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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Model Building Part 3
Code for building the models
Author
Step1:  Training and Testing Split
Step2:  Scaling
Step3:  Text
Step5:  Custom Feature Separator
Step6:  Classification Models
Step7:  Although tuning is not necessary for Naive Bayes, I pass the default parameters of those models to GridSearchCV anyway so that I can do a direct pair-wise comparison with the other models across the different steps of cross-validation.  
In the interest of time, I didn't use the SVM classifier.
Step8:  Creating Pipelines
Step9:  I plan on using imblearn classes for later iterations so I use it's pipeline in the beginning for convenience
Step10:  Main Feature Union
Step11:  Naive Bayes Estimators
Step12:  QDA Estimators
Step13:  Logistic Estimators
Step14:  Random Forest Estimators
Step15:  Fitting Estimators
Step16:  Basic Estimators
Step17:  Testing Estimators
Step18:  Closer look at the variability of the best model
Step19:  Importing bootstrapped scores from parts 1 & 2 for side-by-side comparison
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Python Code:
import os
import pandas as pd
import numpy as np
import scipy as sp
import seaborn as sns
import matplotlib.pyplot as plt
import json
from IPython.display import Image
from IPython.core.display import HTML
retval=os.chdir("..")
clean_data=pd.read_pickle('./clean_data/clean_data.pkl')
clean_data.head()
kept_cols=['helpful', 'num_sents', 'num_words', 'readability', 'neg_senti',
       'pos_senti', 'neu_senti', 'comp_senti', 'text_lemma',]
Explanation: Model Building Part 3
Code for building the models
Author: Jimmy Charité
Email: jimmy.charite@gmail.com  
Following up with part one and two, I will combine the bag of words and the macro-level text summary statistics approach
End of explanation
my_rand_state=0
test_size=0.25
from sklearn.model_selection import train_test_split
X = (clean_data[kept_cols].iloc[:,1:]).as_matrix()
y = (clean_data[kept_cols].iloc[:,0]).tolist()
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, 
                                              random_state=my_rand_state)
Explanation: Training and Testing Split
End of explanation
from sklearn.preprocessing import StandardScaler
std_scale=StandardScaler()
Explanation: Scaling
End of explanation
from sklearn.feature_extraction.text import TfidfVectorizer
#set max_features to minimize training time
#also, I didn't apply LDA-based dimensionality reduction
tfidf=TfidfVectorizer(lowercase=False,max_features=200)
Explanation: Text
End of explanation
from sklearn.base import BaseEstimator, TransformerMixin
class ExtractText(BaseEstimator, TransformerMixin):
    
    Separates the features by numerical and text
    
    def __init__(self, text,n_text=-1):
        self.text = text
        self.n_text=n_text
    def fit(self, X, y=None):
        return self
    def transform(self, X, y=None):
        if(self.text):
            return X[:,self.n_text]
        else:
            return X[:,:self.n_text]
from sklearn.pipeline import FeatureUnion
Explanation: Custom Feature Separator
End of explanation
from sklearn.naive_bayes import GaussianNB
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import RandomForestClassifier
Explanation: Classification Models
End of explanation
nb_clf=GaussianNB()
priors=[None]
qda_clf=QuadraticDiscriminantAnalysis()
reg_param=[0.0, 0.25, 0.5, 0.75]
log_clf=LogisticRegression(penalty='l2')
C=[0.001 , 0.01, 10, 100,1000]
rf_clf=RandomForestClassifier()
n_estimators=[100,200]
max_features=[.1,.3,.5]
class_weight=['balanced']
class_weight.extend([{1: w} for w in [1, 2, 10]])
Explanation: Although tuning is not necessary for Naive Bayes, I pass the default parameters of those models to GridSearchCV anyway so that I can do a direct pair-wise comparison with the other models across the different steps of cross-validation.  
In the interest of time, I didn't use the SVM classifier.
End of explanation
from imblearn import pipeline #needed if mixing imblearn with sklearn classes
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import StratifiedKFold
Explanation: Creating Pipelines
End of explanation
n_jobs=4
n_folds=10
skfold = StratifiedKFold(n_splits=n_folds,random_state=my_rand_state, shuffle=False)
class DenseTransformer(BaseEstimator, TransformerMixin):
    def transform(self, X, y=None, **fit_params):
        return X.todense()
    def fit_transform(self, X, y=None, **fit_params):
        self.fit(X, y, **fit_params)
        return self.transform(X)
    def fit(self, X, y=None, **fit_params):
        return self
Explanation: I plan on using imblearn classes for later iterations so I use it's pipeline in the beginning for convenience
End of explanation
ft_union=FeatureUnion(transformer_list=[('text_pipe',pipeline.Pipeline([('extract',ExtractText(text=True)),
                                                                        ('tfidf',tfidf),
                                                                        ('to_dense', DenseTransformer())])),
                                        ('numb_pipe',pipeline.Pipeline([('extract',ExtractText(text=False)),
                                                                        ('scale',std_scale)]))])
Explanation: Main Feature Union
End of explanation
nb_clf_b = pipeline.Pipeline(steps=[('union',ft_union),('clf',nb_clf)])
nb_clf_est_b = GridSearchCV(estimator=nb_clf_b,cv=skfold,
              scoring='roc_auc',n_jobs=n_jobs,
              param_grid=dict(clf__priors=priors))
Explanation: Naive Bayes Estimators
End of explanation
qda_clf_b = pipeline.Pipeline(steps=[('union',ft_union),('clf',qda_clf)])
qda_clf_est_b = GridSearchCV(estimator=qda_clf_b,cv=skfold,
              scoring='roc_auc',n_jobs=n_jobs,
              param_grid=dict(clf__reg_param=reg_param))
Explanation: QDA Estimators
End of explanation
log_clf_b = pipeline.Pipeline(steps=[('union',ft_union),('clf',log_clf)])
log_clf_est_b = GridSearchCV(estimator=log_clf_b,cv=skfold,
              scoring='roc_auc',n_jobs=n_jobs,
              param_grid=dict(clf__C=C,
                              clf__class_weight=class_weight))
Explanation: Logistic Estimators
End of explanation
rf_clf_b = pipeline.Pipeline(steps=[('union',ft_union),('clf',rf_clf)])
rf_clf_est_b = GridSearchCV(estimator=rf_clf_b,cv=skfold,
              scoring='roc_auc',n_jobs=n_jobs,
              param_grid=dict(clf__n_estimators=n_estimators,
                              clf__max_features=max_features,
                              clf__class_weight=class_weight))
Explanation: Random Forest Estimators
End of explanation
from sklearn.externals import joblib
Explanation: Fitting Estimators
End of explanation
nb_clf_est_b.fit(X_train,y_train)
joblib.dump(nb_clf_est_b, './other_output/merged/nb_clf_est_b.pkl')
qda_clf_est_b.fit(X_train,y_train)
joblib.dump(qda_clf_est_b, './other_output/merged/qda_clf_est_b.pkl')
log_clf_est_b.fit(X_train,y_train)
joblib.dump(log_clf_est_b, './other_output/merged/log_clf_est_b.pkl')
rf_clf_est_b.fit(X_train,y_train)
joblib.dump(rf_clf_est_b, './other_output/merged/rf_clf_est_b.pkl')
Explanation: Basic Estimators: no bag of words or PCA
End of explanation
from sklearn.metrics import roc_curve, auc
nb_clf_est_b=joblib.load('./other_output/merged/nb_clf_est_b.pkl')
qda_clf_est_b=joblib.load('./other_output/merged/qda_clf_est_b.pkl')
log_clf_est_b=joblib.load('./other_output/merged/log_clf_est_b.pkl')
rf_clf_est_b=joblib.load('./other_output/merged/rf_clf_est_b.pkl')
nb_fpr, nb_tpr, _ = roc_curve(y_test, 
                    nb_clf_est_b.predict_proba(X_test)[:,1])
nb_roc_auc = auc(nb_fpr, nb_tpr)
qda_fpr, qda_tpr, _ = roc_curve(y_test, 
                    qda_clf_est_b.predict_proba(X_test)[:,1])
qda_roc_auc = auc(qda_fpr, qda_tpr)
log_fpr, log_tpr, _ = roc_curve(y_test, 
                    log_clf_est_b.predict_proba(X_test)[:,1])
log_roc_auc = auc(log_fpr, log_tpr)
rf_fpr, rf_tpr, _ = roc_curve(y_test, 
                    rf_clf_est_b.predict_proba(X_test)[:,1])
rf_roc_auc = auc(rf_fpr, rf_tpr)
plt.plot(nb_fpr, nb_tpr, color='cyan', linestyle='--',
         label='NB (area = %0.2f)' % nb_roc_auc, lw=2)
plt.plot(qda_fpr, qda_tpr, color='indigo', linestyle='--',
         label='QDA (area = %0.2f)' % qda_roc_auc, lw=2)
plt.plot(log_fpr, log_tpr, color='seagreen', linestyle='--',
         label='LOG (area = %0.2f)' % log_roc_auc, lw=2)
plt.plot(rf_fpr, rf_tpr, color='blue', linestyle='--',
         label='RF (area = %0.2f)' % rf_roc_auc, lw=2)
plt.plot([0, 1], [0, 1], linestyle='--', lw=2, color='k',
         label='Luck')
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curves of Basic Models Using Bag of Words and Macro-Text Stats')
plt.legend(loc="lower right")
plt.savefig('./plots/ROC_Basic_BOW_MERGED.png', bbox_inches='tight')
plt.show()
Explanation: Testing Estimators
End of explanation
from scipy.stats import sem
len(y_test) #the sample is large enough that we can get away with 5% draws
y_test=np.array(y_test)
X_test=np.array(X_test)
#initialize 
n_bootstraps = 2000
rng_seed = 1
sample_percent=0.05
min_index=0
max_index=len(y_test)-1
draw_size=int(len(y_test)*sample_percent)
bootstrapped_scores = []
rng = np.random.RandomState(rng_seed)
for i in range(n_bootstraps):
    # bootstrap by sampling with replacement on 
    indices = rng.random_integers(min_index, max_index, draw_size)
    
    #calculate ROC from 
    rf_fpr, rf_tpr, _ = roc_curve(y_test[indices], 
                    rf_clf_est_b.predict_proba(X_test[indices,:])[:,1])
    rf_roc_auc = auc(rf_fpr, rf_tpr)
    
    #save
    bootstrapped_scores.append(rf_roc_auc)
import pickle
with open('./other_output/merged/rf_bootstrapped_scores.pkl', 'wb') as f:
    pickle.dump(bootstrapped_scores, f)
plt.hist(bootstrapped_scores, bins=50)
plt.title('Histogram of Bootstrapped AUC ROC of\nRandom Forest Model Using Bag of Words and Macro-Text Stats')
plt.savefig('./plots/ROC_Histogram_Basic_BOW_MERGED.png', bbox_inches='tight')
plt.show()
pd.DataFrame({'auc':bootstrapped_scores}).auc.describe()
Explanation: Closer look at the variability of the best model
End of explanation
with open('./other_output/rf_bootstrapped_scores.pkl', 'rb') as f:
    bootstrapped_scores_macrotext = pickle.load(f)
with open('./other_output/bow/rf_bootstrapped_scores.pkl', 'rb') as f:
    bootstrapped_scores_bow = pickle.load(f)
bs_auc=pd.DataFrame({'Macro-Text Only':bootstrapped_scores_macrotext,
                     'Bag of Words Only':bootstrapped_scores_bow,
                     'Combined':bootstrapped_scores})
bs_auc=bs_auc[['Macro-Text Only','Bag of Words Only','Combined']]
bs_auc.describe()
pl_axes=bs_auc.boxplot(return_type='axes')
x=pl_axes.axes.set_ylabel('AUC ROC')
x=pl_axes.axes.set_title('Comparison of Bootstrapped AUC ROC of Random Forest Model\nUsing Macro-Text Stats, Bag of Words, and Combined Approaches')
plt.savefig('./plots/BoxPlot_ROC_MacText_BOX.png', bbox_inches='tight')
plt.show()
Explanation: Importing bootstrapped scores from parts 1 & 2 for side-by-side comparison
End of explanation
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