Feature Importance 이쁘게 그리기

1. Lib

import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline

# Plotly
import plotly.offline as py
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import (accuracy_score, log_loss, classification_report)
from imblearn.over_sampling import SMOTE
import xgboost

from scipy.stats import pearsonr, chi2_contingency
from itertools import combinations
from statsmodels.stats.proportion import proportion_confint

import warnings
warnings.filterwarnings("ignore")

2. Code

trace = go.Scatter(
	# Feature importances
    y = rf.feature_importances_,
    
    # data columns (ex. data.columns.values)
    x = 데이터 컬럼,
    mode='markers',
    marker=dict(
        sizemode = 'diameter',
        sizeref = 1,
        size = 13,
        # Feature importances to colors
        color = rf.feature_importances_,
        colorscale='Portland',
        showscale=True
    ),
    # data columns (ex. data.columns.values)
    text = 데이터 컬럼
)
data = [trace]

layout= go.Layout(
    autosize= True,
    title= 'Random Forest Feature Importance',
    hovermode= 'closest',
     xaxis= dict(
         ticklen= 5,
         showgrid=False,
        zeroline=False,
        showline=False
     ),
    yaxis=dict(
        title= 'Feature Importance',
        showgrid=False,
        zeroline=False,
        ticklen= 5,
        gridwidth= 2
    ),
    showlegend= False
)
fig = go.Figure(data=data, layout=layout)
py.iplot(fig,filename='RF Feature Importances')

3. 다른 방법

# data.columns.values
feat = 데이터 컬럼

imp = gb.feature_importances_
df = pd.DataFrame({'Feature': feat, 'Importance': imp})
df = df.sort_values('Importance', ascending=False)[:10]
sns.barplot(x='Importance', y='Feature', data=df);