#!/usr/bin/env python

'''
An example file to show how to use the feature-selection code in ml_lib
'''
import pandas
from tqdm import tqdm
import pandas as pd
import csv
from sklearn.datasets import fetch_mldata
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn import svm

import numpy as np

import seaborn as sns
import matplotlib.pyplot as plt

import feature_select
import depmeas

if __name__=='__main__':
    NUM_CV = 3
    RANDOM_SEED = 123
    MAX_ITER = 1000

    leuk = pd.read_csv(r'C:/Users/pc/Desktop/dataset/leukemia.csv')
    X = leuk['data']
    y = leuk['target']
   
    # split the data for testing
    (X_train, X_test, y_train, y_test) = train_test_split(X, y, test_size=0.3, random_state=RANDOM_SEED)

    # perform feature selection
    num_features_to_select = 25
    K_MAX = 1000
    estimator = depmeas.mi_tau
    n_jobs = -1
    feature_ranking = feature_select.feature_select(X_train, y_train, num_features_to_select=num_features_to_select, K_MAX=K_MAX, estimator=estimator, n_jobs=n_jobs)
    num_selected_features = len(feature_ranking )
    # for each feature, compute the accuracy on the test data as we add features
    mean_acc = np.empty((num_selected_features,))
    var_acc  = np.empty((num_selected_features,))
    for ii in tqdm(range(num_selected_features), desc='Computing Classifier Performance...'):
        classifier = svm.SVC(random_state=RANDOM_SEED,max_iter=MAX_ITER)
        X_test_in = X_test[:,feature_ranking [0:ii+1]]
        scores = cross_val_score(classifier, X_test_in, y_test, cv=NUM_CV, n_jobs=-1)

        mu = scores.mean()
        sigma_sq = scores.std()
        
        mean_acc[ii] = mu
        var_acc[ii] = sigma_sq

    x = np.arange(num_selected_features)+1
    y = mean_acc
    yLo = mean_acc-var_acc/2.
    yHi = mean_acc+var_acc/2.
    
    plt.plot(x,y)
    plt.fill_between(x,yLo,yHi,alpha=0.2)
    plt.grid(True)
    plt.title('Leukemia Dataset Feature Selection\n Total # Features=%d' % (X.shape[1]))
    plt.xlabel('# Selected Features')
    plt.ylabel('SVC Classifier Accuracy')
    plt.show()

Error:
Traceback (most recent call last):
  File "C:\Users\pc\PycharmProjects\MymrmrTest\venv\lib\site-packages\pandas\core\indexes\base.py", line 2657, in get_loc
    return self._engine.get_loc(key)
  File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas\_libs\hashtable_class_helper.pxi", line 1608, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'data'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "C:/Users/pc/PycharmProjects/MymrmrTest/feature_select_test.py", line 39, in <module>
    X = leuk['data']
  File "C:\Users\pc\PycharmProjects\MymrmrTest\venv\lib\site-packages\pandas\core\frame.py", line 2927, in __getitem__
    indexer = self.columns.get_loc(key)
  File "C:\Users\pc\PycharmProjects\MymrmrTest\venv\lib\site-packages\pandas\core\indexes\base.py", line 2659, in get_loc
    return self._engine.get_loc(self._maybe_cast_indexer(key))
  File "pandas\_libs\index.pyx", line 108, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\index.pyx", line 132, in pandas._libs.index.IndexEngine.get_loc
  File "pandas\_libs\hashtable_class_helper.pxi", line 1601, in pandas._libs.hashtable.PyObjectHashTable.get_item
  File "pandas\_libs\hashtable_class_helper.pxi", line 1608, in pandas._libs.hashtable.PyObjectHashTable.get_item
KeyError: 'data'

Process finished with exit code 1