XGboost

import numpy as np
import os

import xgboost as xgb
from sklearn.metrics import roc_auc_score
from sklearn.model_selection import cross_val_score, train_test_split, GridSearchCV

if __name__ == '__main__':
    data_path = "./"  # This folder holds the csv files

    # load csv files. We use np.loadtxt. Delimiter is ","
    # and the text-only header row will be skipped.

    print("Loading data...")
    x_train = np.loadtxt(data_path + os.sep + "x_train.csv",
                         delimiter=",", skiprows=1)
    x_test = np.loadtxt(data_path + os.sep + "x_test.csv",
                        delimiter=",", skiprows=1)
    y_train = np.loadtxt(data_path + os.sep + "y_train.csv",
                         delimiter=",", skiprows=1)

    print ("All files loaded. Preprocessing...")

    # remove the first column(Id)
    x_train = x_train[:, 1:]
    x_test = x_test[:, 1:]
    y_train = y_train[:, 1:]

    # Every 100 rows correspond to one gene.
    # Extract all 100-row-blocks into a list using np.split.
    num_genes_train = x_train.shape[0] / 100
    num_genes_test = x_test.shape[0] / 100

    print("Train / test data has %d / %d genes." % \
          (num_genes_train, num_genes_test))
    x_train = np.split(x_train, num_genes_train)
    x_test = np.split(x_test, num_genes_test)

    # Reshape by raveling each 100x5 array into a 500-length vector
    x_train = [g.ravel() for g in x_train]
    x_test = [g.ravel() for g in x_test]

    # convert data from list to array
    x_train = np.array(x_train)
    y_train = np.array(y_train)
    x_test = np.array(x_test)
    y_train = np.ravel(y_train)

    # Now x_train should be 15485 x 500 and x_test 3871 x 500.
    # y_train is 15485-long vector.

    print("x_train shape is %s" % str(x_train.shape))
    print("y_train shape is %s" % str(y_train.shape))
    print("x_test shape is %s" % str(x_test.shape))

    print('Data preprocessing done...')

    x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size=0.2)

    # specify parameters via map
    param = {'max_depth':2, 'eta':1, 'silent':1, 'objective':'binary:logistic' }

    num_round = 2
    dtrain = xgb.DMatrix(x_train, label=y_train)
    bst = xgb.train(param, dtrain, num_round)

    # make prediction
    dtestx = xgb.DMatrix(x_test)
    y_pred = bst.predict(dtestx)

    accuracy = roc_auc_score(y_test, y_pred)

    print("the accuurace for XGboost is: ", accuracy)