import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import pandas
def load_pkl():
    df = pandas.read_pickle('gender_36.pkl', 'rb')
feat_labels = load_pkl.columns[1:]
forest = RandomForestClassifier(n_estimators=1000, random_state=2, n_jobs=-1)
forest.fit(X_train, y_train)
importances = forest.feature_importances_
indices = np.argsort(importances)[::-1]
for f in range(X_train.shape[1]):
    print(f + 1, 30, feat_labels[f], importances[indices[f]])
plt.title('Feature Importances')
plt.bar(range(X_train.shape[1]), importances[indices], color='lightblue', align='center')
plt.xticks(range(X_train.shape[1]), feat_labels[indices], rotation=90)
plt.xlim([-1, X_train.shape[1]])
plt.tight_layout()
plt.show()

runfile('C:/Users/HSIPL/Desktop/Homework 9 draft.py', wdir='C:/Users/HSIPL/Desktop')
Reloaded modules: __mp_main__
Traceback (most recent call last):

  File "<ipython-input-3-53490d045156>", line 1, in <module>
    runfile('C:/Users/HSIPL/Desktop/Homework 9 draft.py', wdir='C:/Users/HSIPL/Desktop')

  File "C:UsersHSIPLAnaconda3libsite-packagesspyder_kernelscustomizespydercustomize.py", line 668, in runfile
    execfile(filename, namespace)

  File "C:UsersHSIPLAnaconda3libsite-packagesspyder_kernelscustomizespydercustomize.py", line 108, in execfile
    exec(compile(f.read(), filename, 'exec'), namespace)

  File "C:/Users/HSIPL/Desktop/Homework 9 draft.py", line 11, in <module>
    feat_labels = load_pkl.columns[1:]

AttributeError: 'function' object has no attribute 'columns'