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modelling.py
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modelling.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Wed Mar 30 11:36:13 2022
"""
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer, HashingVectorizer, TfidfVectorizer
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.pipeline import Pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import MultinomialNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, roc_auc_score
from warnings import simplefilter
simplefilter(action='ignore', category=FutureWarning)
model_data = pd.read_csv('../data/data_for_model.csv', keep_default_na=False)
print(model_data['is_suicide'].mean())
df_list=[]
def multi_modelling(columns_list, model):
for i in columns_list:
X = model_data[i]
y = model_data['is_suicide']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)
cvec = CountVectorizer()
cvec.fit(X_train)
X_train = pd.DataFrame(cvec.transform(X_train).todense(),
columns=cvec.get_feature_names())
X_test = pd.DataFrame(cvec.transform(X_test).todense(),
columns=cvec.get_feature_names())
nb = MultinomialNB()
nb.fit(X_train,y_train)
pred = nb.predict(X_test)
tn, fp, fn, tp = confusion_matrix(y_test, pred).ravel()
nb.predict_proba(X_test)
pred_proba = [i[1] for i in nb.predict_proba(X_test)]
auc = roc_auc_score(y_test, pred_proba)
classi_dict = (classification_report(y_test,pred, output_dict=True))
model_results = {}
model_results['series used (X)'] = i
model_results['model'] = model
model_results['AUC Score'] = auc
model_results['precision']= classi_dict['weighted avg']['precision']
model_results['recall (sensitivity)']= classi_dict['weighted avg']['recall']
model_results['confusion matrix']={"TP": tp,"FP":fp, "TN": tn, "FN": fn}
model_results['train accuracy'] = nb.score(X_train, y_train)
model_results['test accuracy'] = nb.score(X_test, y_test)
model_results['baseline accuracy']=0.496
model_results['specificity']= tn/(tn+fp)
model_results['f1-score']= classi_dict['weighted avg']['f1-score']
model_results
df_list.append(model_results)
pd.set_option("display.max_colwidth", 50)
return (pd.DataFrame(df_list)).round(2)
columns_list = ['selftext', "author", "title",'selftext_clean', "author_clean", "title_clean", "megatext_clean"]
model = "CountVec + MultinomialNB"
multi_modelling(columns_list, model)
test=pd.DataFrame(data=df_list)
test.to_csv('../form/colum_data_.csv', index = False)
def gridsearch_multi(steps_titles, steps_list, pipe_params):
X = model_data["megatext_clean"]
y = model_data['is_suicide']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)
gs_results = pd.DataFrame(columns=['model','AUC Score', 'precision', 'recall (sensitivity)',
'best_params', 'best score', 'confusion matrix',
'train_accuracy','test_accuracy','baseline_accuracy',
'specificity', 'f1-score'])
for i in range(len(steps_list)):
pipe = Pipeline(steps=steps_list[i])
gs = GridSearchCV(pipe, pipe_params[i], cv=3)
gs.fit(X_train, y_train)
pred = gs.predict(X_test)
tn, fp, fn, tp = confusion_matrix(y_test, gs.predict(X_test)).ravel()
classi_dict = (classification_report(y_test,pred, output_dict=True))
gs.predict_proba(X_test)
pred_proba = [i[1] for i in gs.predict_proba(X_test)]
auc = roc_auc_score(y_test, pred_proba)
model_results = {}
model_results['model'] = steps_titles[i]
model_results['AUC Score'] = auc
model_results['precision']= classi_dict['weighted avg']['precision']
model_results['recall (sensitivity)']= classi_dict['weighted avg']['recall']
model_results['best params'] = gs.best_params_
model_results['best score'] = gs.best_score_
model_results['confusion matrix']={"TP": tp,"FP":fp, "TN": tn, "FN": fn}
model_results['train accuracy'] = gs.score(X_train, y_train)
model_results['test accuracy'] = gs.score(X_test, y_test)
model_results['baseline accuracy'] = 0.496
model_results['specificity']= tn/(tn+fp)
model_results['f1-score']= classi_dict['weighted avg']['f1-score']
df_list.append(model_results)
pd.set_option("display.max_colwidth", 200)
return (pd.DataFrame(df_list)).round(2)
df_list=[]
steps_titles = ['cvec+ multi_nb','cvec + ss + knn','cvec + ss + logreg']
steps_list = [
[('cv', CountVectorizer()),('multi_nb', MultinomialNB())], #不考虑语法、词的顺序，只考虑所有的词的出现频率 + 朴素贝叶斯
[('cv', CountVectorizer()),('scaler', StandardScaler(with_mean=False)),('knn', KNeighborsClassifier())],
[('cv', CountVectorizer()),('scaler', StandardScaler(with_mean=False)),('logreg', LogisticRegression())]
]
pipe_params = [
{'cv__stop_words':['english'], 'cv__ngram_range':[(1,1),(1,2)],'cv__max_features': [20, 30, 50],'cv__min_df': [2, 3],'cv__max_df': [.2, .25, .3]},
{'cv__stop_words':['english'], 'cv__ngram_range':[(1,1),(1,2)],'cv__max_features': [20, 30, 50],'cv__min_df': [2, 3],'cv__max_df': [.2, .25, .3]},
{'cv__stop_words':['english'], 'cv__ngram_range':[(1,1),(1,2)],'cv__max_features': [20, 30, 50],'cv__min_df': [2, 3],'cv__max_df': [.2, .25, .3]}
]
gridsearch_multi(steps_titles, steps_list, pipe_params)
steps_titles = ['tvec + multi_nb','tvec + ss + knn','tvec + ss + logreg']
steps_list = [
[('tv', TfidfVectorizer()),('multi_nb', MultinomialNB())],
[('tv', TfidfVectorizer()),('scaler', StandardScaler(with_mean=False)),('knn', KNeighborsClassifier())],
[('tv', TfidfVectorizer()),('scaler', StandardScaler(with_mean=False)),('logreg', LogisticRegression())]
]
pipe_params = [
{'tv__stop_words':['english'], 'tv__ngram_range':[(1,1),(1,2)],'tv__max_features': [20, 30, 50],'tv__min_df': [2, 3],'tv__max_df': [.2, .25, .3]},
{'tv__stop_words':['english'], 'tv__ngram_range':[(1,1),(1,2)],'tv__max_features': [20, 30, 50],'tv__min_df': [2, 3],'tv__max_df': [.2, .25, .3]},
{'tv__stop_words':['english'], 'tv__ngram_range':[(1,1),(1,2)],'tv__max_features': [20, 30, 50],'tv__min_df': [2, 3],'tv__max_df': [.2, .25, .3]}
]
gridsearch_multi(steps_titles, steps_list, pipe_params)
steps_titles = ['hvec + multi_nb','hvec + ss + knn','hvec + ss + logreg']
steps_list = [
[('hv', HashingVectorizer(alternate_sign=False)),('multi_nb', MultinomialNB())],
[('hv', HashingVectorizer(alternate_sign=False)),('scaler', StandardScaler(with_mean=False)),('knn', KNeighborsClassifier())],
[('hv', HashingVectorizer(alternate_sign=False)),('scaler', StandardScaler(with_mean=False)),('logreg', LogisticRegression())]
]
pipe_params = [
{'hv__stop_words':['english'], 'hv__ngram_range':[(1,1),(1,2)]},
{'hv__stop_words':['english'], 'hv__ngram_range':[(1,1),(1,2)]},
{'hv__stop_words':['english'], 'hv__ngram_range':[(1,1),(1,2)]}
]
gridsearch_multi(steps_titles, steps_list, pipe_params)
steps_titles = ['hvec + multi_nb (tuning_1)','tvec + multi_nb (tuning_1)']
steps_list = [
[('hv', HashingVectorizer(alternate_sign=False)),('multi_nb', MultinomialNB())],
[('tv', TfidfVectorizer()),('multi_nb', MultinomialNB())]
]
pipe_params = [
{'hv__stop_words':['english'], 'hv__ngram_range':[(1,1)], 'hv__n_features': [1000, 1200, 1400, 2000]},
{'tv__stop_words':['english'], 'tv__ngram_range':[(1,1),(1,2),(1,3)],'tv__max_features': [60, 65, 70, 75, 80],'tv__min_df': [1, 2, 3],'tv__max_df': [.4, .45,.5,.55, .6]},
]
gridsearch_multi(steps_titles, steps_list, pipe_params)
test=pd.DataFrame(data=df_list)
test.to_csv('../form/data_model.csv', index = False)
X = model_data["megatext_clean"]
y = model_data['is_suicide']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y)
tvec_optimised = TfidfVectorizer(max_df= 0.5, max_features=70, min_df=2, ngram_range=(1, 3),stop_words = 'english')
X_train_tvec = tvec_optimised.fit_transform(X_train).todense()
X_test_tvec = tvec_optimised.transform(X_test).todense()
nb = MultinomialNB()
nb.fit(X_train_tvec, y_train)
accuracy = nb.score(X_test_tvec, y_test)
pred_proba = [i[1] for i in nb.predict_proba(X_test_tvec)]
auc = roc_auc_score(y_test, pred_proba)
print("ACCURACY: {}\nAUC SCORE: {}".format(accuracy, auc))
def TF_IDF_most_used_words(category_string, data_series, palette):
tvec_optimised = TfidfVectorizer(max_df= 0.5, max_features=70, min_df=2, ngram_range=(1, 3),stop_words = 'english')
tvec_optimised.fit(data_series)
created_df = pd.DataFrame(tvec_optimised.transform(data_series).todense(),
columns=tvec_optimised.get_feature_names())
total_words = created_df.sum(axis=0)
top_20_words = total_words.sort_values(ascending = False).head(20)
top_20_words_df = pd.DataFrame(top_20_words, columns = ["count"])
sns.set_style("white")
plt.figure(figsize = (15, 8), dpi=300)
ax = sns.barplot(y= top_20_words_df.index, x="count", data=top_20_words_df, palette = palette)
plt.xlabel("Count", fontsize=9)
plt.ylabel('Common Words in {}'.format(category_string), fontsize=9)
plt.yticks(rotation=-5)
plt.savefig('../images/.most_used_wordsjpg')
TF_IDF_most_used_words("Words used by production model to identify SuicideWatch Posts", model_data["megatext_clean"], "vlag_r")
test_data = pd.read_csv('../test/ready.csv', keep_default_na=False)
X = test_data["megatext_clean"]
X_test_tvec = tvec_optimised.transform(X).todense()
test_pred = nb.predict(X_test_tvec)
test_data['predict'] = test_pred
test_data.to_csv('../test/result.csv', index = False)