import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import draw_image
train = pd.read_csv("data/train.csv")
label = train['label']
train = train.drop(['label'], 1)
for i in range(train.shape[0]):
t = train[i:(i+1)].values
t = t[0,:]
draw_image.draw(t,'data/' + str(i) + '_' + str(label[i]) + '.png')
train, test, feature_train, feature_test, label_train, label_test = dg.test_data(.8)
else:
train, test, feature_train, feature_test, label_train = dg.prod_data()
f_train = pd.concat([train,feature_train], axis = 1)
f_test = pd.concat([test,feature_test], axis = 1)
xgb = XGBClassifier(max_depth=6, learning_rate=0.1, n_estimators=50, objective='multi:softprob', subsample=1.0, colsample_bytree=1, seed=0)
le = LabelEncoder()
y = le.fit_transform(label_train.values)
xgb.fit(f_train.values, y)
y_pred = xgb.predict(f_test.values)
y_pred = le.inverse_transform(y_pred)
if isTest == 1 :
y_f = y_pred == label_test.values
print("misclassified = " + str(len(y_f[y_f==False])))
print("currect class = " + str(len(y_f[y_f==True])))
print("score = " + str(len(y_f[y_f==True])/len(y_f)))
t = test[~y_f]
l = label_test[~y_f]
l_p = y_pred[~y_f]
for i in range(0, len(l)):
di.draw(t[i:i+1].values[0,], "images/prob_" + str(i) + "_" + str(l.values[i]) + "_" + str(l_p[i]) )
else :
index = list(range(1,len(y_pred)+1))
index = pd.DataFrame(index, columns = ['ImageId'])
y_pred = pd.DataFrame(y_pred, columns = ['Label'])
final = pd.concat([index, y_pred], axis = 1)
final.to_csv("data/result.csv", index = False)