/
utils.py
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/
utils.py
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import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import imputers
import random_deletion
from sklearn.cross_validation import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
_rf = RandomForestClassifier(n_jobs=-1, criterion='entropy', random_state=123)
_lr = LogisticRegression(random_state=123)
_nn = KNeighborsClassifier(n_jobs=-1)
_algs = ['RF', 'LR', 'kNN']
_methods = ['ignore', 'special', 'common', 'mean', 'svd', 'knn', 'rf', 'lr', 'em', 'k-means', 'zet']
_colors = {'zet': '#CC0000',
'special': '#888800',
'common': '#66CC00',
'mean': '#00CCCC',
'svd': '#0066CC',
'knn': '#6600CC',
'rf': '#CC00CC',
'lr': '#333333',
'em': '#FF8000',
'k-means': '#CCCC00'}
_datasets = ['krkp', 'creditg', 'segment']
def multi_algs_cv(data, y, cv):
rf_cv = np.mean(cross_val_score(_rf, data, y, scoring='accuracy', cv=cv))
lr_cv = np.mean(cross_val_score(_lr, data, y, scoring='accuracy', cv=cv))
nn_cv = np.mean(cross_val_score(_nn, data, y, scoring='accuracy', cv=cv))
return rf_cv, lr_cv, nn_cv
def dataset_exps(data, y, cv, add_binary):
data_ignore, y_ignore = imputers.ignore_imputer(data, y)
data_special_1 = imputers.special_value_imputer(data, -1, add_binary=add_binary)
data_special_2 = imputers.special_value_imputer(data, 0, add_binary=add_binary)
data_common = imputers.common_value_imputer(data, add_binary=add_binary)
data_mean = imputers.mean_value_imputer(data, add_binary=add_binary)
data_svd = imputers.svd_imputer(data, rank=data.shape[1] // 2, add_binary=add_binary)
data_knn = imputers.knn_imputer(data, n_neighbors=5, add_binary=add_binary)
data_rf = imputers.rf_imputer(data, add_binary=add_binary)
data_lr = imputers.linear_imputer(data, add_binary=add_binary)
data_em = imputers.em_imputer(data, add_binary=add_binary)
data_km = imputers.kmean_imputer(data, add_binary=add_binary)
data_zet = imputers.zet_imputer(data, competent_row_num=6, competent_col_num=4, add_binary=add_binary)
result = np.zeros((len(_methods), len(_algs)))
if data_ignore.shape[0] >= 10:
result[0] = multi_algs_cv(data_ignore, y_ignore, 10)
result[1, 0] = multi_algs_cv(data_special_1, y, cv)[0]
result[1, 1:] = multi_algs_cv(data_special_2, y, cv)[1:]
result[2] = multi_algs_cv(data_common, y, cv)
result[3] = multi_algs_cv(data_mean, y, cv)
result[4] = multi_algs_cv(data_svd, y, cv)
result[5] = multi_algs_cv(data_knn, y, cv)
result[6] = multi_algs_cv(data_rf, y, cv)
result[7] = multi_algs_cv(data_lr, y, cv)
result[8] = multi_algs_cv(data_em, y, cv)
result[9] = multi_algs_cv(data_km, y, cv)
result[10] = multi_algs_cv(data_zet, y, cv)
result = pd.DataFrame(result, columns=_algs, index=_methods)
if data_ignore.shape[0] < 10:
result.drop('ignore', inplace=True)
return result
def make_experiments(data_real, target, clf, cv, missing_frac_range, num_iter, sp_value, add_binary, del_columns=None):
accuracy = pd.DataFrame(np.zeros((len(_methods), len(missing_frac_range))), index=_methods, columns=missing_frac_range)
rmse = pd.DataFrame(np.zeros((len(_methods), len(missing_frac_range))), index=_methods, columns=missing_frac_range)
for missing_frac in missing_frac_range:
print('start fraction:', missing_frac)
for iteration in range(num_iter):
data_missing = random_deletion.make_missing_value(data_real, del_fraction=missing_frac,
del_fraction_column=0.5)
# ignore
data_imp, y = imputers.ignore_imputer(data_missing, target)
if data_imp.shape[0] >= data_missing.shape[0] / 10:
cur_accuracy = np.mean(cross_val_score(clf, data_imp, y, scoring='accuracy', cv=10))
else:
cur_accuracy = 0
if iteration == 0:
accuracy.ix['ignore', missing_frac] = cur_accuracy / num_iter
elif cur_accuracy == 0 or accuracy.ix['ignore', missing_frac] == 0:
accuracy.ix['ignore', missing_frac] = 0
else:
accuracy.ix['ignore', missing_frac] += cur_accuracy / num_iter
# special value
data_imp = imputers.special_value_imputer(data_missing, value=sp_value, add_binary=add_binary)
cur_accuracy = np.mean(cross_val_score(clf, data_imp, target, scoring='accuracy', cv=cv))
cur_rmse = np.sum(np.array((data_real - data_imp) ** 2)) ** 0.5
accuracy.ix['special', missing_frac] += cur_accuracy / num_iter
rmse.ix['special', missing_frac] += cur_rmse / num_iter
# common value
data_imp = imputers.common_value_imputer(data_missing, add_binary=add_binary)
cur_accuracy = np.mean(cross_val_score(clf, data_imp, target, scoring='accuracy', cv=cv))
cur_rmse = np.sum(np.array((data_real - data_imp) ** 2)) ** 0.5
accuracy.ix['common', missing_frac] += cur_accuracy / num_iter
rmse.ix['common', missing_frac] += cur_rmse / num_iter
# mean value
data_imp = imputers.mean_value_imputer(data_missing, add_binary=add_binary)
cur_accuracy = np.mean(cross_val_score(clf, data_imp, target, scoring='accuracy', cv=cv))
cur_rmse = np.sum(np.array((data_real - data_imp) ** 2)) ** 0.5
accuracy.ix['mean', missing_frac] += cur_accuracy / num_iter
rmse.ix['mean', missing_frac] += cur_rmse / num_iter
# svd
data_imp = imputers.svd_imputer(data_missing, rank=data_missing.shape[1] // 2, add_binary=add_binary)
cur_accuracy = np.mean(cross_val_score(clf, data_imp, target, scoring='accuracy', cv=cv))
cur_rmse = np.sum(np.array((data_real - data_imp) ** 2)) ** 0.5
accuracy.ix['svd', missing_frac] += cur_accuracy / num_iter
rmse.ix['svd', missing_frac] += cur_rmse / num_iter
# knn
data_imp = imputers.knn_imputer(data_missing, n_neighbors=5, add_binary=add_binary)
cur_accuracy = np.mean(cross_val_score(clf, data_imp, target, scoring='accuracy', cv=cv))
cur_rmse = np.sum(np.array((data_real - data_imp) ** 2)) ** 0.5
accuracy.ix['knn', missing_frac] += cur_accuracy / num_iter
rmse.ix['knn', missing_frac] += cur_rmse / num_iter
# rf
data_imp = imputers.rf_imputer(data_missing, add_binary=add_binary)
cur_accuracy = np.mean(cross_val_score(clf, data_imp, target, scoring='accuracy', cv=cv))
cur_rmse = np.sum(np.array((data_real - data_imp) ** 2)) ** 0.5
accuracy.ix['rf', missing_frac] += cur_accuracy / num_iter
rmse.ix['rf', missing_frac] += cur_rmse / num_iter
# lr
data_imp = imputers.linear_imputer(data_missing, add_binary=add_binary)
cur_accuracy = np.mean(cross_val_score(clf, data_imp, target, scoring='accuracy', cv=cv))
cur_rmse = np.sum(np.array((data_real - data_imp) ** 2)) ** 0.5
accuracy.ix['lr', missing_frac] += cur_accuracy / num_iter
rmse.ix['lr', missing_frac] += cur_rmse / num_iter
# em
data_imp = imputers.em_imputer(data_missing, add_binary=add_binary)
cur_accuracy = np.mean(cross_val_score(clf, data_imp, target, scoring='accuracy', cv=cv))
cur_rmse = np.sum(np.array((data_real - data_imp) ** 2)) ** 0.5
accuracy.ix['em', missing_frac] += cur_accuracy / num_iter
rmse.ix['em', missing_frac] += cur_rmse / num_iter
# km
data_imp = imputers.kmean_imputer(data_missing, add_binary=add_binary)
cur_accuracy = np.mean(cross_val_score(clf, data_imp, target, scoring='accuracy', cv=cv))
cur_rmse = np.sum(np.array((data_real - data_imp) ** 2)) ** 0.5
accuracy.ix['k-means', missing_frac] += cur_accuracy / num_iter
rmse.ix['k-means', missing_frac] += cur_rmse / num_iter
# zet
data_imp = imputers.zet_imputer(data_missing, competent_row_num=6, competent_col_num=4, add_binary=add_binary)
cur_accuracy = np.mean(cross_val_score(clf, data_imp, target, scoring='accuracy', cv=cv))
cur_rmse = np.sum(np.array((data_real - data_imp) ** 2)) ** 0.5
accuracy.ix['zet', missing_frac] += cur_accuracy / num_iter
rmse.ix['zet', missing_frac] += cur_rmse / num_iter
return accuracy, rmse
def make_plots_accuracy(accuracy_rf, accuracy_lr, accuracy_knn, dataset_name, filename=''):
plt.figure(figsize=(20, 5))
plt.suptitle('Accuracy (' + dataset_name + ')', fontsize=18)
plt.subplot(1, 4, 1)
for method in accuracy_rf.index:
plt.plot(accuracy_rf.columns, accuracy_rf.ix[method], label=method, color=_colors[method], lw=1.5)
plt.title('Random forest', fontsize=14)
plt.xlabel("Missing value fraction", fontsize=12)
plt.xlim([0, 0.15])
plt.ylabel("Accuracy", fontsize=12)
plt.subplot(1, 4, 2)
for method in accuracy_lr.index:
plt.plot(accuracy_lr.columns, accuracy_lr.ix[method], label=method, color=_colors[method], lw=1.5)
plt.title('Logistic regression', fontsize=14)
plt.xlabel("Missing value fraction", fontsize=12)
plt.xlim([0, 0.15])
#plt.ylabel("Accuracy", fontsize=12)
plt.subplot(1, 4, 3)
for method in accuracy_knn.index:
plt.plot(accuracy_knn.columns, accuracy_knn.ix[method], label=method, color=_colors[method], lw=1.5)
plt.title('Nearest neighbors', fontsize=14)
plt.xlabel("Missing value fraction", fontsize=12)
plt.xlim([0, 0.15])
#plt.ylabel("Accuracy", fontsize=12)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=14)
if filename:
plt.savefig(filename)
plt.show()
def make_plots_rmse(rmse_krkp, rmse_creditg, rmse_segment, filename):
plt.figure(figsize=(20, 5))
plt.suptitle('RMSE', fontsize=18)
plt.subplot(1, 4, 1)
for method in rmse_krkp.index:
plt.plot(rmse_krkp.columns, rmse_krkp.ix[method], label=method, color=_colors[method], lw=1.5)
plt.title(_datasets[0], fontsize=14)
plt.xlim([0, 0.15])
plt.xlabel("Missing value fraction", fontsize=12)
plt.ylabel("RMSE", fontsize=12)
plt.subplot(1, 4, 2)
for method in rmse_creditg.index:
plt.plot(rmse_creditg.columns, rmse_creditg.ix[method], label=method, color=_colors[method], lw=1.5)
plt.title(_datasets[1], fontsize=14)
plt.xlim([0, 0.15])
plt.xlabel("Missing value fraction", fontsize=12)
#plt.ylabel("RMSE", fontsize=12)
plt.subplot(1, 4, 3)
for method in rmse_segment.index:
plt.plot(rmse_segment.columns, rmse_segment.ix[method], label=method, color=_colors[method], lw=1.5)
plt.title(_datasets[2], fontsize=14)
plt.xlim([0, 0.15])
plt.xlabel("Missing value fraction", fontsize=12)
#plt.ylabel("RMSE", fontsize=12)
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5), fontsize=14)
if filename:
plt.savefig(filename)
plt.show()