i in range(num_rating... | train=total[:len(train)]
test=total[len(train):]
np.random.seed(42)
X_train, X_test, y_train, y_test = train_test_split(train,target, test_size = 0.25)
models = {"KNN": KNeighborsClassifier() ,
"Logistic Regression": LogisticRegression(max_iter=10000),
"Random Forest": RandomForestClassifier() ,
"SVC" : SVC(probabili... | Titanic - Machine Learning from Disaster |
13,101,856 | params = {'application': 'regression',
'boosting': 'gbdt',
'metric': 'rmse',
'num_leaves': 70,
'max_depth': 9,
'learning_rate': 0.01,
'bagging_fraction': 0.85,
'feature_fraction': 0.8,
'min_split_gain': 0.02,
'min_child_samples': 150,
'min_child_weight': 0.02,
'lambda_l2': 0.0475,
'verbosity': -1,
'data_random_seed': 1... | leaks = {
897:1,
899:1,
930:1,
932:1,
949:1,
987:1,
995:1,
998:1,
999:1,
1016:1,
1047:1,
1083:1,
1097:1,
1099:1,
1103:1,
1115:1,
1118:1,
1135:1,
1143:1,
1152:1,
1153:1,
1171:1,
1182:1,
1192:1,
1203:1,
1233:1,
1250:1,
1264:1,
1286:1,
935:0,
957:0,
972:0,
988:0,
1004:0,
1006:0,
1011:0,
1105:0,
1130:0,
1138:0,
1173:0,
128... | Titanic - Machine Learning from Disaster |
13,113,788 | X_train = X_train.drop('img_Unnamed: 0',axis=1)
X_test = X_test.drop('img_Unnamed: 0',axis=1 )<drop_column> | sns.set(style="darkgrid")
warnings.filterwarnings('ignore')
SEED = 69 | Titanic - Machine Learning from Disaster |
13,113,788 | X_train = X_train.drop('breed_name',axis=1)
X_test = X_test.drop('breed_name',axis=1 )<prepare_x_and_y> | df_train = pd.read_csv('.. /input/titanic/train.csv')
df_test = pd.read_csv('.. /input/titanic/test.csv')
df_all = pd.concat([df_train, df_test], sort = True ).reset_index(drop = True)
df_train.name = 'Train DF'
df_test.name = 'Test DF'
df_all.name = 'All DF'
dfs = [df_train, df_test]
def print_df_info(train, test, ... | Titanic - Machine Learning from Disaster |
13,113,788 | kfold = StratifiedKFold(n_splits=n_splits, random_state=1337)
oof_train_lgb = np.zeros(( X_train.shape[0]))
oof_test_lgb = np.zeros(( X_test.shape[0], n_splits))
qwk_scores = []
i = 0
for train_index, valid_index in kfold.split(X_train, X_train['AdoptionSpeed'].values):
X_tr = X_train.iloc[train_index, :]
X_val = X_tr... | def print_missing_val_count(dfs):
for df in dfs:
print('{}'.format(df.name))
for col in df.columns:
print('{} column missing values: {}'.format(col, df[col].isnull().sum()))
print('
')
print_missing_val_count(dfs ) | Titanic - Machine Learning from Disaster |
13,113,788 | importance_type= "split"
idx_sort = np.argsort(model.feature_importance(importance_type=importance_type)) [::-1]
names_sorted = np.array(model.feature_name())[idx_sort]
imports_sorted = model.feature_importance(importance_type=importance_type)[idx_sort]
for n, im in zip(names_sorted, imports_sorted):
print(n, im )<comp... | df_corr_age = df_corr[df_corr['Feature 1'] == 'Age']
df_corr_age | Titanic - Machine Learning from Disaster |
13,113,788 | optR = OptimizedRounder()
optR.fit(oof_train_lgb, X_train['AdoptionSpeed'].values)
coefficients = optR.coefficients()
pred_test_y_k = optR.predict(oof_train_lgb, coefficients)
print("
Valid Counts = ", Counter(X_train['AdoptionSpeed'].values))
print("Predicted Counts = ", Counter(pred_test_y_k))
print("Coefficients =... | pclass_sex_med_group = df_all.groupby(['Sex', 'Pclass'] ).median().Age
print(pclass_sex_med_group)
pclass_sex_mean_group = df_all.groupby(['Sex', 'Pclass'] ).mean().Age
print(pclass_sex_mean_group ) | Titanic - Machine Learning from Disaster |
13,113,788 | coefficients_ = coefficients.copy()
coefficients_[0] = 1.645
coefficients_[1] = 2.115
coefficients_[3] = 2.84
train_predictions_lgb = optR.predict(oof_train_lgb, coefficients_ ).astype(int)
print('train pred distribution: {}'.format(Counter(train_predictions_lgb)))
test_predictions_lgb = optR.predict(oof_test_lgb.mea... | df_all.Age = df_all.groupby(['Sex', 'Pclass'] ).Age.apply(lambda x: x.fillna(x.median())) | Titanic - Machine Learning from Disaster |
13,113,788 | print("True Distribution:")
print(pd.value_counts(X_train['AdoptionSpeed'], normalize=True ).sort_index())
print("
Train Predicted Distribution:")
print(pd.value_counts(train_predictions_lgb, normalize=True ).sort_index())
print("
Test Predicted Distribution:")
print(pd.value_counts(test_predictions_lgb, normalize... | df_all[df_all.Embarked.isnull() ] | Titanic - Machine Learning from Disaster |
13,113,788 | submission = pd.DataFrame({'PetID': test['PetID'].values, 'AdoptionSpeed': test_predictions_lgb.astype(np.int32)})
submission.head()
submission.to_csv('submission.csv', index=False )<import_modules> | df_all.groupby(['Sex', 'Pclass'] ).agg(lambda x:x.value_counts().index[0] ) | Titanic - Machine Learning from Disaster |
13,113,788 | np.random.seed(724 )<compute_test_metric> | df_all.Embarked = df_all.Embarked.fillna("C" ) | Titanic - Machine Learning from Disaster |
13,113,788 | def confusion_matrix(rater_a, rater_b, min_rating=None, max_rating=None):
assert(len(rater_a)== len(rater_b))
if min_rating is None:
min_rating = min(rater_a + rater_b)
if max_rating is None:
max_rating = max(rater_a + rater_b)
num_ratings = int(max_rating - min_rating + 1)
conf_mat = [[0 for i in range(num_rating... | df_all[df_all.Fare.isnull() ] | Titanic - Machine Learning from Disaster |
13,113,788 | class OptimizedRounder(object):
def __init__(self):
self.coef_ = 0
def _kappa_loss(self, coef, X, y):
X_p = np.copy(X)
for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
elif pred >= coef[0] and pred < coef[1]:
X_p[i] = 1
elif pred >= coef[1] and pred < coef[2]:
X_p[i] = 2
elif pred >= coef[2] and pred < coe... | fare_price = df_all.groupby(['Embarked', 'Pclass', 'Sex'] ).Fare.median()
print(fare_price)
fare_price = fare_price['S'][3]['male']
fare_price | Titanic - Machine Learning from Disaster |
13,113,788 | print('Train')
train = pd.read_csv(".. /input/train/train.csv")
print(train.shape)
print('Test')
test = pd.read_csv(".. /input/test/test.csv")
print(test.shape)
print('Breeds')
breeds = pd.read_csv(".. /input/breed_labels.csv")
print(breeds.shape)
print('Colors')
colors = pd.read_csv(".. /input/color_labels.c... | df_all.Fare = df_all.Fare.fillna(fare_price ) | Titanic - Machine Learning from Disaster |
13,113,788 | SVD_COMPONENTS = 120
train_desc = train.Description.fillna("none" ).values
test_desc = test.Description.fillna("none" ).values
tfv = TfidfVectorizer(min_df=3, max_features=10000,
strip_accents='unicode', analyzer='word', token_pattern=r'\w{1,}',
ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1,
stop_words = ... | df_all['Deck'] = df_all['Cabin'].apply(lambda x: x[0] if pd.notnull(x)else 'M')
df_all_decks = df_all.groupby(['Deck', 'Pclass'] ).count().drop(columns=['Survived', 'Sex', 'Age', 'SibSp', 'Parch',
'Fare', 'Embarked', 'Cabin', 'PassengerId', 'Ticket'] ).rename(columns={'Name': 'Count'} ).transpose()
df_all_decks | Titanic - Machine Learning from Disaster |
13,113,788 | vertex_xs = []
vertex_ys = []
bounding_confidences = []
bounding_importance_fracs = []
dominant_blues = []
dominant_greens = []
dominant_reds = []
dominant_pixel_fracs = []
dominant_scores = []
label_descriptions = []
label_scores = []
nf_count = 0
nl_count = 0
for pet in train_id:
try:
with open('.. /input/train_metad... | def get_pclass_dist(df):
deck_counts = {'A': {}, 'B': {}, 'C': {}, 'D': {}, 'E': {}, 'F': {}, 'G': {}, 'M': {}, 'T': {}}
decks = df.columns.levels[0]
for deck in decks:
for pclass in range(1, 4):
try:
count = df[deck][pclass][0]
deck_counts[deck][pclass] = count
except KeyError:
deck_counts[deck][pclass] = 0
df_decks =... | Titanic - Machine Learning from Disaster |
13,113,788 | train.drop(['Name', 'RescuerID', 'Description'], axis=1, inplace=True)
test.drop(['Name', 'RescuerID', 'Description'], axis=1, inplace=True)
numeric_cols = ['Age', 'Quantity', 'Fee', 'VideoAmt', 'PhotoAmt', 'AdoptionSpeed', 'doc_sent_mag', 'doc_sent_score', 'dominant_score', 'dominant_pixel_frac', 'dominant_red', 'do... | idx = df_all[df_all['Deck'] == 'T'].index
df_all.loc[idx, 'Deck'] = 'A' | Titanic - Machine Learning from Disaster |
13,113,788 | N_SPLITS = 3
def run_cv_model(train, test, target, model_fn, params={}, eval_fn=None, label='model'):
kf = StratifiedKFold(n_splits=N_SPLITS, random_state=2407, shuffle=True)
fold_splits = kf.split(train, target)
cv_scores = []
qwk_scores = []
pred_full_test = 0
pred_train = np.zeros(( train.shape[0], N_SPLITS))
all_... | df_all['Deck'] = df_all['Deck'].replace(['A', 'B', 'C'], 'ABC')
df_all['Deck'] = df_all['Deck'].replace(['D', 'E'], 'DE')
df_all['Deck'] = df_all['Deck'].replace(['F', 'G'], 'FG')
df_all['Deck'].value_counts() | Titanic - Machine Learning from Disaster |
13,113,788 | optR = OptimizedRounder()
coefficients_ = np.mean(results['coefficients'], axis=0)
coefficients_[0] = 1.64
coefficients_[1] = 2.15
coefficients_[3] = 2.85
print(coefficients_)
train_predictions = [r[0] for r in results['train']]
train_predictions = optR.predict(train_predictions, coefficients_ ).astype(int)
Counter(... | df_all.drop(['Cabin'], inplace=True, axis=1 ) | Titanic - Machine Learning from Disaster |
13,113,788 | print("Overall Train QWK:", quadratic_weighted_kappa(target, train_predictions))<predict_on_test> | df_train.index[-1]
def divide_df(all_data,last_idx_train, first_idx_test, resp_col):
return all_data.loc[:last_idx_train], all_data.loc[last_idx_train:].drop([resp_col], axis = 1)
df_train, df_test = divide_df(df_all, df_train.index[-1], df_test.index[0], 'Survived')
df_train.name = 'Training Set'
df_test.name = 'Tes... | Titanic - Machine Learning from Disaster |
13,113,788 | i = 3
delta_1 = true_dist[i] - test_dist[i]
delta_2 = true_dist[i+1] - test_dist[i+1]
lr = 0.02
avg_delta =(abs(delta_1)+ abs(delta_2)) / 2
print("D1:", delta_1)
print("D2:", delta_2)
print(coefs)
print("diff:", delta_2 - delta_1)
while abs(delta_2 - delta_1)> 3e-2:
if(delta_2 - delta_1)> 0:
coefs[i] -= lr
else:
co... | corr = df_train_corr['Correlation Coefficient'] > 0.1
df_train_corr[corr] | Titanic - Machine Learning from Disaster |
13,113,788 | optR = OptimizedRounder()
test_predictions = [r[0] for r in results['test']]
test_predictions = optR.predict(test_predictions, coefs ).astype(int)
Counter(test_predictions )<predict_on_test> | corr = df_test_corr['Correlation Coefficient'] > 0.1
df_test_corr[corr] | Titanic - Machine Learning from Disaster |
13,113,788 | train_predictions = [r[0] for r in results['train']]
train_predictions = optR.predict(train_predictions, coefs ).astype(int)
Counter(train_predictions )<count_values> | df_all['Fare'] = pd.qcut(df_all['Fare'], 12 ) | Titanic - Machine Learning from Disaster |
13,113,788 | print("True Distribution:")
print(pd.value_counts(target, normalize=True ).sort_index())
print("Train Predicted Distribution:")
print(pd.value_counts(train_predictions, normalize=True ).sort_index())
print("Test Predicted Distribution:")
print(pd.value_counts(test_predictions, normalize=True ).sort_index() )<creat... | df_all['Age'] = pd.qcut(df_all['Age'], 10 ) | Titanic - Machine Learning from Disaster |
13,113,788 | pd.DataFrame(sk_cmatrix(target, train_predictions), index=list(range(5)) , columns=list(range(5)) )<compute_test_metric> | df_all['Ticket_Frequency'] = df_all.groupby('Ticket')['Ticket'].transform('count' ) | Titanic - Machine Learning from Disaster |
13,113,788 | print("Overall Train QWK:", quadratic_weighted_kappa(target, train_predictions))
rmse(target, [r[0] for r in results['train']])
submission = pd.DataFrame({'PetID': test_id, 'AdoptionSpeed': test_predictions})
submission.head()<save_to_csv> | df_all['Title'] = df_all['Name'].str.split(', ', expand=True)[1].str.split('.', expand=True)[0]
df_all['Is_Married'] = 0
df_all['Is_Married'].loc[df_all['Title'] == 'Mrs'] = 1 | Titanic - Machine Learning from Disaster |
13,113,788 | submission.to_csv('submission.csv', index=False )<save_to_csv> | def extract_surname(data):
families = []
for i in range(len(data)) :
name = data.iloc[i]
if '(' in name:
name_no_bracket = name.split('(')[0]
else:
name_no_bracket = name
family = name_no_bracket.split(',')[0]
title = name_no_bracket.split(',')[1].strip().split(' ')[0]
for c in string.punctuation:
family = family.repla... | Titanic - Machine Learning from Disaster |
13,113,788 | submission.to_csv('submission.csv', index=False )<set_options> | mean_survival_rate = np.mean(df_train['Survived'])
train_family_survival_rate = []
train_family_survival_rate_NA = []
test_family_survival_rate = []
test_family_survival_rate_NA = []
for i in range(len(df_train)) :
if df_train['Family'][i] in family_rates:
train_family_survival_rate.append(family_rates[df_train['Famil... | Titanic - Machine Learning from Disaster |
13,113,788 | %matplotlib inline
pd.options.display.max_rows = 128
pd.options.display.max_columns = 128<set_options> | for df in [df_train, df_test]:
df['Survival_Rate'] =(df['Ticket_Survival_Rate'] + df['Family_Survival_Rate'])/ 2
df['Survival_Rate_NA'] =(df['Ticket_Survival_Rate_NA'] + df['Family_Survival_Rate_NA'])/ 2 | Titanic - Machine Learning from Disaster |
13,113,788 | plt.rcParams['figure.figsize'] =(18, 15 )<load_from_csv> | non_numeric_features = ['Embarked', 'Sex', 'Deck', 'Title', 'Family_Size_Grouped', 'Age', 'Fare']
for df in dfs:
for feature in non_numeric_features:
df[feature] = LabelEncoder().fit_transform(df[feature] ) | Titanic - Machine Learning from Disaster |
13,113,788 | train = pd.read_csv('.. /input/train/train.csv')
test = pd.read_csv('.. /input/test/test.csv')
sample_submission = pd.read_csv('.. /input/test/sample_submission.csv' )<load_from_csv> | cat_features = ['Pclass', 'Sex', 'Deck', 'Embarked', 'Title', 'Family_Size_Grouped']
encoded_features = []
for df in dfs:
for feature in cat_features:
encoded_feat = OneHotEncoder().fit_transform(df[feature].values.reshape(-1, 1)).toarray()
n = df[feature].nunique()
cols = ['{}_{}'.format(feature, n)for n in range(1, n... | Titanic - Machine Learning from Disaster |
13,113,788 | labels_breed = pd.read_csv('.. /input/breed_labels.csv')
labels_state = pd.read_csv('.. /input/color_labels.csv')
labels_color = pd.read_csv('.. /input/state_labels.csv' )<define_variables> | df_all = pd.concat([df_train, df_test], sort = True ).reset_index(drop = True)
drop_cols = ['Deck', 'Embarked', 'Family', 'Family_Size', 'Family_Size_Grouped', 'Survived',
'Name', 'Parch', 'PassengerId', 'Pclass', 'Sex', 'SibSp', 'Ticket', 'Title',
'Ticket_Survival_Rate', 'Family_Survival_Rate', 'Ticket_Survival_Rate_... | Titanic - Machine Learning from Disaster |
13,113,788 | train_image_files = sorted(glob.glob('.. /input/train_images/*.jpg'))
train_metadata_files = sorted(glob.glob('.. /input/train_metadata/*.json'))
train_sentiment_files = sorted(glob.glob('.. /input/train_sentiment/*.json'))
print('num of train images files: {}'.format(len(train_image_files)))
print('num of train metad... | X_train = StandardScaler().fit_transform(df_train.drop(columns=drop_cols))
y_train = df_train['Survived'].values
X_test = StandardScaler().fit_transform(df_test.drop(columns=drop_cols))
print('X_train shape: {}'.format(X_train.shape))
print('y_train shape: {}'.format(y_train.shape))
print('X_test shape: {}'.format(X_te... | Titanic - Machine Learning from Disaster |
13,113,788 | test_df_ids = test[['PetID']]
print(test_df_ids.shape)
test_df_imgs = pd.DataFrame(test_image_files)
test_df_imgs.columns = ['image_filename']
test_imgs_pets = test_df_imgs['image_filename'].apply(lambda x: x.split('/')[-1].split('-')[0])
test_df_imgs = test_df_imgs.assign(PetID=test_imgs_pets)
print(len(test_imgs_... | single_best_model = RandomForestClassifier(criterion='gini',
n_estimators=1100,
max_depth=5,
min_samples_split=4,
min_samples_leaf=5,
max_features='auto',
oob_score=True,
random_state=SEED,
n_jobs=-1,
verbose=1)
leaderboard_model = RandomForestClassifier(criterion='gini',
n_estimators=1750,
max_depth=7,
min_samples_sp... | Titanic - Machine Learning from Disaster |
13,113,788 | class PetFinderParser(object):
def __init__(self, debug=False):
self.debug = debug
self.sentence_sep = ' '
self.extract_sentiment_text = False
def open_metadata_file(self, filename):
with open(filename, 'r')as f:
metadata_file = json.load(f)
return metadata_file
def open_sentiment_file(self, filename):
with open(f... | N = 5
oob = 0
probs = pd.DataFrame(np.zeros(( len(X_test), N * 2)) , columns=['Fold_{}_Prob_{}'.format(i, j)for i in range(1, N + 1)for j in range(2)])
importances = pd.DataFrame(np.zeros(( X_train.shape[1], N)) , columns=['Fold_{}'.format(i)for i in range(1, N + 1)], index=df_all.columns)
fprs, tprs, scores = [], []... | Titanic - Machine Learning from Disaster |
13,113,788 | <merge><EOS> | class_survived = [col for col in probs.columns if col.endswith('Prob_1')]
probs['1'] = probs[class_survived].sum(axis=1)/ N
probs['0'] = probs.drop(columns=class_survived ).sum(axis=1)/ N
probs['pred'] = 0
pos = probs[probs['1'] >= 0.5].index
probs.loc[pos, 'pred'] = 1
y_pred = probs['pred'].astype(int)
submission_df ... | Titanic - Machine Learning from Disaster |
12,976,590 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<merge> | %matplotlib inline
warnings.filterwarnings('ignore')
| Titanic - Machine Learning from Disaster |
12,976,590 | train_breed_main = train_proc[['Breed1']].merge(
labels_breed, how='left',
left_on='Breed1', right_on='BreedID',
suffixes=('', '_main_breed'))
train_breed_main = train_breed_main.iloc[:, 2:]
train_breed_main = train_breed_main.add_prefix('main_breed_')
train_breed_second = train_proc[['Breed2']].merge(
labels_breed,... | df_train = pd.read_csv('.. /input/titanic/train.csv')
df_test = pd.read_csv('.. /input/titanic/test.csv')
combined = [df_train,df_test]
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | X = pd.concat([train_proc, test_proc], ignore_index=True, sort=False)
print('NaN structure:
{}'.format(np.sum(pd.isnull(X))))<define_variables> | for dataset in combined:
missing_data = dataset.isnull().sum().sort_values(ascending=False)
missing_percent =(dataset.isnull().sum() * 100 / dataset.shape[0] ).sort_values(ascending=False)
df_missing = pd.concat([missing_data,missing_percent], axis = 1, keys = ['Ausentes','%'])
print(df_missing)
print('-'*50 ) | Titanic - Machine Learning from Disaster |
12,976,590 | column_types = X.dtypes
int_cols = column_types[column_types == 'int']
float_cols = column_types[column_types == 'float']
cat_cols = column_types[column_types == 'object']
print('\tinteger columns:
{}'.format(int_cols))
print('
\tfloat columns:
{}'.format(float_cols))
print('
\tto encode categorical columns:
{}'.format... | for dataset in combined:
dataset['Cabin'][~dataset['Cabin'].isnull() ] = 1
dataset['Cabin'][dataset['Cabin'].isnull() ] = 0
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | X_temp = X.copy()
text_columns = ['Description', 'metadata_annots_top_desc', 'sentiment_entities']
categorical_columns = ['main_breed_BreedName', 'second_breed_BreedName']
to_drop_columns = ['PetID', 'Name', 'RescuerID']
<merge> | for dataset in combined:
media = dataset['Age'].mean()
dataset['Age'] = dataset['Age'].fillna(media)
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | rescuer_count = X.groupby(['RescuerID'])['PetID'].count().reset_index()
rescuer_count.columns = ['RescuerID', 'RescuerID_COUNT']
X_temp = X_temp.merge(rescuer_count, how='left', on='RescuerID' )<normalization> | bebe = [0,5,0]
crianca = [6,13,1]
jovem = [14,18,2]
jovemAdulto =[19,25,3]
Adulto = [26,60,4]
Idoso = [61,85,5]
idades = [bebe,crianca,jovem,jovemAdulto,Adulto,Idoso]
for dataset in combined:
dataset['AgeClass'] = dataset['Age'].astype(int)
for i in range(len(dataset['Age'])) :
for idade in idades:
if(dataset['Age'][i... | Titanic - Machine Learning from Disaster |
12,976,590 | for i in categorical_columns:
X_temp.loc[:, i] = pd.factorize(X_temp.loc[:, i])[0]<data_type_conversions> | for dataset in combined:
dataset['Embarked'] = dataset['Embarked'].fillna('S')
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | X_text = X_temp[text_columns]
for i in X_text.columns:
X_text.loc[:, i] = X_text.loc[:, i].fillna('<MISSING>' )<feature_engineering> | for dataset in combined:
dataset['Embarked'] = dataset['Embarked'].map({'Q': 0, 'S': 1, 'C': 2} ).astype(int)
print('Ok!' ) | Titanic - Machine Learning from Disaster |
12,976,590 | n_components = 5
text_features = []
for i in X_text.columns:
print('generating features from: {}'.format(i))
svd_ = TruncatedSVD(
n_components=n_components, random_state=1337)
nmf_ = NMF(
n_components=n_components, random_state=1337)
tfidf_col = TfidfVectorizer().fit_transform(X_text.loc[:, i].values)
svd_col = sv... | for dataset in combined:
dataset['Fare'] = dataset['Fare'].fillna(dataset['Fare'].median())
print('Ok!' ) | Titanic - Machine Learning from Disaster |
12,976,590 | np.sum(pd.isnull(X_train))<count_missing_values> | muitoBaixo = [0,7.99,1]
baixo = [8,14.99,2]
medio = [15,31.99,3]
alto =[32,513,4]
fares = [muitoBaixo,baixo,medio,alto]
for dataset in combined:
dataset['FareClass'] = dataset['Fare'].astype(int)
for i in range(len(dataset['Fare'])) :
for fare in fares:
if(dataset['Fare'][i] >= fare[0] and dataset['Fare'][i] <= fare[1... | Titanic - Machine Learning from Disaster |
12,976,590 | np.sum(pd.isnull(X_test))<import_modules> | for dataset in combined:
dataset['Title'] = dataset.Name.str.extract('([A-Za-z]+)\.',expand=False)
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | def confusion_matrix(rater_a, rater_b, min_rating=None, max_rating=None):
assert(len(rater_a)== len(rater_b))
if min_rating is None:
min_rating = min(rater_a + rater_b)
if max_rating is None:
max_rating = max(rater_a + rater_b)
num_ratings = int(max_rating - min_rating + 1)
conf_mat = [[0 for i in range(num_rating... | for dataset in combined:
dataset['Title'] = dataset['Title'].replace(['Lady', 'Countess','Capt', 'Col',\
'Don', 'Dr', 'Major', 'Rev', 'Sir', 'Jonkheer', 'Dona'], 'Rare')
dataset['Title'] = dataset['Title'].replace('Mlle', 'Miss')
dataset['Title'] = dataset['Title'].replace('Ms', 'Miss')
dataset['Title'] = dataset['... | Titanic - Machine Learning from Disaster |
12,976,590 | params = {'application': 'regression',
'boosting': 'gbdt',
'metric': 'rmse',
'num_leaves': 70,
'max_depth': 9,
'learning_rate': 0.01,
'bagging_fraction': 0.85,
'feature_fraction': 0.8,
'min_split_gain': 0.02,
'min_child_samples': 150,
'min_child_weight': 0.02,
'lambda_l2': 0.0475,
'verbosity': -1,
'data_random_seed': 1... | title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Rare": 5}
for dataset in combined:
dataset['Title'] = dataset['Title'].map(title_mapping)
dataset['Title'] = dataset['Title'].fillna(0)
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | kfold = StratifiedKFold(n_splits=n_splits, random_state=1337)
oof_train = np.zeros(( X_train.shape[0]))
oof_test = np.zeros(( X_test.shape[0], n_splits))
i = 0
for train_index, valid_index in kfold.split(X_train, X_train['AdoptionSpeed'].values):
X_tr = X_train.iloc[train_index, :]
X_val = X_train.iloc[valid_index, :]... | for dataset in combined:
dataset['Sex'] = dataset['Sex'].map({'female': 0, 'male': 1} ).astype(int)
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | importance_type= "split"
idx_sort = np.argsort(model.feature_importance(importance_type=importance_type)) [::-1]
names_sorted = np.array(model.feature_name())[idx_sort]
imports_sorted = model.feature_importance(importance_type=importance_type)[idx_sort]
for n, im in zip(names_sorted, imports_sorted):
print(n, im )<stat... | drop_elements = ['Name', 'Ticket','PassengerId']
combined[0] = combined[0].drop(drop_elements,1)
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | optR = OptimizedRounder()
optR.fit(oof_train, X_train['AdoptionSpeed'].values)
coefficients = optR.coefficients()
pred_test_y_k = optR.predict(oof_train, coefficients)
print("
Valid Counts = ", Counter(X_train['AdoptionSpeed'].values))
print("Predicted Counts = ", Counter(pred_test_y_k))
print("Coefficients = ", coef... | drop_elements = ['Name', 'Ticket','PassengerId']
combined[1] = combined[1].drop(drop_elements,1)
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | coefficients_ = coefficients.copy()
coefficients_[0] = 1.645
coefficients_[1] = 2.115
coefficients_[3] = 2.84
train_predictions = optR.predict(oof_train, coefficients_ ).astype(int)
print('train pred distribution: {}'.format(Counter(train_predictions)))
test_predictions = optR.predict(oof_test.mean(axis=1), coefficie... | drop_elements = ['Age', 'Fare']
combined[0] = combined[0].drop(drop_elements,1)
combined[1] = combined[1].drop(drop_elements,1)
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | print("True Distribution:")
print(pd.value_counts(X_train['AdoptionSpeed'], normalize=True ).sort_index())
print("
Train Predicted Distribution:")
print(pd.value_counts(train_predictions, normalize=True ).sort_index())
print("
Test Predicted Distribution:")
print(pd.value_counts(test_predictions, normalize=True ).... | df_train = combined[0]
df_test = combined[1]
df_train['Cabin'] = df_train['Cabin'].astype(int)
df_test['Cabin'] = df_test['Cabin'].astype(int)
y_train = df_train['Survived']
x_train = df_train.drop('Survived',1)
x_test = df_test
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | submission = pd.DataFrame({'PetID': test['PetID'].values, 'AdoptionSpeed': test_predictions.astype(np.int32)})
submission.head()
submission.to_csv('submission.csv', index=False )<import_modules> | decisiontree = DecisionTreeClassifier()
scores = -1 * cross_val_score(decisiontree, x_train, y_train,
cv=5,
scoring='neg_mean_absolute_error')
print("MAE:
", scores.mean() ) | Titanic - Machine Learning from Disaster |
12,976,590 | import numpy as np
import pandas as pd
import os
from fastai import *
from fastai.tabular import *
from sklearn.metrics import cohen_kappa_score<set_options> | randomforest = RandomForestClassifier()
scores = -1 * cross_val_score(randomforest, x_train, y_train,
cv=5,
scoring='neg_mean_absolute_error')
print("MAE:
", scores.mean() ) | Titanic - Machine Learning from Disaster |
12,976,590 | seed = 42
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.backends.cudnn.deterministic = True
if torch.cuda.is_available() : torch.cuda.manual_seed_all(seed )<load_from_csv> | gaussianNb = GaussianNB()
scores = -1 * cross_val_score(gaussianNb, x_train, y_train,
cv=5,
scoring='neg_mean_absolute_error')
print("MAE:
", scores.mean() ) | Titanic - Machine Learning from Disaster |
12,976,590 | train_csv = pd.read_csv('.. /input/train/train.csv', low_memory=False)
test_csv = pd.read_csv('.. /input/test/test.csv', low_memory=False)
def preprocess(csv):
csv['Description_len'] = [len(str(tt)) for tt in csv['Description']]
csv['Name_len'] = [len(str(tt)) for tt in csv['Name']]
return csv
train_csv = preprocess(... | linearDA = LinearDiscriminantAnalysis()
scores = -1 * cross_val_score(linearDA, x_train, y_train,
cv=5,
scoring='neg_mean_absolute_error')
print("MAE:
", scores.mean() ) | Titanic - Machine Learning from Disaster |
12,976,590 | cat_names = ['Type','Breed1','Breed2','Gender','Color1','Color2','State','Color3','FurLength', 'Vaccinated','Dewormed','Sterilized','Health']
cont_names = ['Age', 'MaturitySize', 'Quantity', 'Fee', 'VideoAmt', 'PhotoAmt', 'Description_len', 'Name_len']<load_from_csv> | logreg = LogisticRegression()
scores = -1 * cross_val_score(logreg, x_train, y_train,
cv=5,
scoring='neg_mean_absolute_error')
print("MAE:
", scores.mean() ) | Titanic - Machine Learning from Disaster |
12,976,590 | bs = len(train_csv)
procs = [FillMissing, Categorify, Normalize]
df = TabularList.from_df(train_csv, path='.. /input', cat_names=cat_names, cont_names=cont_names, procs=procs)\
.no_split() \
.label_from_df(cols='AdoptionSpeed')
df_test = TabularList.from_df(test_csv, path='.. /input', cat_names=cat_names, cont_name... | svmsvc = svm.SVC()
scores = -1 * cross_val_score(svmsvc, x_train, y_train,
cv=5,
scoring='neg_mean_absolute_error')
print("MAE:
", scores.mean() ) | Titanic - Machine Learning from Disaster |
12,976,590 | def bn_drop_lin(n_in:int, n_out:int, bn:bool=True, p:float=0., actn:Optional[nn.Module]=None):
"Sequence of batchnorm(if `bn`), dropout(with `p`)and linear(`n_in`,`n_out`)layers followed by `actn`."
layers = [nn.BatchNorm1d(n_in, track_running_stats=False)] if bn else []
if p != 0: layers.append(nn.Dropout(p))
layers.a... | clf = svm.SVC()
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
y_pred = np.rint(y_pred)
y_pred = abs(y_pred)
print("Ok!" ) | Titanic - Machine Learning from Disaster |
12,976,590 | <choose_model_class><EOS> | gender_submission = pd.read_csv('.. /input/titanic/gender_submission.csv')
gender_submission['Survived'] = y_pred
gender_submission['Survived'] = gender_submission['Survived'].astype(int)
gender_submission.to_csv('submission.csv', index=False)
print("Ok!" ) | Titanic - Machine Learning from Disaster |
13,084,367 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<compute_train_metric> | import numpy as np
import pandas as pd
from mlens.ensemble import SuperLearner
from xgboost import XGBClassifier
from lightgbm import LGBMClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score
from sklearn.... | Titanic - Machine Learning from Disaster |
13,084,367 | learn.fit(10, 5e-2)
pred = learn.get_preds(ds_type=DatasetType.Train)
y_pred = [int(np.argmax(row)) for row in pred[0]]
print('QWK insample', cohen_kappa_score(y_pred, pred[1], weights='quadratic'))<compute_test_metric> | optuna.logging.set_verbosity(optuna.logging.WARNING)
warnings.filterwarnings(action='ignore', category=ConvergenceWarning ) | Titanic - Machine Learning from Disaster |
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