michaelgao8

logging.basicConfig(
    format='%(asctime)s %(levelname)-8s %(message)s',
    level=logging.INFO,
    datefmt='%Y-%m-%d %H:%M:%S')
logging.getLogger().setLevel(logging.INFO)

michaelgao8

class CustomScaler(BaseEstimator,TransformerMixin):
    """Inspired by https://stackoverflow.com/a/41461843/6248179
    """
    def __init__(self,columns,copy=True,with_mean=True,with_std=True):
        self.scaler = StandardScaler(copy,with_mean,with_std)
        self.columns = columns

    def fit(self, X, y=None):
        self.scaler.fit(X.loc[:, self.columns].values, y)
        return self

michaelgao8

# SOURCE: https://news.ycombinator.com/item?id=21260001

replace nvl with coalesce

replace rownum <= 1 with LIMIT 1

replace listagg with string_agg

replace recursive hierarchy (start with/connect by/prior) with recursive

michaelgao8

    def featurize_num_prior_encounters_multi_index(id_col, time_col, period_in_days, df):
        start_col = 'start_col'
        df[start_col] = df[time_col] - pd.Timedelta(days = period_in_days)
        # set multi_index
        df = df.set_index([id_col, time_col])
        num_adm = []
        id_list = []
        for i, (idx, data) in enumerate(df.groupby(level = id_col)):
            num_adm.append(data.loc[data.index.get_level_values(1) > data['start_col']].shape[0])
            id_list.append(idx)

michaelgao8

Keybase proof

I hereby claim:

• I am michaelgao8 on github.
• I am michaelgao8 (https://keybase.io/michaelgao8) on keybase.
• I have a public key ASD0gvLDXyHs3rX3JLhkC09CLKU7q2HrdvMna8mHPC8qMwo

To claim this, I am signing this object:

michaelgao8

def inspect_df(DataFrame):
    """
    Drop-in code for easier grading
    input: pd.DataFrame of interest
    """
    print("Head: ")
    print(DataFrame.head())
    
    print(" ======================== ")
    print("Shape: ")

michaelgao8

hash=$(docker run -d -p 8888:8888 -v /Users/michael/Projects:/home/jovyan/work jupyter/datascience-notebook jupyter notebook) && sleep 5 && docker exec "$hash" jupyter notebook list

michaelgao8

def cross_validate_xgboost(train_data, train_output, 
                           n_folds, param_grid, 
                           type_dict, 
                           fixed_param_dict = {'objective': 'binary:logistic', 'eval_metric': ['auc']}, 
                           metric_func_dict = {'auc': sklearn.metrics.roc_auc_score}, 
                           other_metrics_dict = None, keep_data = True, **kwargs):

    """ 
    Perform k-fold cross-validation with xgboost hyperparameters
    Get the average performance across folds and save all of the results

michaelgao8

jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace Notebook.ipynb

michaelgao8

import numpy as np

a = [1,2,3]
b = [3,4,5]
c = [6,7]
d = [8,9,0]

# Desired:
# All possible combinations of these 4 values.