新手探索NLP（二）

ML CLassifier模塊

沿用上一篇的例子。此處的問題是垃圾郵件的分類，監督學習。

1. Random Forest + KFold

import nltk
import pandas as pd
import re
from sklearn.feature_extraction.text import TfidfVectorizer
import string

stopwords = nltk.corpus.stopwords.words('english')
ps = nltk.PorterStemmer()

data = pd.read_csv("SMSSpamCollection.tsv", sep='\t')
data.columns = ['label', 'body_text']

def count_punct(text):
    count = sum([1 for char in text if char in string.punctuation])
    return round(count/(len(text) - text.count(" ")), 3)*100

data['body_len'] = data['body_text'].apply(lambda x: len(x) - x.count(" "))
data['punct%'] = data['body_text'].apply(lambda x: count_punct(x))

def clean_text(text):
    text = "".join([word.lower() for word in text if word not in string.punctuation])
    tokens = re.split('\W+', text)
    text = [ps.stem(word) for word in tokens if word not in stopwords]
    return text

tfidf_vect = TfidfVectorizer(analyzer=clean_text)
X_tfidf = tfidf_vect.fit_transform(data['body_text'])

X_features = pd.concat([data['body_len'], data['punct%'], pd.DataFrame(X_tfidf.toarray())], axis=1)
X_features.head()

接下來建立模型。

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import KFold, cross_val_score

rf = RandomForestClassifier(n_jobs=-1)  # parallel building
kfold = KFold(n_splits=10)
cross_val_score(rf,X_features,data["label"],cv=kfold,scoring="accuracy",n_jobs=-1)

2. Holdout Test Set Evaluation

from sklearn.metrics import precision_recall_fscore_support as score
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier

xTrain, xTest, yTrain, yTest = train_test_split(X_features, data.label, test_size=0.2)

rf = RandomForestClassifier(n_estimators=50, max_depth=20, n_jobs=-1)
rf_model = rf.fit(xTrain, yTrain)

# find out the most important features with respect to the model
sorted(zip(rf_model.feature_importances_, xTrain.columns), reverse=True)[:5]

y_pred = rf_model.predict(xTest)
precision, recall, fscore, support = score(yTest, y_pred, pos_label = "spam", average = "binary")

print('precision: {} / recall: {} / accuracy: {}'.format(precision, recall, (y_pred==yTest).sum()/len(y_pred)))

3. Grid Search + Model Evaluation

手動實現一個簡易的網格搜索。

 def train_RF(n_est, depth):
    rf = RandomForestClassifier(n_estimators=n_est, max_depth=depth, n_jobs=-1)
    rf_model = rf.fit(X_train, y_train)
    y_pred = rf_model.predict(X_test)
    prec, recall, fscore, sup = score(y_test, y_pred, pos_label="spam", average="binary")
    print("Est:{}/Dpeth:{}\nprecision:{}/recall:{}/accur:{}".format(n_est, depth, prec, recall, (y_pred==y_test).sum()/len(y_pred)))
        

for n_est in [10,20,50]:
    for depth in range(10,40,10):
        train_RF(n_est, depth)

調用sklearn自帶的方法。

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV

rf = RandomForestClassifier()
param = {'n_estimators':[10,150,300],
        'max_depth':[30,60,90,None]}
gs = GridSearchCV(rf, param, cv=5, n_jobs=-1)
gs_fit = gs.fit(X_tfidf_feat, data["label"])
pd.DataFrame(gs_fit.cv_results_).sort_values("mean_test_score", ascending=False)[:5]

 本機跑GridSearchCV的時候報了Memory Error錯誤。解決方法有別的博客講解，建議增大虛擬內存，具體操作在此不贅述。

4. Gradient Boost

定義：Ensemble learning method that takes an iterative approach to combining weak learners to create a strong learner by focusing on mistakes of prior iterations. Decision tree based. 

與RF的區別：

RF:

1. Bagging, so training can be done in parallel.
2. Unweighted voting for final prediction. 
3. Easier to tune, harder to overfit. 

Gradient Boosting:

1. Boosting, so training must be done iteratively.
2. Weighted voting for final prediction.
3. Harder to tune, easier to overfit. 

Tradeoffs of GB:

pros:

1. powerful
2. accepts various types of inputs
3. can be used for classification or regression
4. outputs feature importance

Cons:

1. longer to train
2. more likely to overfit
3. more difficult to properly tune

from sklearn.ensemble import GradientBoostingClassifier
from sklearn.model_selection import GridSearchCV

gb = GradientBoostingClassifier()
param = {
    "n_estimators":[100, 150],
    "max_depth":[7, 11, 15],
    "learning_rate":[0.1]
}
gs = GridSearchCV(gb, param, cv = 5, n_jobs=-1)
cv_fit = gs.fit(X_tfidf_feat, data.label)
pd.DataFrame(cv_fit.cv_results_).sort_values("mean_test_score", ascending=False)[:5]

5. Pipeline總結

• read in raw text
• clean text and tokenize
• feature engineering
• fit simple model
• tune hyperparameters and evalueate model
• final model selection

Vectorizers should be fit on the training set and only be used to transform the test set. 

Process:

split data into trainig and test set -> train vectorizers on training set and use that to transform test set -> fit best RF and GB model on training set and predict on test set -> evaluate results of two models to select best model 

貼出完整代碼：

import nltk
import pandas as pd
import re
from sklearn.feature_extraction.text import TfidfVectorizer
import string

stopwords = nltk.corpus.stopwords.words('english')
ps = nltk.PorterStemmer()

data = pd.read_csv("SMSSpamCollection.tsv", sep='\t')
data.columns = ['label', 'body_text']

def count_punct(text):
    count = sum([1 for char in text if char in string.punctuation])
    return round(count/(len(text) - text.count(" ")), 3)*100

data['body_len'] = data['body_text'].apply(lambda x: len(x) - x.count(" "))
data['punct%'] = data['body_text'].apply(lambda x: count_punct(x))

def clean_text(text):
    text = "".join([word.lower() for word in text if word not in string.punctuation])
    tokens = re.split('\W+', text)
    text = [ps.stem(word) for word in tokens if word not in stopwords]
    return text

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(data[['body_text', 'body_len', 'punct%']], data['label'], test_size=0.2)

tfidf_vect = TfidfVectorizer(analyzer=clean_text)
tfidf_vect_fit = tfidf_vect.fit(X_train['body_text'])

tfidf_train = tfidf_vect_fit.transform(X_train['body_text'])
tfidf_test = tfidf_vect_fit.transform(X_test['body_text'])

X_train_vect = pd.concat([X_train[['body_len', 'punct%']].reset_index(drop=True), 
           pd.DataFrame(tfidf_train.toarray())], axis=1)
X_test_vect = pd.concat([X_test[['body_len', 'punct%']].reset_index(drop=True), 
           pd.DataFrame(tfidf_test.toarray())], axis=1)

X_train_vect.head()

 

from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import precision_recall_fscore_support as score
import time

#RF model 
rf = RandomForestClassifier(n_estimators=150, max_depth=None, n_jobs=-1)

start = time.time()
rf_model = rf.fit(X_train_vect, y_train)
end = time.time()
fit_time = (end - start)

start = time.time()
y_pred = rf_model.predict(X_test_vect)
end = time.time()
pred_time = (end - start)

precision, recall, fscore, train_support = score(y_test, y_pred, pos_label='spam', average='binary')
print('Fit time: {} / Predict time: {} ---- Precision: {} / Recall: {} / Accuracy: {}'.format(
    round(fit_time, 3), round(pred_time, 3), round(precision, 3), round(recall, 3), round((y_pred==y_test).sum()/len(y_pred), 3)))

#GB model 
gb = GradientBoostingClassifier(n_estimators=150, max_depth=11)

start = time.time()
gb_model = gb.fit(X_train_vect, y_train)
end = time.time()
fit_time = (end - start)

start = time.time()
y_pred = gb_model.predict(X_test_vect)
end = time.time()
pred_time = (end - start)

precision, recall, fscore, train_support = score(y_test, y_pred, pos_label='spam', average='binary')
print('Fit time: {} / Predict time: {} ---- Precision: {} / Recall: {} / Accuracy: {}'.format(
    round(fit_time, 3), round(pred_time, 3), round(precision, 3), round(recall, 3), round((y_pred==y_test).sum()/len(y_pred), 3)))