vpekar

import numpy as np
import scipy.stats as stats


def pttest(y, yhat):
    """Given NumPy arrays with predictions and with true values, 
    return Directional Accuracy Score, Pesaran-Timmermann statistic and its p-value
    """
    size = y.shape[0]
    pyz = np.sum(np.sign(y) == np.sign(yhat))/size

vpekar

{
	"name": "Darkmatter2",
	"globals":
	{
		"background": "#14191f",
		"caret": "#F8F8F0",
		"foreground": "#aec2e0",
		"invisibles": "#3B3A32",
		"lineHighlight": "#1b232c",
		"selection": "#183c66",

vpekar

"""Macroaveraged MAE and RMSE ([Baccianella et al 2009](http://nmis.isti.cnr.it/sebastiani/Publications/ISDA09.pdf)) for evaluation of ordinal classifiers.

"""

import numpy as np


def groupby_labels(y, yhat):
    """Based on https://stackoverflow.com/questions/38013778/is-there-any-numpy-group-by-function
    """

vpekar

#!/usr/bin/python
# -*- coding: utf-8 -*-
"""
=================================================================
Selecting dimensionality reduction with Pipeline and GridSearchCV
=================================================================

This example constructs a pipeline that does dimensionality
reduction followed by prediction with a support vector
classifier. It demonstrates the use of GridSearchCV and

vpekar

"""
======================================================
Classification of text documents using sparse features
======================================================

This is an example showing how scikit-learn can be used to classify documents
by topics using a bag-of-words approach. This example uses a scipy.sparse
matrix to store the features and demonstrates various classifiers that can
efficiently handle sparse matrices.