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mlp_classifier.py
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mlp_classifier.py
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import nltk
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn.neural_network import MLPClassifier
from utils import read_test_data, convert_to_csv, read_train_data
def cross_validate(clf, X_train, y_train, cv):
"""
Helper method to cross validate
:param clf: classifier
:param X_train:
:param y_train:
:param cv:
:return: void, prints the accuracies
"""
accuracies = cross_val_score(clf, X_train, y_train, scoring='accuracy', cv=cv)
print(accuracies)
def generate_results(res, classes_name):
"""
Converts the predictions to csv and creates the 'output.csv' file in the resources folder
:param res:
:param classes_name:
:return: void
"""
toOutput = []
for i in range(len(res)):
toOutput.append({'Id': i, 'Category': classes_name[res[i]]})
convert_to_csv(toOutput)
def preprocess(s):
"""
Preprocessor to stem words
:param s:
:return: stemmed words
"""
ps = nltk.PorterStemmer()
return ps.stem(s)
def grid_search(mlp, X_train, label_numbers):
"""
Performs a grid search to find the best parameters
:param mlp: model
:param X_train: train data
:param label_numbers: labels in numbered format
:return: void, prints the best parameters
"""
parameter_space = {
'hidden_layer_sizes': [(50, 50, 50), (50, 100, 50), (100,)],
'activation': ['tanh', 'relu'],
'solver': ['sgd', 'adam'],
'alpha': [0.0001, 0.05],
'learning_rate': ['constant', 'adaptive'],
}
clf = GridSearchCV(mlp, parameter_space, n_jobs=-1, cv=3)
clf.fit(X_train, label_numbers)
print('Best parameters found:', clf.best_params_)
def fit_predict(X_train, label_nums, test_data, classes_name, vectorizer):
"""
Fits and makes the class predictions using a MLP classifier
:param X_train:
:param label_nums: The labels numbered
:param test_data: Data for which we want to make predictions
:param classes_name:
:param vectorizer:
:return: predictions
"""
clf = MLPClassifier(verbose=True, early_stopping=True, activation='tanh', learning_rate='adaptive')
clf.fit(X_train, label_nums)
pred = clf.predict(vectorizer.transform(test_data))
generate_results(pred, classes_name)
return pred
if __name__ == "__main__":
train_data = read_train_data()
vectorizer = TfidfVectorizer(stop_words='english')
vectorizer.fit(train_data[0])
X_train = vectorizer.transform(train_data[0])
test_data = read_test_data()
label_numbers = []
result = train_data[1]
classes_name, classes_count = np.unique(result, return_counts=True)
for i in range(len(result)):
label_numbers.append(np.where(classes_name == result[i])[0][0])
label_numbers = np.asarray(label_numbers)
predictions = fit_predict(X_train, label_numbers, test_data, classes_name, vectorizer)
# cross_validate(MLPClassifier(verbose=True, early_stopping=True), X_train, result, 3)
# grid_search(MLPClassifier(verbose=True, early_stopping=True), X_train, label_numbers)