kernel_id int64 24.2k 23.3M | prompt stringlengths 8 1.85M | completetion stringlengths 1 182k | comp_name stringlengths 5 57 |
|---|---|---|---|
13,189,930 | df['Quantity_Bin']=pd.factorize(pd.cut(df.Quantity,bins=[1,2,4,22],right=False)) [0]
quantity_bin_dummies_df = pd.get_dummies(df['Quantity_Bin'] ).rename(columns=lambda x: 'Quantity_Bin_' + str(x))
df = pd.concat([df, quantity_bin_dummies_df], axis=1 )<categorify> | train_df[train_df['Embarked'].isnull() == True] | Titanic - Machine Learning from Disaster |
13,189,930 | df.VideoAmt = df.VideoAmt.apply(lambda x: 1 if x > 0 else 0)
df['PhotoAmt_Bin']=pd.factorize(pd.cut(df.PhotoAmt,bins=[0,1,2,4,31],right=False)) [0]
photo_bin_dummies_df = pd.get_dummies(df['PhotoAmt_Bin'] ).rename(columns=lambda x: 'PhotoAmt_Bin_' + str(x))
df = pd.concat([df, photo_bin_dummies_df], axis=1 )<categorif... | train_df['Embarked'][train_df['Pclass']==1].value_counts() | Titanic - Machine Learning from Disaster |
13,189,930 | def map_state(state):
if state == 41326:
return 'Selangor'
elif state == 41401:
return 'Kuala_Lumpur'
else:
return 'Other_State'
df['State_Bin'] = df.State.apply(map_state)
state_bin_dummies_df = pd.get_dummies(df['State_Bin'] ).rename(columns=lambda x: 'State_' + str(x))
df = pd.concat([df, state_bin_dummies_df], axi... | train_df['Embarked'] = train_df['Embarked'].fillna('C' ) | Titanic - Machine Learning from Disaster |
13,189,930 | rescuer_dict = df.RescuerID.value_counts().to_dict()
df['Rescuer_Num'] = df.RescuerID.map(rescuer_dict)
<load_from_csv> | train_df[train_df['Embarked'].isnull() == True] | Titanic - Machine Learning from Disaster |
13,189,930 | breeds = pd.read_csv('.. /input/breed_labels.csv')
breeds_dict = {k: v for k, v in zip(breeds['BreedID'], breeds['BreedName'])}
df['Breed1_name'] = df['Breed1'].apply(lambda x: '_'.join(breeds_dict[x].split())if x in breeds_dict else 'NA')
df['Breed2_name'] = df['Breed2'].apply(lambda x: '_'.join(breeds_dict[x].split... | train_df[train_df['Fare'].isnull() ==True] | Titanic - Machine Learning from Disaster |
13,189,930 | df['Breed'] = df['Breed1_name'] + '--' + df['Breed2_name']
def mix_breed(string):
breed = string.split('--')
if breed[0] in ['Mixed_Breed','NA']:
return 1
elif breed[1] == 'Mixed_Breed':
return 1
elif breed[1] == 'NA':
return 0
elif breed[0] != breed[1]:
return 1
else:
return 0
df['Mixed_Breed'] = df.Breed.apply(mix_b... | train_df['Fare'] = train_df['Fare'].fillna(train_df['Fare'][train_df.Pclass==3].mean() ) | Titanic - Machine Learning from Disaster |
13,189,930 |
<filter> | train_df[train_df['Fare'].isnull() ==True] | Titanic - Machine Learning from Disaster |
13,189,930 | df_copy = df.drop(columns=['Description','Fee','Fee_per_pet','Name','PhotoAmt','Quantity','RescuerID','State','State_Bin','Fee_Bin','Quantity_Bin','PhotoAmt_Bin','Breed','Breed1_name','Breed2_name'])
train = df_copy[df.AdoptionSpeed.notnull() ]
test = df_copy[df.AdoptionSpeed.isnull() ]
print(train.shape, test.shape )... | train_df[train_df['Age'].isnull() ] | Titanic - Machine Learning from Disaster |
13,189,930 | X_train = train.drop(columns=['AdoptionSpeed'])
y_train = train.AdoptionSpeed
X_test = test.drop(columns=['AdoptionSpeed'] )<train_model> | train_df['Sex']=list(1 if i=='male' else 0 for i in train_df['Sex'] ) | Titanic - Machine Learning from Disaster |
13,189,930 | rf = RandomForestClassifier(n_estimators = 600, max_depth=None, criterion='gini')
rf.fit(X_train,y_train)
y_predict = rf.predict(X_test ).astype(np.int32)
submission = pd.DataFrame({'PetID': test.index, 'AdoptionSpeed': y_predict})
submission = submission[['PetID','AdoptionSpeed']]
submission.head()<save_to_csv> | train_df['Sex']=list('male' if i==1 else 'female' for i in train_df['Sex'] ) | Titanic - Machine Learning from Disaster |
13,189,930 | submission.to_csv('submission.csv', index=False )<save_to_csv> | index_nan_age = list(train_df[train_df['Age'].isnull() ].index ) | Titanic - Machine Learning from Disaster |
13,189,930 | submission.to_csv('submission.csv', index=False )<set_options> | for i in index_nan_age:
age_prediction = train_df['Age'][(( train_df['SibSp']==train_df.iloc[i]['SibSp'])&(train_df['Parch']==train_df.iloc[i]['Parch'])&(train_df['Pclass']==train_df.iloc[i]['Pclass'])) ].median()
age_med = train_df['Age'].median()
if not np.isnan(age_prediction):
train_df['Age'].iloc[i] = age_predicti... | Titanic - Machine Learning from Disaster |
13,189,930 | %matplotlib inline
np.random.seed(seed=1337)
warnings.filterwarnings('ignore')
split_char = '/'<load_from_csv> | train_df[train_df['Age'].isnull() ] | Titanic - Machine Learning from Disaster |
13,189,930 | 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' )<import_modules> | from sklearn.tree import DecisionTreeRegressor | Titanic - Machine Learning from Disaster |
13,189,930 | import cv2
import os
from keras.applications.densenet import preprocess_input, DenseNet121<define_variables> | fare = np.array(train_df.Fare[:train_df_len] ).reshape(-1,1)
survived = np.array(train_df.Survived[:train_df_len] ).reshape(-1,1)
DecTree = DecisionTreeRegressor(max_leaf_nodes=150)
DecTree.fit(fare, survived)
train_df['Fare_class'] = DecTree.apply(np.array(train_df.Fare ).reshape(-1,1)) | Titanic - Machine Learning from Disaster |
13,189,930 | img_size = 256
batch_size = 32<define_search_model> | train_df = pd.get_dummies(data=train_df, columns=['Fare_class'] ) | Titanic - Machine Learning from Disaster |
13,189,930 | inp = Input(( 256,256,3))
backbone = DenseNet121(input_tensor = inp,
weights=".. /input/densenet-keras/DenseNet-BC-121-32-no-top.h5",
include_top = False)
x = backbone.output
x = GlobalAveragePooling2D()(x)
x = Lambda(lambda x: K.expand_dims(x,axis = -1))(x)
x = AveragePooling1D(4 )(x)
out = Lambda(lambda x: x[:,:,... | train_df.drop('Fare', axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
13,189,930 | pet_ids = train['PetID'].values
n_batches = len(pet_ids)// batch_size + 1
features = {}
for b in tqdm(range(n_batches)) :
start = b*batch_size
end =(b+1)*batch_size
batch_pets = pet_ids[start:end]
batch_images = np.zeros(( len(batch_pets),img_size,img_size,3))
for i,pet_id in enumerate(batch_pets):
try:
batch_images[i]... | train_df['Age'] = train_df['Age'].astype('int' ) | Titanic - Machine Learning from Disaster |
13,189,930 | train_feats = pd.DataFrame.from_dict(features, orient='index')
train_feats.columns = [f'pic_{i}' for i in range(train_feats.shape[1])]<define_variables> | train_df['Age_based_survival'] = 0 | Titanic - Machine Learning from Disaster |
13,189,930 | pet_ids = test['PetID'].values
n_batches = len(pet_ids)// batch_size + 1
features = {}
for b in tqdm(range(n_batches)) :
start = b*batch_size
end =(b+1)*batch_size
batch_pets = pet_ids[start:end]
batch_images = np.zeros(( len(batch_pets),img_size,img_size,3))
for i,pet_id in enumerate(batch_pets):
try:
batch_images[i] ... | for i in range(train_df['Age'].max() +1):
df = train_df[train_df['Age']==i][train_df.Survived.isna() == False]
avg_surv_prob = df['Survived'].mean()
train_df.loc[train_df.Age==i,'Age_based_survival'] = avg_surv_prob | Titanic - Machine Learning from Disaster |
13,189,930 | test_feats = pd.DataFrame.from_dict(features, orient='index')
test_feats.columns = [f'pic_{i}' for i in range(test_feats.shape[1])]<rename_columns> | train_df['Age_based_survival'] = train_df['Age_based_survival'].fillna(0)
train_df[train_df['Age_based_survival'].isnull() ] | Titanic - Machine Learning from Disaster |
13,189,930 | train_feats = train_feats.reset_index()
train_feats.rename({'index': 'PetID'}, axis='columns', inplace=True)
test_feats = test_feats.reset_index()
test_feats.rename({'index': 'PetID'}, axis='columns', inplace=True )<concatenate> | train_df.drop('Age', axis=1, inplace=True)
train_df.head() | Titanic - Machine Learning from Disaster |
13,189,930 | all_ids = pd.concat([train, test], axis=0, ignore_index=True, sort=False)[['PetID']]
all_ids.shape<concatenate> | train_df['Title'] = [i.split('.')[0].split(',')[-1].strip() for i in train_df['Name']]
train_df['Title'] | Titanic - Machine Learning from Disaster |
13,189,930 | n_components = 32
svd_ = TruncatedSVD(n_components=n_components, random_state=1337)
features_df = pd.concat([train_feats, test_feats], axis=0)
features = features_df[[f'pic_{i}' for i in range(256)]].values
svd_col = svd_.fit_transform(features)
svd_col = pd.DataFrame(svd_col)
svd_col = svd_col.add_prefix('IMG_SVD_... | train_df['Title'] = train_df['Title'].replace(['Lady','the Countess','Capt','Col','Don','Dr','Major','Rev','Sir','Jonkheer','Dona'],'other' ) | Titanic - Machine Learning from Disaster |
13,189,930 | 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' )<define_variables> | train_df['Title'] = [0 if i=='Master' else 1 if i=='Miss' or i=='Ms' or i=='Mlle' or i=='Mrs' else 2 if i=='Mr' else 3 for i in train_df['Title']] | Titanic - Machine Learning from Disaster |
13,189,930 | 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(... | train_df.drop('Name', axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
13,189,930 | class PetFinderParser(object):
def __init__(self, debug=False):
self.debug = debug
self.sentence_sep = ' '
self.extract_sentiment_text = False
def open_json_file(self, filename):
with open(filename, 'r', encoding='utf-8')as f:
json_file = json.load(f)
return json_file
def parse_sentiment_file(self, file):
file_senti... | train_df = pd.get_dummies(data=train_df, columns=['Title'] ) | Titanic - Machine Learning from Disaster |
13,189,930 | aggregates = ['sum', 'mean', 'var']
sent_agg = ['sum']
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... | train_df['Fsize'] = train_df['SibSp'] + train_df['Parch'] + 1 | Titanic - Machine Learning from Disaster |
13,189,930 | 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... | train_df['Family_size'] = [1 if i in [2,3,4] else 0 for i in train_df['Fsize']] | Titanic - Machine Learning from Disaster |
13,189,930 | 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,... | train_df['SibSp_categ'] = [0 if i in [0,1,2] else 1 if i in [3,4] else 2 for i in train_df.SibSp] | Titanic - Machine Learning from Disaster |
13,189,930 | X = pd.concat([train_proc, test_proc], ignore_index=True, sort=False )<define_variables> | train_df = pd.get_dummies(data=train_df, columns=['SibSp_categ'] ) | Titanic - Machine Learning from Disaster |
13,189,930 | 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_df['Parch_categ'] = [0 if i in [1,2,3] else 1 if i in [0,5] else 2 for i in train_df.Parch] | Titanic - Machine Learning from Disaster |
13,189,930 | 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' )<feature_engineering> | train_df = pd.get_dummies(data=train_df, columns=['Parch_categ'] ) | Titanic - Machine Learning from Disaster |
13,189,930 | for i in categorical_columns:
X_temp.loc[:, i] = pd.factorize(X_temp.loc[:, i])[0]<feature_engineering> | train_df = pd.get_dummies(data=train_df, columns=['Embarked'] ) | Titanic - Machine Learning from Disaster |
13,189,930 | X_text = X_temp[text_columns]
for i in X_text.columns:
X_text.loc[:, i] = X_text.loc[:, i].fillna('none' )<categorify> | ticket_list=[]
for i in train_df['Ticket']:
if not i.strip().isdigit() :
ticket_list.append(i.strip().replace('.','' ).replace('/','' ).split() [0])
else:
ticket_list.append('x')
train_df['Ticket'] = ticket_list | Titanic - Machine Learning from Disaster |
13,189,930 | X_temp['Length_Description'] = X_text['Description'].map(len)
X_temp['Length_metadata_annots_top_desc'] = X_text['metadata_annots_top_desc'].map(len)
X_temp['Lengths_sentiment_entities'] = X_text['sentiment_entities'].map(len )<feature_engineering> | train_df = pd.get_dummies(train_df, columns=['Ticket'], prefix='T' ) | Titanic - Machine Learning from Disaster |
13,189,930 | n_components = 16
text_features = []
for i in X_text.columns:
print(f'generating features from: {i}')
tfv = TfidfVectorizer(min_df=2, max_features=None,
strip_accents='unicode', analyzer='word', token_pattern=r'(?u)\b\w+\b',
ngram_range=(1, 3), use_idf=1, smooth_idf=1, sublinear_tf=1)
svd_ = TruncatedSVD(
n_componen... | train_df['Pclass'] = train_df['Pclass'].astype('category')
train_df = pd.get_dummies(data=train_df, columns=['Pclass'])
train_df.head() | Titanic - Machine Learning from Disaster |
13,189,930 | X_temp = X_temp.merge(img_features, how='left', on='PetID' )<define_variables> | train_df['Sex'] = train_df['Sex'].astype('category')
train_df = pd.get_dummies(data=train_df, columns=['Sex'])
train_df.head() | Titanic - Machine Learning from Disaster |
13,189,930 | train_df_ids = train[['PetID']]
test_df_ids = test[['PetID']]
train_df_imgs = pd.DataFrame(train_image_files)
train_df_imgs.columns = ['image_filename']
train_imgs_pets = train_df_imgs['image_filename'].apply(lambda x: x.split(split_char)[-1].split('-')[0])
test_df_imgs = pd.DataFrame(test_image_files)
test_df_imgs.... | train_df.drop(labels=['PassengerId', 'Cabin'], axis=1, inplace=True)
train_df.columns | Titanic - Machine Learning from Disaster |
13,189,930 | X_temp = X_temp.merge(agg_imgs, how='left', on='PetID' )<drop_column> | from sklearn.model_selection import train_test_split, StratifiedKFold, GridSearchCV
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTr... | Titanic - Machine Learning from Disaster |
13,189,930 | X_temp = X_temp.drop(to_drop_columns, axis=1 )<define_variables> | test = train_df[train_df_len:]
test.drop(labels=['Survived'], axis=1, inplace=True)
test.head() | Titanic - Machine Learning from Disaster |
13,189,930 | X_train_non_null = X_train.fillna(-1)
X_test_non_null = X_test.fillna(-1 )<count_missing_values> | train = train_df[:train_df_len]
y_train = train['Survived']
x_train = train.drop('Survived', axis=1)
x_train,x_test,y_train,y_test = train_test_split(x_train,y_train,test_size=0.2, random_state=42)
print('x_train: ',len(x_train))
print('x_test: ',len(x_test))
print('y_train: ',len(y_train))
print('y_test: ',len(y_tes... | Titanic - Machine Learning from Disaster |
13,189,930 | X_train_non_null.isnull().any().any() , X_test_non_null.isnull().any().any()<import_modules> | log_reg = LogisticRegression()
log_reg.fit(x_train,y_train)
acc_logreg_train = round(log_reg.score(x_train,y_train)*100,2)
acc_logreg_test = round(log_reg.score(x_test,y_test)*100,2)
print('Training accuracy: %{}'.format(acc_logreg_train))
print('Testing accuracy: %{}'.format(acc_logreg_test)) | Titanic - Machine Learning from Disaster |
13,189,930 | 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... | random_state = 42
model_list = [DecisionTreeClassifier(random_state = random_state),
SVC(random_state = random_state, probability=True),
RandomForestClassifier(random_state = random_state),
LogisticRegression(random_state = random_state),
KNeighborsClassifier() ]
dt_param_grid = {"min_samples_split" : range(10,500,20),... | Titanic - Machine Learning from Disaster |
13,189,930 | class OptimizedRounder(object):
def __init__(self):
self.coef_ = 0
def _kappa_loss(self, coef, X, y):
preds = pd.cut(X, [-np.inf] + list(np.sort(coef)) + [np.inf], labels = [0, 1, 2, 3, 4])
return -cohen_kappa_score(y, preds, weights='quadratic')
def fit(self, X, y):
loss_partial = partial(self._kappa_loss, X = X, y ... | score_list = []
best_estimators = []
for i in range(5):
my_model = GridSearchCV(estimator=model_list[i], param_grid=grid_list[i], scoring='accuracy', n_jobs=-1, cv=StratifiedKFold(n_splits=10))
my_model.fit(x_train, y_train)
score_list.append(accuracy_score(my_model.best_estimator_.predict(x_test), y_test))
best_estim... | Titanic - Machine Learning from Disaster |
13,189,930 | xgb_params = {
'eval_metric': 'rmse',
'seed': 1337,
'eta': 0.0123,
'subsample': 0.8,
'colsample_bytree': 0.85,
'tree_method': 'gpu_hist',
'device': 'gpu',
'silent': 1,
}<prepare_x_and_y> | votingC = VotingClassifier([('RandomForest',best_estimators[2]),
('SVC',best_estimators[1]),
('Logistic',best_estimators[3])], voting='soft', n_jobs=-1)
votingC.fit(x_train, y_train)
print('Ensembled score: ', accuracy_score(votingC.predict(x_test),y_test)) | Titanic - Machine Learning from Disaster |
13,189,930 | def run_xgb(params, X_train, X_test):
n_splits = 5
verbose_eval = 1000
num_rounds = 30000
early_stop = 500
kf = StratifiedKFold(n_splits=n_splits, shuffle=True, 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_idx, valid_idx in kf.split(X_tr... | test_survived = pd.Series(votingC.predict(test), name='Survived' ).astype(int)
results = pd.concat([test_PassengerId, test_survived], axis=1)
results.to_csv('titanic.csv', index=False ) | Titanic - Machine Learning from Disaster |
13,217,204 | model, oof_train, oof_test = run_xgb(xgb_params, X_train_non_null, X_test_non_null )<compute_train_metric> | train_data = pd.read_csv(".. /input/titanic/train.csv")
test_data = pd.read_csv(".. /input/titanic/test.csv")
train_data.columns | Titanic - Machine Learning from Disaster |
13,217,204 | optR = OptimizedRounder()
optR.fit(oof_train, X_train['AdoptionSpeed'].values)
coefficients = optR.coefficients()
valid_pred = optR.predict(oof_train, coefficients)
qwk = quadratic_weighted_kappa(X_train['AdoptionSpeed'].values, valid_pred)
print("QWK = ", qwk )<predict_on_test> | def outlier_detect(feature, data):
outlier_index = []
for each in feature:
Q1 = np.percentile(data[each], 25)
Q3 = np.percentile(data[each], 75)
IQR = Q3 - Q1
min_quartile = Q1 - 1.5*IQR
max_quartile = Q3 + 1.5*IQR
outlier_list = data[(data[each] < min_quartile)|(data[each] > max_quartile)].index
outlier_index.extend... | Titanic - Machine Learning from Disaster |
13,217,204 | coefficients_ = coefficients.copy()
coefficients_[0] = 1.65
coefficients_[1] = 2.12
coefficients_[3] = 2.84
train_predictions = optR.predict(oof_train, coefficients_ ).astype(np.int8)
print(f'train pred distribution: {Counter(train_predictions)}')
test_predictions = optR.predict(oof_test.mean(axis=1), coefficients_ )... | outlier_data = outlier_detect(["Age","SibSp","Parch","Fare"], train_data)
train_data.loc[outlier_data]
| Titanic - Machine Learning from Disaster |
13,217,204 | Counter(train_predictions )<count_values> | train_data = train_data.drop(outlier_data, axis=0 ).reset_index(drop=True ) | Titanic - Machine Learning from Disaster |
13,217,204 | Counter(test_predictions )<save_to_csv> | data = pd.concat([train_data, test_data], axis=0 ).reset_index(drop=True ) | Titanic - Machine Learning from Disaster |
13,217,204 | submission = pd.DataFrame({'PetID': test['PetID'].values, 'AdoptionSpeed': test_predictions})
submission.to_csv('submission.csv', index=False)
submission.head()<import_modules> | data[["Sex", "Survived"]].groupby(["Sex"], as_index = False ).mean() | Titanic - Machine Learning from Disaster |
13,217,204 | 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> | data.columns[data.isnull().any() ] | Titanic - Machine Learning from Disaster |
13,217,204 | plt.rcParams['figure.figsize'] =(12, 9 )<load_from_csv> | data.isnull().sum() | Titanic - Machine Learning from Disaster |
13,217,204 | 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> | data.isnull().sum() | Titanic - Machine Learning from Disaster |
13,217,204 | 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' )<data_type_conversions> | data[data["Fare"].isnull() ] | Titanic - Machine Learning from Disaster |
13,217,204 | def maek_features(src_df):
rescuer_count = src_df.groupby(['RescuerID'])['PetID'].count().reset_index()
rescuer_count.columns = ['RescuerID', 'RescuerID_CNT']
src_df = src_df.merge(rescuer_count, how='left', on='RescuerID')
return src_df<feature_engineering> | data["Fare"] = data["Fare"].fillna(np.mean(data[(( data["Pclass"]==3)&(data["Embarked"]==0)) ]["Fare"]))
data[data["Fare"].isnull() ] | Titanic - Machine Learning from Disaster |
13,217,204 | train = maek_features(train)
test = maek_features(test )<load_from_csv> | data[data["Embarked"].isnull() ] | Titanic - Machine Learning from Disaster |
13,217,204 | 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=... | data["Embarked"] = data["Embarked"].fillna(1)
data[data["Embarked"].isnull() ] | Titanic - Machine Learning from Disaster |
13,217,204 | with open('.. /input/cat-and-dog-breeds-parameters/rating.json', 'r')as f:
ratings = json.load(f )<define_variables> | data[data["Age"].isnull() ] | Titanic - Machine Learning from Disaster |
13,217,204 | cat_ratings = ratings['cat_breeds']
dog_ratings = ratings['dog_breeds']
<feature_engineering> | data_age_nan_index = data[data["Age"].isnull() ].index
for i in data_age_nan_index:
mean_age = data["Age"][(data["Pclass"]==data.iloc[i]["Pclass"])].median()
data["Age"].iloc[i] = mean_age | Titanic - Machine Learning from Disaster |
13,217,204 | breed_id = {}
for id,name in zip(labels_breed.BreedID,labels_breed.BreedName):
breed_id[id] = name<define_variables> | data["Alone"] = [1 if i == 0 else 0 for i in data["Family"]]
data["Family"].replace([0,1,2,3,4,5,6,7,10], [0,1,1,1,0,2,0,2,2], inplace=True)
data.head() | Titanic - Machine Learning from Disaster |
13,217,204 | breed_names_1 = [i for i in cat_ratings.keys() ]
breed_names_2 = [i for i in dog_ratings.keys() ]
<feature_engineering> | data['Title']=data.Name.str.extract('([A-Za-z]+)\.' ) | Titanic - Machine Learning from Disaster |
13,217,204 | for id in train['Breed1']:
if id in breed_id.keys() :
name = breed_id[id]
if name in breed_names_1:
for key in cat_ratings[name].keys() :
train[key] = cat_ratings[name][key]
if name in breed_names_2:
for key in dog_ratings[name].keys() :
train[key] = dog_ratings[name][key]<define_variables> | data['Title'].replace(['Mme','Ms','Mlle','Lady','Countess','Dona','Dr','Major','Sir','Capt','Don','Rev','Col', 'Jonkheer'],['Miss','Miss','Miss','Mrs','Mrs','Mrs','Mr','Mr','Mr','Mr','Mr','Other','Other','Other'], inplace=True ) | Titanic - Machine Learning from Disaster |
13,217,204 | 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(... | data['Age_Limit'] = LabelEncoder().fit_transform(data['Age_Limit'])
| Titanic - Machine Learning from Disaster |
13,217,204 | 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_... | data['Fare_Limit'] = LabelEncoder().fit_transform(data['Fare_Limit'])
| Titanic - Machine Learning from Disaster |
13,217,204 | 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... | data['Age']=data['Age'].astype(int)
data.drop(labels=["SibSp","Parch","Cabin","Fare","Age", "Ticket", "Name", "PassengerId"], axis=1, inplace = True)
data.head() | Titanic - Machine Learning from Disaster |
13,217,204 | 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... | data = pd.get_dummies(data,columns=["Pclass"])
data = pd.get_dummies(data,columns=["Embarked"])
data = pd.get_dummies(data,columns=["Family"])
data = pd.get_dummies(data,columns=["Age_Limit"])
data = pd.get_dummies(data,columns=["Fare_Limit"])
data = pd.get_dummies(data,columns=["Title"])
data.head() | Titanic - Machine Learning from Disaster |
13,217,204 | 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... | from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier, VotingClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC | Titanic - Machine Learning from Disaster |
13,217,204 | 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,... | if len(data)==(len(train_data)+ len(test_data)) :
print("success" ) | Titanic - Machine Learning from Disaster |
13,217,204 | X = pd.concat([train_proc, test_proc], ignore_index=True, sort=False)
print('NaN structure:
{}'.format(np.sum(pd.isnull(X))))<define_variables> | test = data[len(train_data):]
test.drop(labels="Survived", axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
13,217,204 | 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... | train = data[:len(train_data)]
X_train = train.drop(labels = "Survived", axis=1)
y_train = train["Survived"]
X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.3, random_state=42)
| Titanic - Machine Learning from Disaster |
13,217,204 | 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> | log_reg = LogisticRegression(random_state=42)
log_reg.fit(X_train, y_train)
print("Accuracy: ", log_reg.score(X_test,y_test)) | Titanic - Machine Learning from Disaster |
13,217,204 | 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> | rf_reg = RandomForestClassifier(random_state=42)
rf_reg.fit(X_train, y_train)
print("Accuracy: ", rf_reg.score(X_test,y_test)) | Titanic - Machine Learning from Disaster |
13,217,204 | for i in categorical_columns:
X_temp.loc[:, i] = pd.factorize(X_temp.loc[:, i])[0]<data_type_conversions> | svm_clsf = SVC()
svm_clsf.fit(X_train, y_train)
print("Accuracy: ", svm_clsf.score(X_test,y_test)) | Titanic - Machine Learning from Disaster |
13,217,204 | X_text = X_temp[text_columns]
for i in X_text.columns:
X_text.loc[:, i] = X_text.loc[:, i].fillna('<MISSING>' )<feature_engineering> | best_knn = []
for n in range(1,12):
knn = KNeighborsClassifier(n_neighbors=n)
knn.fit(X_train, y_train)
best_knn.insert(n, knn.score(X_test,y_test))
best_knn
| Titanic - Machine Learning from Disaster |
13,217,204 | 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... | knn_clsf = KNeighborsClassifier(n_neighbors=8)
knn_clsf.fit(X_train, y_train)
print("Accuracy: ", knn_clsf.score(X_test,y_test)) | Titanic - Machine Learning from Disaster |
13,217,204 | np.sum(pd.isnull(X_train))<count_missing_values> | voting_classfication = VotingClassifier(estimators = [('knn', knn_clsf),('lg', log_reg),('rfg', rf_reg),('svc', svm_clsf)], voting="hard", n_jobs=-1)
voting_classfication.fit(X_train, y_train)
print("Accuracy: ", voting_classfication.score(X_test,y_test)) | Titanic - Machine Learning from Disaster |
13,217,204 | <import_modules><EOS> | test_result = pd.Series(voting_classfication.predict(test), name = "Survived" ).astype(int)
results = pd.concat([test_data["PassengerId"], test_result],axis = 1)
results.to_csv("titanic_submission.csv", index = False ) | Titanic - Machine Learning from Disaster |
13,209,006 | <SOS> metric: categorizationaccuracy Kaggle data source: titanic-machine-learning-from-disaster<init_hyperparams> | import numpy as np
import pandas as pd | Titanic - Machine Learning from Disaster |
13,209,006 | 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... | train = pd.read_csv(".. /input/titanic/train.csv")
test = pd.read_csv(".. /input/titanic/test.csv")
gender_submission = pd.read_csv(".. /input/titanic/gender_submission.csv" ) | Titanic - Machine Learning from Disaster |
13,209,006 | X_train = X_train.drop('img_Unnamed: 0',axis=1)
X_test = X_test.drop('img_Unnamed: 0',axis=1 )<prepare_x_and_y> | data = pd.concat([train, test], sort=False ) | Titanic - Machine Learning from Disaster |
13,209,006 | 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... | data.isnull().sum() | Titanic - Machine Learning from Disaster |
13,209,006 | 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... | data.isnull().sum() | Titanic - Machine Learning from Disaster |
13,209,006 | 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 =... | data['Sex'].replace(['male','female'], [0, 1], inplace=True ) | Titanic - Machine Learning from Disaster |
13,209,006 | coefficients_ = coefficients.copy()
coefficients_[0] = 1.79
coefficients_[1] = 2.39
coefficients_[3] = 2.99
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.mean(... | data['Embarked'].fillna(( 'S'), inplace=True)
data['Embarked'] = data['Embarked'].map({'S': 0, 'C': 1, 'Q': 2} ).astype(int ) | Titanic - Machine Learning from Disaster |
13,209,006 | 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... | data['Fare'].fillna(np.mean(data['Fare']), inplace=True)
data['fare_value']=data['Fare']/50 | Titanic - Machine Learning from Disaster |
13,209,006 | df = pd.concat([X_train, X_test], axis=0)
df.head(2 )<concatenate> | age_avg = data['Age'].mean()
age_std = data['Age'].std()
data['Age'].fillna(np.random.randint(age_avg - age_std, age_avg + age_std), inplace=True)
data['age_value']=data['Age']/50 | Titanic - Machine Learning from Disaster |
13,209,006 | df_ = pd.concat([train,test],axis=0 )<categorify> | data['family'] =(data['SibSp'] + data['Parch'])/5 | Titanic - Machine Learning from Disaster |
13,209,006 | word_vec_size = 300
max_words = 100
max_word_features = 25000
def transform_text(text, tokenizer):
tokenizer.fit_on_texts(text)
text_emb = tokenizer.texts_to_sequences(text)
text_emb = sequence.pad_sequences(text_emb, maxlen=max_words)
return text_emb
desc_tokenizer = text.Tokenizer(num_words=max_word_features)
des... | data['isAlone'] = 0
data.loc[data['family'] > 0, 'isAlone'] = 1 | Titanic - Machine Learning from Disaster |
13,209,006 | text_mode = "fasttext"
if text_mode == "fasttext":
embedding_file = ".. /input/fasttext-crawl-300d-2m/crawl-300d-2M.vec"
def get_coefs(word, *arr): return word, np.asarray(arr, dtype='float32')
embeddings_index = dict(get_coefs(*o.rstrip().rsplit(' ')) for o in open(embedding_file))
word_index = desc_tokenizer.word_in... | delete_columns = ['Name','PassengerId','SibSp','Parch','Ticket','Cabin','Age','Fare']
data.drop(delete_columns, axis=1, inplace=True ) | Titanic - Machine Learning from Disaster |
13,209,006 | np.sum(pd.isnull(df))<define_variables> | train = data[:len(train)]
test = data[len(train):] | Titanic - Machine Learning from Disaster |
13,209,006 | cat_vars = ["Type", "Breed1", "Breed2", "Color1", "Color2", "Color3", "Gender", "MaturitySize",
"FurLength", "Vaccinated", "Dewormed", "Sterilized", "Health", "State"]
cont_vars = ["Fee", "PhotoAmt", "VideoAmt", "Age", "Quantity",'RescuerID_CNT']<categorify> | y_train0 = train['Survived']
X_train0 = train.drop('Survived', axis = 1)
X_test0 = test.drop('Survived', axis = 1 ) | Titanic - Machine Learning from Disaster |
13,209,006 | def preproc(df):
global cont_vars
for var in cat_vars:
df[var] = LabelEncoder().fit_transform(df[var])
for var in cont_vars:
df[var] = MinMaxScaler().fit_transform(df[var].values.reshape(-1,1))
return df<normalization> | X = np.array(X_train0)
y = np.array(y_train0 ) | Titanic - Machine Learning from Disaster |
13,209,006 | df_scaled = preproc(df)
train_df = df_scaled[:len(train)]
test_df = df_scaled[len(train):]
len(train_df), len(test_df )<feature_engineering> | clf = XGBClassifier(max_depth=3, n_estimators=1000, learning_rate=0.01 ) | Titanic - Machine Learning from Disaster |
13,209,006 | def get_keras_data(df, description_embeds):
X = {var: df[var].values for var in cont_vars+cat_vars}
X["description"] = description_embeds
for i in range(256): X[f"img_{i}"] = df[f"img_{i}"]
return X<import_modules> | ss = ShuffleSplit(n_splits=5,
train_size=0.8,
test_size =0.2,
random_state=0)
for train_index, test_index in ss.split(X):
X_train, X_test = X[train_index], X[test_index]
Y_train, Y_test = y[train_index], y[test_index]
clf.fit(X_train, Y_train)
print(clf.score(X_test, Y_test)) | Titanic - Machine Learning from Disaster |
13,209,006 | class CyclicLR(Callback):
def __init__(self, base_lr=0.001, max_lr=0.006, step_size=2000., mode='triangular',
gamma=1., scale_fn=None, scale_mode='cycle'):
super(CyclicLR, self ).__init__()
self.base_lr = base_lr
self.max_lr = max_lr
self.step_size = step_size
self.mode = mode
self.gamma = gamma
if scale_fn == None:
... | y_pred = clf.predict(np.array(X_test0)) | Titanic - Machine Learning from Disaster |
13,209,006 | def rmse(y, y_pred):
return K.sqrt(K.mean(K.square(y-y_pred), axis=-1))
def get_model(emb_n=10, dout=.25, batch_size=1000):
inps = []
embs = []
nums = []
for var in cat_vars:
inp = Input(shape=[1], name=var)
inps.append(inp)
embs.append(( Embedding(df[var].max() +1, emb_n )(inp)))
for var in cont_vars:
inp = Input(s... | sub = gender_submission
sub['Survived'] = list(map(int, y_pred))
sub.to_csv("submission.csv", index=False ) | Titanic - Machine Learning from Disaster |
12,734,089 | nfolds=5
folds = StratifiedKFold(n_splits=nfolds,shuffle=True, random_state=15)
avg_train_kappa = 0
avg_valid_kappa = 0
batch_size=1000
coeffs=None
x_test = get_keras_data(test_df, desc_embs[len(train_df):])
adoptions_keras = np.zeros(( len(test_df),))
oof_train_keras = np.zeros(( train_df.shape[0]))
i =0
for train_i... | training = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
training['train_test'] = 1
test['train_test'] = 0
test['Survived'] = np.NaN
all_data = pd.concat([training,test])
| Titanic - Machine Learning from Disaster |
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