ecolor='black')
ax.set_yticks(y_pos)
ax.set_yticklabels(show_features)
ax.invert_yaxis() # labels read top-to-bottom
ax.set_xlabel('Gain')
plt.show()
transformer.print_config()
print transformer.attribute_position
print show_features[0]
print show_features[0].split("#")[0]
most_important_attribute = int(show_features[0].split("#")[0])
transformer.next_transformation_for_attribute(
most_important_attribute)
print "##############"
transformer.print_config()
print transformer.attribute_position
# Make predictions using the testing set
y_pred = regr.predict(datasets[1])
print "F1: " + str(f1_score(targets[1], y_pred, average='micro'))
#print explain_prediction_me(X_test[0, :], regr, feature_names)
y_test = targets[1]
for record_i in [0]: #range(len(y_test)):
pandas_table.columns[col_rep],
pandas_table.columns[target_column]
]]
transformer = Transformer(new_dataframe, 1)
while True:
transformer.fit()
datasets, targets, feature_names = transformer.transform()
if type(datasets[0]) == type(None):
break
transformer.next_transformation_for_attribute(0)
print str(type(datasets[0]))
print str(datasets[0].shape)
if len(datasets[0].shape) == 1:
for data_i in range(3):
datasets[data_i] = np.matrix(datasets[data_i]).T
regr = xgb.XGBClassifier(objective='multi:softprob',
nthread=4)
regr.fit(datasets[0], targets[0])
from sklearn import svm
#regr = svm.SVC()