beannguyen

beannguyen

import numpy as np
import pandas as pd
from numpy import abs
from numpy import log
from numpy import sign
from scipy.stats import rankdata

# region Auxiliary functions
def ts_sum(df, window=10):
    """

beannguyen

from sklearn.metrics import log_loss, f1_score, accuracy_score

def ml_cross_val_score(
        baseline_fn,
        X: pd.DataFrame,
        y: pd.Series,
        cv_gen,
        sample_weight_train: np.ndarray = None,
        sample_weight_score: np.ndarray = None,
        scoring = log_loss):

beannguyen

from sklearn.svm import SVC
from time import time
from sklearn.metrics import f1_score

def train_classifier(clf, X_train, y_train):
    ''' Fits a classifier to the training data. '''

    # Start the clock, train the classifier, then stop the clock
    start = time()
    clf.fit(X_train, y_train)

beannguyen

_train_data, _test_data, _eval_data = create_dataset(['bitcoin.csv'])
print(_train_data.shape, _test_data.shape, _eval_data.shape)
train_scaler, train_data = scale_data(_train_data)
train_x, train_y = extract_features_labels(train_data)

test_scaler, test_data = scale_data(_test_data)
test_x, test_y = extract_features_labels(test_data)

beannguyen

def preprocessing(file):
    _data = []
    print('Processing file ', file)
    f = open(file, 'r')
    try:
        df = pd.read_csv(f, delimiter=',', usecols=['date', 'open', 'high', 'low', 'close'])
        df = df.sort_values('date')
        df = df.drop(['date'], axis=1)

        dataset = df.values

beannguyen

beannguyen

from __future__ import absolute_import, division, print_function

from math import sqrt

import numpy as np
import pandas as pd
import tensorflow as tf
from keras import Sequential, optimizers, metrics
from keras.layers import LSTM, Dropout, Dense
from keras.losses import mean_squared_error

beannguyen

#!/usr/bin/python
# -*- coding: utf-8 -*-

def xpath_soup(element):
    """
    Generate xpath from BeautifulSoup4 element

    :param element: BeautifulSoup4 element.
    :type element: bs4.element.Tag or bs4.element.NavigableString
    :return: xpath as string