adrialuzllompart

def confidence_intervals(data, confidence_level=0.99):
    
    low_end = (1 - confidence_level) / 2
    high_end = 1 - low_end
    
    bottom_percentile = np.round(data.iloc[:, 0].quantile(low_end), 4)
    top_percentile = np.round(data.iloc[:, 0].quantile(high_end), 4)
    
    print('The {}% confidence interval is [{}, {}]'.format(
        confidence_level * 100, bottom_percentile, top_percentile))

adrialuzllompart

def bootstrap(data, col, st_dev=False, rep=1000):
    
    if not st_dev:
        means = []
        n = len(data)

        for i in range(rep):
            sample = data.sample(n=n, replace=True)
            mean = sample[col].mean()
            means.append(mean)

adrialuzllompart

def permutation_test(control, treatment, alpha, r=1000):
    
    """
    Runs a permutation test to check whether the difference in means
    between control and treatment is statistically significant.
    
    Parameters:
        control: pd.Series
            A pandas series with all the control (A) observations
        

adrialuzllompart

from __future__ import division
import numpy as np
import pandas as pd
import random
from datetime import date


def expected(A, B):
    """
    Calculate expected score of team A in a match against team B

adrialuzllompart

def plot_learning_curves(estimator, X_train, y_train, X_val, y_val,
                         suptitle='', title='', xlabel='', ylabel=''):
    """
    Plots learning curves for a given estimator.

    Parameters
    ----------

    estimator : sklearn estimator
    X_train : pd.DataFrame

adrialuzllompart

# convert X and y to numpy arrays
X = X.as_matrix()
y = y.as_matrix()

# create stratified k-fold split generators for inner and outer loops
outer_kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=12)
inner_kf = StratifiedKFold(n_splits=10, shuffle=True, random_state=12)

# set up hyperparameter tuning
Cs = 10.0 ** np.arange(-4,3)