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trabajo_final_v2.py
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# -*- coding: utf-8 -*-
"""
Created on Thu Jun 7 11:38:53 2018
@author: alber
"""
# Librerias NLP
from nltk.corpus import stopwords
from nltk.stem import SnowballStemmer
import re
import numpy as np
import pandas as pd
import random
import os
from os import listdir
from os.path import isfile, join
import csv
import random
import time
import glob
import pickle
import nltk
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.utils import shuffle
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix, precision_score, accuracy_score, recall_score, f1_score
from sklearn import preprocessing
from sklearn.utils import shuffle
global stemmer
stemmer = SnowballStemmer("english")
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
from keras.preprocessing import sequence
from keras.layers.convolutional import Conv1D
from keras.layers.convolutional import MaxPooling1D
from keras.models import load_model
from keras.utils.np_utils import to_categorical
from keras.models import load_model
# Semilla aleatoria
# Se usa el RNG por defecto: Mersenne Twister
random.seed(0)
np.random.seed(0)
def hasNumbers(inputString):
return any(char.isdigit() for char in inputString)
def resample(df_data, tipo):
#median_ref = int(df_data.groupby(tipo).count().median().values[0])
min_ref = int(df_data.groupby(tipo).count().min().values[0])
median_ref = min_ref
df_data = df_data.sort_values(tipo)
list_cat = list(df_data[tipo].unique())
df_resampled = None
df_train = None
df_test = None
for cat in list_cat:
df_iter = df_data[df_data[tipo] == cat].copy()
df_iter = shuffle(df_iter).reset_index()
del df_iter["index"]
if max(df_iter.index) > median_ref:
df_iter = df_iter[:median_ref]
if type(df_resampled)==type(None):
df_resampled = df_iter
df_train = df_iter[:int(round(0.80*len(df_iter)))]
df_test = df_iter[int(round(0.80*len(df_iter))):]
else:
df_resampled = pd.concat([df_resampled, df_iter])
df_train = pd.concat([df_train, df_iter[:int(round(0.80*len(df_iter)))]])
df_test = pd.concat([df_test, df_iter[int(round(0.80*len(df_iter))):]])
# Histograma nuevo
df_resampled[tipo].hist()
df_train[tipo].hist()
df_test[tipo].hist()
print(len(df_train["posts"].max())) # Se ve que los tamaños de textos son mas o menos ctes
print(len(df_train["posts"].min()))
return df_resampled, df_train, df_test
def bow_build(df_train):
#### Construccion de la BOW
lines = list(df_train["posts"])
# Generar vocabulario
words = []
vocabulario = {}
# Primer filtrado de caracteres no utiles
for line in lines:
# Elimino links
line = re.sub(r"http\S+", "", line)
# Elimino retweets
line = re.sub(r"RT", "", line)
# Elimino otros caracteres especiales
line = line.replace("[", "")
line = line.replace("]", "")
line = line.replace(",", "")
line = line.replace('"', '')
line = line.replace('|', ' ')
documento = lines
i = 0
# Hago la tokenizacion
for utterance in documento:
i += 1
print("iteracion {0}/{1}".format(i, len(documento)))
# Tokenizo cada frase
w = re.findall(r'\w+', utterance.lower(),flags = re.UNICODE) # Paso a minusculas todo
# La añado a la lista
words.extend(w)
# Uno en una palabra las named_entities
tokens = words
_tokens_bef = tokens
tokens = []
for chunk in nltk.ne_chunk(nltk.pos_tag(_tokens_bef)):
if hasattr(chunk, 'label'):
tokens.append('_'.join([c[0] for c in chunk]))
else:
tokens.append(chunk[0])
words = tokens
# Eliminación de las stop_words
words = [word for word in words if word not in stopwords.words('english')]
# Elimino guiones y otros simbolos raros
words = [word for word in words if not word.isdigit()] # Elimino numeros
# Elimino lo que no sea un valor a-zA-Z
words = [word for word in words if not hasNumbers(word)]
words = [re.sub(r'[^a-zA-Z]', "", word) for word in words]
words = list(filter(None, words)) # Quito las lineas vacias
# Stemming y eliminación de duplicados
words = [stemmer.stem(w) for w in words]
# Inicializo la bolsa de palabras
pattern_words = words
# Vocabulario total sin filtrar
words_totales = sorted(list(set(pattern_words)))
# if len(words)==0:
# words.append('UNK')
# words_glob.extend(words)
# continue
i = 0
df_pattern = pd.DataFrame(pattern_words)
df_pattern['ocurrencias'] = 1
df_pattern['documento'] = i
df_pattern.columns = ['palabras', 'ocurrencias', 'documento']
df_pattern = df_pattern.groupby(['palabras', 'documento'])['ocurrencias'].sum() # En este pundo, al pasarlo a indices, se ordenan
df_pattern = df_pattern.reset_index()
words_glob = []
df_pattern_glob = pd.DataFrame()
# Creo Vocabulario
words = sorted(list(set(words))) # Ordeno alfabéticamente y elimino duplicados
words.append('UNK') # Palabra por defecto para las palabras desconocidas
# Pongo las palabras en la lista global de vocabulario
words_glob.extend(words)
if df_pattern_glob.empty:
df_pattern_glob = df_pattern.copy()
else:
df_pattern_glob = df_pattern_glob.append(df_pattern)
# Se eliminan de nuevo las palabras duplicadas
words_glob = sorted(list(set(words_glob)))
# Agrego las frecuencias de palabras que se han ido obteniendo
df_pattern_glob = pd.DataFrame(df_pattern_glob)
df_pattern_glob = df_pattern_glob.reset_index()
df_pattern_glob_tot = df_pattern_glob.groupby(["palabras"])["ocurrencias"].sum().copy()
#df_pattern_glob.mean()
#df_pattern_glob.std()
#df_pattern_glob.median() # Sale 2, con lo que se ve que casi todas las palabras tienen poca frecuencia
#df_pattern_glob = df_pattern_glob.sort_values(ascending = False)
# Elimino las palabras que tengan mucha/poca frecuencia
l_sup = df_pattern_glob_tot.mean() + 2*df_pattern_glob_tot.std()
df_pattern_glob_tot = df_pattern_glob_tot[df_pattern_glob_tot < l_sup] # Elimino la cola de mas del 95%
df_pattern_glob_tot = df_pattern_glob_tot[df_pattern_glob_tot > 10] # Elimino las palabras que aparecen menos de 10 veces
#from scipy.stats import normaltest
#normaltest([0,1,2,2,3,3,3,4,4,5,6])
words_glob = sorted(set(list(pd.DataFrame(df_pattern_glob_tot).
reset_index()['palabras']))) # Palabras ya filtradas
words_glob.append('UNK')
words_tot_glob = df_pattern_glob_tot.to_dict()
words_tot_glob['UNK'] = 0
return words_glob, df_train, words_totales
def crear_vocabulario():
## Lectura del documento
try:
#files_path = glob.glob(os.path.abspath('') + '/mbti-myers-briggs-personality-type-dataset/*')
files_path = glob.glob(os.path.abspath('') + '/mbti-myers-briggs-personality-type-dataset/mbti_1.csv')
except:
pass
df_data = None
for f in files_path:
if type(df_data)==type(None):
df_data = pd.read_csv(f, engine='python')
else:
df_data = pd.concat([df_data, pd.read_csv(f)], engine='python')
df_subtypes = df_data["type"].apply(lambda x: pd.Series(list(x)))
df_subtypes = df_subtypes.rename(columns={0: "energy", 1: "information", 2: "decision", 3: "lifestyle"})
df_subtypes = df_subtypes.reset_index()
df_subtypes = df_subtypes.rename(columns={'index':'user_id'})
df_data = df_data.reset_index()
df_data = df_data.rename(columns={'index':'user_id'})
df_data = df_data.merge(df_subtypes, left_on='user_id', right_on='user_id', how='inner')
# Data descriptive analysis
df_data["type"].hist()
df_data.groupby("type").count()
df_data.groupby("type").count().median() # Valor maximo que va a tener de datos cada clase
df_data.groupby("type").count().mean()
df_data.groupby("type").count().std()
df_data["energy"].hist()
df_data.groupby("energy").count()
df_data.groupby("energy").count().median() # Valor maximo que va a tener de datos cada clase
df_data.groupby("energy").count().mean()
df_data.groupby("energy").count().std()
df_data["information"].hist()
df_data.groupby("information").count()
df_data.groupby("information").count().median() # Valor maximo que va a tener de datos cada clase
df_data.groupby("information").count().mean()
df_data.groupby("information").count().std()
df_data["decision"].hist()
df_data.groupby("decision").count()
df_data.groupby("decision").count().median() # Valor maximo que va a tener de datos cada clase
df_data.groupby("decision").count().mean()
df_data.groupby("decision").count().std()
df_data["lifestyle"].hist()
df_data.groupby("lifestyle").count()
df_data.groupby("lifestyle").count().median() # Valor maximo que va a tener de datos cada clase
df_data.groupby("lifestyle").count().mean()
df_data.groupby("lifestyle").count().std()
# Resampling aleatorio de datos hasta ese maximo de la mediana
"""
Tambien aprovecho para hacer el train/test split
"""
df_resampled_e, df_train_e, df_test_e = resample(df_data, "energy")
df_resampled_i, df_train_i, df_test_i = resample(df_data, "information")
df_resampled_d, df_train_d, df_test_d = resample(df_data, "decision")
df_resampled_l, df_train_l, df_test_l = resample(df_data, "lifestyle")
words_glob_e, df_train_e, words_totales_e = bow_build(df_train_e)
words_glob_i, df_train_i, words_totales_i = bow_build(df_train_i)
words_glob_d, df_train_d, words_totales_d = bow_build(df_train_d)
words_glob_l, df_train_l, words_totales_l = bow_build(df_train_l)
vocabulario = [{'corpus':{"energy": words_glob_e, "information": words_glob_i, "decision":words_glob_d, "lifestyle":words_glob_l},
'test_files':{"energy": df_test_e, "information": df_test_i, "decision": df_test_d, "lifestyle": df_test_l},
'train_files':{"energy": df_train_e, "information": df_train_i, "decision": df_train_d, "lifestyle": df_train_l},
'corpus_total':{"energy": words_totales_e, "information": words_totales_i, "decision": words_totales_d, "lifestyle": words_totales_l}}]
# Se guarda el vocabulario global en disco
with open(os.path.abspath('') + r'/generated_data_2/vocabulario.p', 'wb') as f:
pickle.dump(vocabulario, f)
return vocabulario
def cargar_corpus():
with open(os.path.abspath('') + r'/generated_data_2/vocabulario.p', 'rb') as f:
vocabulario = pickle.load(f)
return vocabulario
def cargar_train(tipo):
with open(os.path.abspath('') + r'/generated_data_2/train_files_'+ tipo +'.p', 'rb') as f:
train_f = pickle.load(f)
return train_f
def cargar_test(tipo):
with open(os.path.abspath('') + r'/generated_data_2/test_files_'+ tipo +'.p', 'rb') as f:
test_f = pickle.load(f)
return test_f
def cargar_train_rnn(tipo):
with open(os.path.abspath('') + r'/generated_data_2/train_files_rnn_'+ tipo +'.p', 'rb') as f:
train_f = pickle.load(f)
return train_f
def cargar_test_rnn(tipo):
with open(os.path.abspath('') + r'/generated_data_2/test_files_rnn_'+ tipo +'.p', 'rb') as f:
test_f = pickle.load(f)
return test_f
def train_generation(vocabulario, tipo):
train_files = vocabulario[0]['train_files'][tipo]
corpus = vocabulario[0]['corpus'][tipo]
X = []
user_id = list(train_files["user_id"])
### One-hot encoding labels
y = list(train_files[tipo])
labelencoder_X = LabelEncoder()
onehotencoder = OneHotEncoder()
y = labelencoder_X.fit_transform(y)
y = y.reshape(-1, 1)
y = onehotencoder.fit_transform(y).toarray()
# Remove dummy variable trap
y = y[:, 1:] # Elimino una de las columnas por ser linearmente dependiente de las demas
### Train text encoding
lines = list(train_files["posts"])
# Primer filtrado de caracteres no utiles
for line in lines:
# Elimino links
line = re.sub(r"http\S+", "", line)
# Elimino retweets
line = re.sub(r"RT", "", line)
# Elimino otros caracteres especiales
line = line.replace("[", "")
line = line.replace("]", "")
line = line.replace(",", "")
line = line.replace('"', '')
line = line.replace('|', ' ')
utterances = lines
for text in utterances:
# Tokenizo cada frase
w = re.findall(r'\w+', text.lower(),flags = re.UNICODE) # Paso a minusculas todo
words = w
# Uno en una palabra las named_entities
tokens = words
_tokens_bef = tokens
tokens = []
for chunk in nltk.ne_chunk(nltk.pos_tag(_tokens_bef)):
if hasattr(chunk, 'label'):
tokens.append('_'.join([c[0] for c in chunk]))
else:
tokens.append(chunk[0])
words = tokens
# Eliminación de las stop_words
words = [word for word in words if word not in stopwords.words('english')]
# Elimino guiones y otros simbolos raros
words = [word for word in words if not word.isdigit()] # Elimino numeros
# Elimino lo que no sea un valor a-zA-Z
words = [word for word in words if not hasNumbers(word)]
words = [re.sub(r'[^a-zA-Z]', "", word) for word in words]
words = list(filter(None, words)) # Quito las lineas vacias
# Stemming
words = [stemmer.stem(w) for w in words]
# Pongo como UNK las que no estan en el vocabulario - flattening
words = [x if x in corpus else 'UNK' for x in words]
sentence = [0]*len(corpus)
for t in words:
if t in corpus:
idx = corpus.index(t)
sentence[idx] += 1
# Pongo a 1 las frecuencias de los UNK para no influir demasiado
sentence[-1] = 1
# Guardo la frase final
X.append(np.array(sentence)) # lo guardo como un numpy array
train_f = {"X":X, "y":y, "y_original": list(train_files[tipo]), 'user_id': user_id}
# Se guarda el vocabulario global en disco
with open(os.path.abspath('') + r'/generated_data_2/train_files_'+ tipo +'.p', 'wb') as f:
pickle.dump(train_f, f)
return X, y
def test_generation(vocabulario, tipo):
test_files = vocabulario[0]['test_files'][tipo]
corpus = vocabulario[0]['corpus'][tipo]
X = []
user_id = list(test_files["user_id"])
### One-hot encoding labels
y = list(test_files[tipo])
labelencoder_X = LabelEncoder()
onehotencoder = OneHotEncoder()
y = labelencoder_X.fit_transform(y)
y = y.reshape(-1, 1)
y = onehotencoder.fit_transform(y).toarray()
# Remove dummy variable trap
y = y[:, 1:] # Elimino una de las columnas por ser linearmente dependiente de las demas
### Train text encoding
lines = list(test_files["posts"])
# Primer filtrado de caracteres no utiles
for line in lines:
# Elimino links
line = re.sub(r"http\S+", "", line)
# Elimino retweets
line = re.sub(r"RT", "", line)
# Elimino otros caracteres especiales
line = line.replace("[", "")
line = line.replace("]", "")
line = line.replace(",", "")
line = line.replace('"', '')
line = line.replace('|', ' ')
utterances = lines
for text in utterances:
# Tokenizo cada frase
w = re.findall(r'\w+', text.lower(),flags = re.UNICODE) # Paso a minusculas todo
words = w
# Uno en una palabra las named_entities
tokens = words
_tokens_bef = tokens
tokens = []
for chunk in nltk.ne_chunk(nltk.pos_tag(_tokens_bef)):
if hasattr(chunk, 'label'):
tokens.append('_'.join([c[0] for c in chunk]))
else:
tokens.append(chunk[0])
words = tokens
# Eliminación de las stop_words
words = [word for word in words if word not in stopwords.words('english')]
# Elimino guiones y otros simbolos raros
words = [word for word in words if not word.isdigit()] # Elimino numeros
# Elimino lo que no sea un valor a-zA-Z
words = [word for word in words if not hasNumbers(word)]
words = [re.sub(r'[^a-zA-Z]', "", word) for word in words]
words = list(filter(None, words)) # Quito las lineas vacias
# Stemming
words = [stemmer.stem(w) for w in words]
# Pongo como UNK las que no estan en el vocabulario - flattening
words = [x if x in corpus else 'UNK' for x in words]
sentence = [0]*len(corpus)
for t in words:
if t in corpus:
idx = corpus.index(t)
sentence[idx] += 1
# Pongo a 1 las frecuencias de los UNK para no influir demasiado
sentence[-1] = 1
# Guardo la frase final
X.append(np.array(sentence)) # lo guardo como un numpy array
train_f = {"X":X, "y":y, "y_original": list(test_files[tipo]), 'user_id':user_id}
# Se guarda el vocabulario global en disco
with open(os.path.abspath('') + r'/generated_data_2/test_files_'+ tipo +'.p', 'wb') as f:
pickle.dump(train_f, f)
return X, y
def train_model(model_type, tipo, X, y):
# d_train= cargar_train()
# X = d_train["X"]
# y = d_train["y"]
#
# Train/validation split
X_train, X_val, y_train, y_val = train_test_split(X, y, test_size = 0.1, random_state = 0)
if model_type == "random_forest":
# Fitting Random Forest Classificator to the Training set
classifier = RandomForestClassifier(n_estimators = 100, criterion = 'entropy', random_state = 0)
classifier.fit(X_train, y_train)
print("Entrenamiento terminado")
# Predicting the Test set results
y_pred = classifier.predict(X_val)
# Deshago el onehot encoding para sacar las metricas
y_val = pd.DataFrame(y_val)
y_pred = pd.DataFrame(y_pred)
y_val = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_val.values]
y_pred = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_pred.values]
elif model_type == "naive_bayes":
##### Naive-Bayes
"""
Resultados muy malos
"""
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_val = sc.transform(X_val)
y_train = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_train.tolist()]
# Fitting classifier to the Training set
from sklearn.naive_bayes import GaussianNB
classifier = GaussianNB() #No tiene argumentos de input
classifier.fit(X_train, y_train)
y_pred = list(classifier.predict(X_val))
# Making the Confusion Matrix
y_val = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_val]
cm = confusion_matrix(y_val, y_pred)
# Accuracy
accuracy = accuracy_score(y_val, y_pred)
# Precision
average_precision = precision_score(y_val, y_pred, average = "weighted")
# Recall
recall = recall_score(y_val, y_pred, average='weighted')
# F1
f1 = f1_score(y_val, y_pred, average='weighted', labels=np.unique(y_pred))
print("Modelo - resultados")
print("accuracy ", accuracy, " precision ", average_precision, " recall ", recall, " f1 ", f1)
elif model_type == "kernel_svm":
### Kernel SVM
"""
kernel rbf = Mejora un poco al Naive
kernel poly = Peor que rbf
kernel linear = Mejores resultados que los anteriores!
kernel sigmoid = Mejores resultados de todos!!!
"""
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_val = sc.transform(X_val)
y_train = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_train.tolist()]
# Fitting classifier to the Training set
from sklearn.svm import SVC
# classifier = SVC(kernel = 'rbf', random_state = 0)
# classifier = SVC(kernel = 'linear', random_state = 0)
# classifier = SVC(kernel = 'poly', random_state = 0)
classifier = SVC(kernel = 'sigmoid', random_state = 0)
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = list(classifier.predict(X_val))
y_pred = [int(x) for x in y_pred]
y_val = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_val.tolist()]
y_val = [int(x) for x in y_val]
# Making the Confusion Matrix
cm = confusion_matrix(y_val, y_pred)
# Accuracy
accuracy = accuracy_score(y_val, y_pred)
# Precision
average_precision = precision_score(y_val, y_pred, average = "weighted")
# Recall
recall = recall_score(y_val, y_pred, average='weighted')
# F1
f1 = f1_score(y_val, y_pred, average='weighted', labels=np.unique(y_pred))
print("Modelo - resultados")
print("accuracy ", accuracy, " precision ", average_precision, " recall ", recall, " f1 ", f1)
# del classifier
# classifier = SVC(kernel = 'sigmoid', random_state = 0)
# X = sc.fit_transform(X)
# y = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y.tolist()]
# classifier.fit(X, y)
elif model_type == "knn":
### KNN
"""
Peor que el SVM
"""
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_val = sc.transform(X_val)
y_train = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_train.tolist()]
# Fitting classifier to the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 50, metric = 'minkowski', p = 7) #Defino que me interesa la distancia Euclidea
classifier.fit(X_train, y_train)
# Predicting the Test set results
y_pred = list(classifier.predict(X_val))
y_pred = [int(x) for x in y_pred]
y_val = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_val.tolist()]
y_val = [int(x) for x in y_val]
# Making the Confusion Matrix
cm = confusion_matrix(y_val, y_pred)
# Accuracy
accuracy = accuracy_score(y_val, y_pred)
# Precision
average_precision = precision_score(y_val, y_pred, average = "weighted")
# Recall
recall = recall_score(y_val, y_pred, average='weighted')
# F1
f1 = f1_score(y_val, y_pred, average='weighted', labels=np.unique(y_pred))
print("Modelo - resultados")
print("accuracy ", accuracy, " precision ", average_precision, " recall ", recall, " f1 ", f1)
elif model_type == "linear_svc":
### LinearSVC
"""
Resultados igual de buenos, mas o menos, que KernelSVM
"""
from sklearn.svm import LinearSVC
y_train = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_train.tolist()]
classifier = LinearSVC(random_state=0)
classifier.fit(X_train, y_train)
print(classifier.coef_)
print(classifier.intercept_)
y_pred = classifier.predict(X_val)
y_pred = [int(x) for x in y_pred]
y_val = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_val.tolist()]
y_val = [int(x) for x in y_val]
# Making the Confusion Matrix
cm = confusion_matrix(y_val, y_pred)
# Accuracy
accuracy = accuracy_score(y_val, y_pred)
# Precision
average_precision = precision_score(y_val, y_pred, average = "weighted")
# Recall
recall = recall_score(y_val, y_pred, average='weighted')
# F1
f1 = f1_score(y_val, y_pred, average='weighted', labels=np.unique(y_pred))
print("Modelo - resultados")
print("accuracy ", accuracy, " precision ", average_precision, " recall ", recall, " f1 ", f1)
# Persistencia del modelo entrenado
with open(os.path.abspath('') + r'/generated_data_2/model_'+ model_type +'_'+ tipo + '_t.p', 'wb') as handle:
pickle.dump(classifier, handle)
return classifier
def test_model(model_type, tipo, X_test, y_test):
# Carga del modelo entrenado
with open(os.path.abspath('') + r'/generated_data_2/model_'+ model_type +'_'+ tipo + '_t.p', 'rb') as handle:
classifier = pickle.load(handle)
if model_type == "random_forest":
pass
elif model_type == "naive_bayes":
##### Naive-Bayes
"""
Resultados muy malos
"""
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_test = sc.fit_transform(X_test)
y_test = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_test.tolist()]
# Predictions
y_pred = list(classifier.predict(X_test))
# Making the Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
# Accuracy
accuracy = accuracy_score(y_test, y_pred)
# Precision
average_precision = precision_score(y_test, y_pred, average = "weighted")
# Recall
recall = recall_score(y_test, y_pred, average='weighted')
# F1
f1 = f1_score(y_test, y_pred, average='weighted', labels=np.unique(y_pred))
print("Modelo - resultados")
print("accuracy ", accuracy, " precision ", average_precision, " recall ", recall, " f1 ", f1)
elif model_type == "kernel_svm":
### Kernel SVM
"""
kernel rbf = Mejora un poco al Naive
kernel poly = Peor que rbf
kernel linear = Mejores resultados que los anteriores!
kernel sigmoid = Mejores resultados de todos!!!
"""
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_test = sc.fit_transform(X_test)
y_test = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_test.tolist()]
# Predictions
y_pred = list(classifier.predict(X_test))
# Making the Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
# Accuracy
accuracy = accuracy_score(y_test, y_pred)
# Precision
average_precision = precision_score(y_test, y_pred, average = "weighted")
# Recall
recall = recall_score(y_test, y_pred, average='weighted')
# F1
f1 = f1_score(y_test, y_pred, average='weighted', labels=np.unique(y_pred))
print("Modelo - resultados")
print("accuracy ", accuracy, " precision ", average_precision, " recall ", recall, " f1 ", f1)
elif model_type == "knn":
### KNN
"""
Peor que el SVM
"""
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_test = sc.fit_transform(X_test)
y_test = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_test.tolist()]
# Predictions
y_pred = list(classifier.predict(X_test))
# Making the Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
# Accuracy
accuracy = accuracy_score(y_test, y_pred)
# Precision
average_precision = precision_score(y_test, y_pred, average = "weighted")
# Recall
recall = recall_score(y_test, y_pred, average='weighted')
# F1
f1 = f1_score(y_test, y_pred, average='weighted', labels=np.unique(y_pred))
print("Modelo - resultados")
print("accuracy ", accuracy, " precision ", average_precision, " recall ", recall, " f1 ", f1)
elif model_type == "linear_svc":
### LinearSVC
"""
Resultados igual de buenos, mas o menos, que KernelSVM
"""
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_test = sc.fit_transform(X_test)
y_test = [(np.argmax(np.asarray(x)) + 1) if max(np.asarray(x)) > 0 else 0.0 for x in y_test.tolist()]
# Predictions
y_pred = list(classifier.predict(X_test))
# Making the Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
# Accuracy
accuracy = accuracy_score(y_test, y_pred)
# Precision
average_precision = precision_score(y_test, y_pred, average = "weighted")
# Recall
recall = recall_score(y_test, y_pred, average='weighted')
# F1
f1 = f1_score(y_test, y_pred, average='weighted', labels=np.unique(y_pred))
print("Modelo - resultados")
print("accuracy ", accuracy, " precision ", average_precision, " recall ", recall, " f1 ", f1)
return cm, accuracy, average_precision, recall, f1
# Cargar corpus y crear sets
vocabulario = cargar_corpus()
train_generation(vocabulario, "energy")
train_generation(vocabulario, "information")
train_generation(vocabulario, "decision")
train_generation(vocabulario, "lifestyle")
test_generation(vocabulario, "energy")
test_generation(vocabulario, "information")
test_generation(vocabulario, "decision")
test_generation(vocabulario, "lifestyle")
#X_e = train_files_e['X']
#for i in X_e:
# i[-1] = 1
#y_e = train_files_e['y']
#
#X_i = train_files_i['X']
#for i in X_i:
# i[-1] = 1
#y_i = train_files_i['y']
#
#X_d = train_files_d['X']
#for i in X_d:
# i[-1] = 1
#y_d = train_files_d['y']
#
#X_l = train_files_l['X']
#for i in X_l:
# i[-1] = 1
#y_l = train_files_l['y']
### Train
train_files_e = cargar_train("energy")
train_files_i = cargar_train("information")
train_files_d = cargar_train("decision")
train_files_l = cargar_train("lifestyle")
X_e = train_files_e['X']
y_e = train_files_e['y']
user_id_e = train_files_e['user_id']
X_i = train_files_i['X']
y_i = train_files_i['y']
user_id_i = train_files_i['user_id']
X_d = train_files_d['X']
y_d = train_files_d['y']
user_id_d = train_files_d['user_id']
X_l = train_files_l['X']
y_l = train_files_l['y']
user_id_l = train_files_l['user_id']
classifier_e = train_model("kernel_svm", "energy", X_e, y_e)
classifier_i = train_model("kernel_svm", "information", X_i, y_i)
classifier_d = train_model("kernel_svm", "decision", X_d, y_d)
classifier_l = train_model("kernel_svm", "lifestyle", X_l, y_l)
#### Test
test_files_e = cargar_test("energy")
test_files_i = cargar_test("information")
test_files_d = cargar_test("decision")
test_files_l = cargar_test("lifestyle")
X_e = test_files_e['X']
y_e = test_files_e['y']
user_id_e = test_files_e['user_id']
X_i = test_files_i['X']
y_i = test_files_i['y']
user_id_i = test_files_i['user_id']
X_d = test_files_d['X']
y_d = test_files_d['y']
user_id_d = test_files_d['user_id']
X_l = test_files_l['X']
y_l = test_files_l['y']
user_id_l = test_files_l['user_id']
cm, accuracy, average_precision, recall, f1 = test_model("kernel_svm", "energy", X_e, y_e)
cm, accuracy, average_precision, recall, f1 = test_model("kernel_svm", "information", X_i, y_i)
cm, accuracy, average_precision, recall, f1 = test_model("kernel_svm", "decision", X_d, y_d)
cm, accuracy, average_precision, recall, f1 = test_model("kernel_svm", "lifestyle", X_l, y_l)
##### RNN
def create_data_train_rnn(vocabulario, tipo):
# vocabulario = cargar_corpus()
train_files = vocabulario[0]['train_files'][tipo]
corpus_total = vocabulario[0]['corpus'][tipo]
X = []
user_id = list(train_files["user_id"])
# corpus_total = vocabulario[0]["corpus_total"]
# train_files = vocabulario[0]['train_files']
word2idx = {'START': 0, 'END': 1} # Start/End Tokens. Inicialmente así mi frase es: START END
current_idx = 2 # El indice comenzará desde la posición 2
X = []
Y = []
lines = list(train_files["posts"])
Y = list(train_files[tipo])
# Primer filtrado de caracteres no utiles
for line in lines:
# Elimino links
line = re.sub(r"http\S+", "", line)
# Elimino retweets
line = re.sub(r"RT", "", line)
# Elimino otros caracteres especiales
line = line.replace("[", "")
line = line.replace("]", "")
line = line.replace(",", "")
line = line.replace('"', '')
line = line.replace('|', ' ')
utterances = lines
i = 1
for text in utterances:
print("iteracion: {0}/{1}".format(i, len(utterances)))
# Tokenizo cada frase
w = re.findall(r'\w+', text.lower(),flags = re.UNICODE) # Paso a minusculas todo
words = w
# Uno en una palabra las named_entities
tokens = words
_tokens_bef = tokens
tokens = []
for chunk in nltk.ne_chunk(nltk.pos_tag(_tokens_bef)):
if hasattr(chunk, 'label'):
tokens.append('_'.join([c[0] for c in chunk]))
else:
tokens.append(chunk[0])
words = tokens
# Eliminación de las stop_words
words = [word for word in words if word not in stopwords.words('english')]
# Elimino guiones y otros simbolos raros
words = [word for word in words if not word.isdigit()] # Elimino numeros
# Elimino lo que no sea un valor a-zA-Z
words = [word for word in words if not hasNumbers(word)]
words = [re.sub(r'[^a-zA-Z]', "", word) for word in words]
words = list(filter(None, words)) # Quito las lineas vacias
# Stemming
words = [stemmer.stem(w) for w in words]
# Pongo como UNK las palabras que no están en mi vocabulario
tokens = [x if x in corpus_total else 'UNK' for x in tokens]
sentence = []
for t in tokens:
if t not in word2idx:
word2idx[t] = current_idx
current_idx += 1 # El índice lo voy aumentando a medida que añado tokens
idx = word2idx[t]
sentence.append(idx)