#!/usr/bin/env python
# coding: utf-8

# In[1]:


# Full width
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))


# In[2]:


get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')


# In[3]:


import math
import os
import subprocess
import pickle 

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from IPython.display import display

# 
from lib_modeling import *
from lib_feature_engineering import *

# some settings for displaying Pandas results
pd.set_option('display.width', 2000)
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.precision', 4)
pd.set_option('display.max_colwidth', -1)


# # Load combined features + label

# In[4]:


pdf_features_label = pd.read_csv(os.path.join("features", "pdf_features_label.csv.bz2"), compression="bz2")
pdf_train = pdf_features_label.query("tvt_code == 'train'")
meta_cols = ["SK_ID_CURR", "TARGET", "tvt_code"]
ls_features = [cname for cname in pdf_features_label.columns if cname not in meta_cols]

# 
print("Number of features: {}".format(len(ls_features)))
print("pdf_features_label: {}".format(pdf_features_label.shape))
print("pdf_train: {}".format(pdf_train.shape))


# # Load feature important

# In[5]:


# read model
version = "v07"
with open("../05_modeling/models/xgb_model_{}.mod".format(version), "rb") as input_file:
    res_model = pickle.load(input_file)
res_model.keys()


# In[6]:


# load most important feature set
pdf_imp = pd.DataFrame(res_model["imp"])
pdf_imp.rename(columns= {0: "feat_name", 1: "F-score"}, inplace=True) 
pdf_imp.set_index("feat_name", inplace=True)
pdf_imp.head()


# In[7]:


# analyse tracking important features
ls_imp = []
for track in res_model["ls_tracked_imp"]:
    imp = pd.DataFrame(track)
    imp.rename(columns= {0: "feat_name", 1: "imp"}, inplace=True) 
    ls_imp.append(imp)
pdf_analysis = pd.concat(ls_imp).groupby("feat_name").agg({"imp": ["max", "min", "mean", "std"], "feat_name": "count"})

# rename columns
name01 = pdf_analysis.columns.get_level_values(0)
name02 = pdf_analysis.columns.get_level_values(1)
rename_cols = ["{}_{}".format(tpl[0], tpl[1]) for tpl in zip(name01, name02)]
rename_cols[-1] = "num_chosen_by_model"
pdf_analysis.columns = rename_cols
pdf_analysis = pdf_analysis.sort_values(by="imp_max", ascending=False)


# In[8]:


# get top features
pdf_analysis.head()


# In[9]:


# get tail features
pdf_analysis.tail()


# # Analysis

# In[10]:


def plot_distribution_over_target(pdf_input, cname):
    fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 5))
    
    #
    pdf_input.groupby("TARGET")[cname].plot(kind='kde', ax=axes[0], rot=45)
    pdf_input.groupby("TARGET")[cname].hist(bins=100, ax=axes[1], xrot=45)
    pdf_input.boxplot(column=cname, by='TARGET', ax=axes[2])
    
    #
    plt.suptitle("Distribution of {} (0: blue, 1: red)".format(cname))
    plt.show()
    
def my_auc(y_score, y_true, flexible_sign=True):
    # filter NaN
    idx = np.isfinite(y_score)
    xxx = y_score[idx]
    yyy = y_true[idx]

    # if label not only 1s/0s
    if yyy.std() > 0.0:
        auc = metrics.roc_auc_score(y_score=xxx, y_true=yyy)
    else:
        auc = 0.5

    # for evaluation only
    if (auc < 0.5) & (flexible_sign):
        auc = 1.0 - auc
    return auc

def feature_evaluate(pdf_feat_label, ls_feat=None):
    out_res = {
        "feat_name": [],
        "auc": [],
        "corr": [],
        "coverage": []
    }

    # calculate correlation
    pdf_corr = pdf_feat_label[["TARGET"] + ls_feat].corr()

    for feat in ls_feat:
        out_res["feat_name"].append(feat)
        out_res["auc"].append(my_auc(pdf_feat_label[feat], pdf_feat_label["TARGET"]))
        out_res["corr"].append(pdf_corr.loc[feat, "TARGET"])
        out_res["coverage"].append((~pdf_feat_label[feat].isna()).mean())

    #
    pdf_res = pd.DataFrame(out_res)
    pdf_res = pdf_res[["feat_name", "auc", "corr", "coverage"]].sort_values(by="auc", ascending=False)
    pdf_res.set_index("feat_name", inplace=True)
    return pdf_res


# In[11]:


ls_feat = ["prev_app_NAME_PRODUCT_TYPE_walk_in_max", "baseline_extend_EXT_SOURCE_2", "credit_card_balance_AMT_DRAWINGS_CURRENT_min"]
pdf_eval01 = feature_evaluate(pdf_train, ls_feat=ls_feat)
pdf_eval01 = pdf_analysis.join(pdf_eval01, how="inner")
pdf_eval01


# # AUC
# 
# Seperate càng cao thì càng tốt 

# In[12]:


pdf0 = pdf_train.query("TARGET == 0")
pdf1 = pdf_train.query("TARGET == 1")

fig, ax = plt.subplots()
pdf0["prev_app_NAME_PRODUCT_TYPE_walk_in_max"].value_counts().plot(kind="bar", ax=ax, color="blue")
pdf1["prev_app_NAME_PRODUCT_TYPE_walk_in_max"].value_counts().plot(kind="bar", ax=ax, color="red")
plt.show()


# In[13]:


attr = "baseline_extend_EXT_SOURCE_2"
display(pdf_eval01.loc[attr].to_frame().T)
plot_distribution_over_target(pdf_train, attr)


# In[14]:


attr = "credit_card_balance_AMT_DRAWINGS_CURRENT_min"
display(pdf_eval01.loc[attr].to_frame().T)
plot_distribution_over_target(pdf_train, attr)


# # Correlation
# 
# Tương quan càng cao càng tốt

# In[15]:


pdf_train[["TARGET", "baseline_extend_EXT_SOURCE_2", "credit_card_balance_AMT_DRAWINGS_CURRENT_min"]].corr()


# # Coverage
# 
# Null càng ít càng tốt

# In[16]:


pdf_train["baseline_extend_EXT_SOURCE_2"].isna().mean()


# In[17]:


pdf_train["credit_card_balance_AMT_DRAWINGS_CURRENT_min"].isna().mean()