I have a problem where I am tasked with creating three classifiers (two "out of box", one "optimized") for predicting sentiment analysis using sklearn.
The instructions are to:
Steps 1-3 are no problem and quite frankly work well, the issue is using model.predict(). I am using sklearn's TfidfVectorizer, which creates a feature vector from text. My issue lies in that the feature vector I create for the training set is different than the training vector that is created for the testing set, since the text that is being provided is different.
Below is an example from the train.tsv file...
4|z8DDztUxuIoHYHddDL9zQ|So let me set the scene first, My church social group took a trip here last saturday. We are not your mothers church. The churhc is Community Church of Hope, We are the valleys largest GLBT church so when we desended upon Organ stop Pizza, in LDS land you know we look a little out of place. We had about 50 people from our church come and boy did we have fun. There was a baptist church a couple rows down from us who didn't see it coming. Now we aren't a bunch of flamers frolicking around or anything but we do tend to get a little loud and generally have a great time. I did recognized some of the music so I was able to sing along with those. This is a great place to take anyone over 50. I do think they might be washing dirtymob money or something since the business is cash only.........which I think caught a lot of people off guard including me. The show starts at 530 so dont be late !!!!!!
:-----:|:-----:|:-----:
2|BIeDBg4MrEd1NwWRlFHLQQ|Decent but terribly inconsistent food. I've had some great dishes and some terrible ones, I love chaat and 3 out of 4 times it was great, but once it was just a fried greasy mess (in a bad way, not in the good way it usually is.) Once the matar paneer was great, once it was oversalted and the peas were just plain bad. I don't know how they do it, but it's a coinflip between good food and an oversalted overcooked bowl. Either way, portions are generous.
4|NJHPiW30SKhItD5E2jqpHw|Looks aren't everything....... This little divito looks a little scary looking, but like I've said before "you can't judge a book by it's cover". Not necessarily the kind of place you will take your date (unless she's blind and hungry), but man oh man is the food ever good! We have ordered breakfast, lunch, & dinner, and it is all fantastico. They make home-made corn tortillas and several salsas. The breakfast burritos are out of this world and cost about the same as a McDonald's meal. We are a family that eats out frequently and we are frankly tired of pretty places with below average food. This place is sure to cure your hankerin for a tasty Mexican meal.
2|nnS89FMpIHz7NPjkvYHmug|Being a creature of habit anytime I want good sushi I go to Tokyo Lobby. Well, my group wanted to branch out and try something new so we decided on Sakana. Not a fan. And what's shocking to me is this place was packed! The restaurant opens at 5:30 on Saturday and we arrived at around 5:45 and were lucky to get the last open table. I don't get it... Messy rolls that all tasted the same. We ordered the tootsie roll and the crunch roll, both tasted similar, except of course for the crunchy captain crunch on top. Just a mushy mess, that was hard to eat. Bland tempura. No bueno. I did, however, have a very good tuna poke salad, but I would not go back just for that. If you want good sushi on the west side, or the entire valley for that matter, say no to Sakana and yes to Tokyo Lobby.
2|FYxSugh9PGrX1PR0BHBIw|I recently told a friend that I cant figure out why there is no good Mexican restaurants in Tempe. His response was what about MacAyo's? I responded with "why are there no good Mexican food restaurants in Tempe?" Seriously if anyone out there knows of any legit Mexican in Tempe let me know. And don't say restaurant Mexico!
Here is the train.py file:
import nltk, re, pandas as pd
from nltk.corpus import stopwords
import sklearn, string
import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from itertools import islice
import time
from joblib import dump, load
def ID_to_Num(arr):
le = preprocessing.LabelEncoder()
new_arr = le.fit_transform(arr)
return new_arr
def Num_to_ID(arr):
le = preprocessing.LabelEncoder()
new_arr = le.inverse_transform(arr)
return new_arr
def check_performance(preds, acts):
preds = list(preds)
acts = pd.Series.tolist(acts)
right = 0
total = 0
for i in range(len(preds)):
if preds[i] == acts[i]:
right += 1
total += 1
return (right / total) * 100
# This function removes numbers from an array
def remove_nums(arr):
# Declare a regular expression
pattern = '[0-9]'
# Remove the pattern, which is a number
arr = [re.sub(pattern, '', i) for i in arr]
# Return the array with numbers removed
return arr
# This function cleans the passed in paragraph and parses it
def get_words(para):
# Create a set of stop words
stop_words = set(stopwords.words('english'))
# Split it into lower case
lower = para.lower().split()
# Remove punctuation
no_punctuation = (nopunc.translate(str.maketrans('', '', string.punctuation)) for nopunc in lower)
# Remove integers
no_integers = remove_nums(no_punctuation)
# Remove stop words
dirty_tokens = (data for data in no_integers if data not in stop_words)
# Ensure it is not empty
tokens = [data for data in dirty_tokens if data.strip()]
# Ensure there is more than 1 character to make up the word
tokens = [data for data in tokens if len(data) > 1]
# Return the tokens
return tokens
def minmaxscale(data):
scaler = MinMaxScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
return df_scaled
# This function takes the first n items of a dictionary
def take(n, iterable):
#https://stackoverflow.com/questions/7971618/python-return-first-n-keyvalue-pairs-from-dict
#Return first n items of the iterable as a dict
return dict(islice(iterable, n))
def main():
tsv_file = "filepath"
csv_table=pd.read_csv(tsv_file, sep='\t', header=None)
csv_table.columns = ['class', 'ID', 'text']
s = pd.Series(csv_table['text'])
new = s.str.cat(sep=' ')
vocab = get_words(new)
s = pd.Series(csv_table['text'])
corpus = s.apply(lambda s: ' '.join(get_words(s)))
csv_table['dirty'] = csv_table['text'].str.split().apply(len)
csv_table['clean'] = csv_table['text'].apply(lambda s: len(get_words(s)))
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
df = pd.DataFrame(data=X.todense(), columns=vectorizer.get_feature_names())
result = pd.concat([csv_table, df], axis=1, sort=False)
Y = result['class']
result = result.drop('text', axis=1)
result = result.drop('ID', axis=1)
result = result.drop('class', axis=1)
X = result
mlp = MLPClassifier()
rf = RandomForestClassifier()
mlp_opt = MLPClassifier(
activation = 'tanh',
hidden_layer_sizes = (1000,),
alpha = 0.009,
learning_rate = 'adaptive',
learning_rate_init = 0.01,
max_iter = 250,
momentum = 0.9,
solver = 'lbfgs',
warm_start = False
)
print("Training Classifiers")
mlp_opt.fit(X, Y)
mlp.fit(X, Y)
rf.fit(X, Y)
dump(mlp_opt, "C:\\filepath\Models\\mlp_opt.joblib")
dump(mlp, "C:\\filepath\\Models\\mlp.joblib")
dump(rf, "C:\\filepath\\Models\\rf.joblib")
print("Trained Classifiers")
main()
And here is the Tester.py file:
from nltk.corpus import stopwords
import sklearn, string, nltk, re, pandas as pd, numpy, time
from sklearn.neural_network import MLPClassifier
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split, KFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from joblib import dump, load
def ID_to_Num(arr):
le = preprocessing.LabelEncoder()
new_arr = le.fit_transform(arr)
return new_arr
def Num_to_ID(arr):
le = preprocessing.LabelEncoder()
new_arr = le.inverse_transform(arr)
return new_arr
def check_performance(preds, acts):
preds = list(preds)
acts = pd.Series.tolist(acts)
right = 0
total = 0
for i in range(len(preds)):
if preds[i] == acts[i]:
right += 1
total += 1
return (right / total) * 100
# This function removes numbers from an array
def remove_nums(arr):
# Declare a regular expression
pattern = '[0-9]'
# Remove the pattern, which is a number
arr = [re.sub(pattern, '', i) for i in arr]
# Return the array with numbers removed
return arr
# This function cleans the passed in paragraph and parses it
def get_words(para):
# Create a set of stop words
stop_words = set(stopwords.words('english'))
# Split it into lower case
lower = para.lower().split()
# Remove punctuation
no_punctuation = (nopunc.translate(str.maketrans('', '', string.punctuation)) for nopunc in lower)
# Remove integers
no_integers = remove_nums(no_punctuation)
# Remove stop words
dirty_tokens = (data for data in no_integers if data not in stop_words)
# Ensure it is not empty
tokens = [data for data in dirty_tokens if data.strip()]
# Ensure there is more than 1 character to make up the word
tokens = [data for data in tokens if len(data) > 1]
# Return the tokens
return tokens
def minmaxscale(data):
scaler = MinMaxScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
return df_scaled
# This function takes the first n items of a dictionary
def take(n, iterable):
#https://stackoverflow.com/questions/7971618/python-return-first-n-keyvalue-pairs-from-dict
#Return first n items of the iterable as a dict
return dict(islice(iterable, n))
def main():
tsv_file = "filepath\\dev.tsv"
csv_table=pd.read_csv(tsv_file, sep='\t', header=None)
csv_table.columns = ['class', 'ID', 'text']
s = pd.Series(csv_table['text'])
new = s.str.cat(sep=' ')
vocab = get_words(new)
s = pd.Series(csv_table['text'])
corpus = s.apply(lambda s: ' '.join(get_words(s)))
csv_table['dirty'] = csv_table['text'].str.split().apply(len)
csv_table['clean'] = csv_table['text'].apply(lambda s: len(get_words(s)))
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform(corpus)
df = pd.DataFrame(data=X.todense(), columns=vectorizer.get_feature_names())
result = pd.concat([csv_table, df], axis=1, sort=False)
Y = result['class']
result = result.drop('text', axis=1)
result = result.drop('ID', axis=1)
result = result.drop('class', axis=1)
X = result
mlp_opt = load("C:\\filepath\\Models\\mlp_opt.joblib")
mlp = load("C:\\filepath\\Models\\mlp.joblib")
rf = load("C:\\filepath\\Models\\rf.joblib")
print("Testing Classifiers")
mlp_opt_preds = mlp_opt.predict(X)
mlp_preds = mlp.predict(X)
rf_preds = rf.predict(X)
mlp_opt_performance = check_performance(mlp_opt_preds, Y)
mlp_performance = check_performance(mlp_preds, Y)
rf_performance = check_performance(rf_preds, Y)
print("MLP OPT PERF: {}".format(mlp_opt_performance))
print("MLP PERF: {}".format(mlp_performance))
print("RF PERF: {}".format(rf_performance))
main()
What I end up with is an error:
Testing Classifiers
Traceback (most recent call last):
File "Reader.py", line 121, in <module>
main()
File "Reader.py", line 109, in main
mlp_opt_preds = mlp_opt.predict(X)
File "C:\Users\Jerry\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 953, in predict
y_pred = self._predict(X)
File "C:\Users\Jerry\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 676, in _predict
self._forward_pass(activations)
File "C:\Users\Jerry\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\neural_network\multilayer_perceptron.py", line 102, in _forward_pass
self.coefs_[i])
File "C:\Users\Jerry\AppData\Local\Programs\Python\Python37\lib\site-packages\sklearn\utils\extmath.py", line 173, in safe_sparse_dot
return np.dot(a, b)
**ValueError: shapes (2000,13231) and (12299,1000) not aligned: 13231 (dim 1) != 12299 (dim 0)**
I know the error is related to the differences in the feature vector size -- since the vectors are being created from the text in the data. I do not know enough about NLP or Machine Learning to devise a solution to workaround this problem. How can I create a way to have the model predict using the feature sets in the test data?
I tried making edits per answers below to save the feature vector:
Train.py now looks like:
import nltk, re, pandas as pd
from nltk.corpus import stopwords
import sklearn, string
import numpy as np
from sklearn.neural_network import MLPClassifier
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from sklearn.svm import SVC
from sklearn.neighbors import KNeighborsClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from itertools import islice
import time
import pickle
from joblib import dump, load
def ID_to_Num(arr):
le = preprocessing.LabelEncoder()
new_arr = le.fit_transform(arr)
return new_arr
def Num_to_ID(arr):
le = preprocessing.LabelEncoder()
new_arr = le.inverse_transform(arr)
return new_arr
def check_performance(preds, acts):
preds = list(preds)
acts = pd.Series.tolist(acts)
right = 0
total = 0
for i in range(len(preds)):
if preds[i] == acts[i]:
right += 1
total += 1
return (right / total) * 100
# This function removes numbers from an array
def remove_nums(arr):
# Declare a regular expression
pattern = '[0-9]'
# Remove the pattern, which is a number
arr = [re.sub(pattern, '', i) for i in arr]
# Return the array with numbers removed
return arr
# This function cleans the passed in paragraph and parses it
def get_words(para):
# Create a set of stop words
stop_words = set(stopwords.words('english'))
# Split it into lower case
lower = para.lower().split()
# Remove punctuation
no_punctuation = (nopunc.translate(str.maketrans('', '', string.punctuation)) for nopunc in lower)
# Remove integers
no_integers = remove_nums(no_punctuation)
# Remove stop words
dirty_tokens = (data for data in no_integers if data not in stop_words)
# Ensure it is not empty
tokens = [data for data in dirty_tokens if data.strip()]
# Ensure there is more than 1 character to make up the word
tokens = [data for data in tokens if len(data) > 1]
# Return the tokens
return tokens
def minmaxscale(data):
scaler = MinMaxScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
return df_scaled
# This function takes the first n items of a dictionary
def take(n, iterable):
#https://stackoverflow.com/questions/7971618/python-return-first-n-keyvalue-pairs-from-dict
#Return first n items of the iterable as a dict
return dict(islice(iterable, n))
def main():
tsv_file = "filepath\\train.tsv"
csv_table=pd.read_csv(tsv_file, sep='\t', header=None)
csv_table.columns = ['class', 'ID', 'text']
s = pd.Series(csv_table['text'])
new = s.str.cat(sep=' ')
vocab = get_words(new)
s = pd.Series(csv_table['text'])
corpus = s.apply(lambda s: ' '.join(get_words(s)))
csv_table['dirty'] = csv_table['text'].str.split().apply(len)
csv_table['clean'] = csv_table['text'].apply(lambda s: len(get_words(s)))
vectorizer = TfidfVectorizer()
test = vectorizer.fit_transform(corpus)
df = pd.DataFrame(data=test.todense(), columns=vectorizer.get_feature_names())
result = pd.concat([csv_table, df], axis=1, sort=False)
Y = result['class']
result = result.drop('text', axis=1)
result = result.drop('ID', axis=1)
result = result.drop('class', axis=1)
X = result
mlp = MLPClassifier()
rf = RandomForestClassifier()
mlp_opt = MLPClassifier(
activation = 'tanh',
hidden_layer_sizes = (1000,),
alpha = 0.009,
learning_rate = 'adaptive',
learning_rate_init = 0.01,
max_iter = 250,
momentum = 0.9,
solver = 'lbfgs',
warm_start = False
)
print("Training Classifiers")
mlp_opt.fit(X, Y)
mlp.fit(X, Y)
rf.fit(X, Y)
dump(mlp_opt, "filepath\\Models\\mlp_opt.joblib")
dump(mlp, "filepath\\Models\\mlp.joblib")
dump(rf, "filepath\\Models\\rf.joblib")
pickle.dump(test, open("filepath\\tfidf_vectorizer.pkl", 'wb'))
print("Trained Classifiers")
main()
And Test.py now looks like:
from nltk.corpus import stopwords
import sklearn, string, nltk, re, pandas as pd, numpy, time
from sklearn.neural_network import MLPClassifier
from sklearn import preprocessing
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split, KFold
from sklearn.ensemble import RandomForestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from joblib import dump, load
import pickle
def ID_to_Num(arr):
le = preprocessing.LabelEncoder()
new_arr = le.fit_transform(arr)
return new_arr
def Num_to_ID(arr):
le = preprocessing.LabelEncoder()
new_arr = le.inverse_transform(arr)
return new_arr
def check_performance(preds, acts):
preds = list(preds)
acts = pd.Series.tolist(acts)
right = 0
total = 0
for i in range(len(preds)):
if preds[i] == acts[i]:
right += 1
total += 1
return (right / total) * 100
# This function removes numbers from an array
def remove_nums(arr):
# Declare a regular expression
pattern = '[0-9]'
# Remove the pattern, which is a number
arr = [re.sub(pattern, '', i) for i in arr]
# Return the array with numbers removed
return arr
# This function cleans the passed in paragraph and parses it
def get_words(para):
# Create a set of stop words
stop_words = set(stopwords.words('english'))
# Split it into lower case
lower = para.lower().split()
# Remove punctuation
no_punctuation = (nopunc.translate(str.maketrans('', '', string.punctuation)) for nopunc in lower)
# Remove integers
no_integers = remove_nums(no_punctuation)
# Remove stop words
dirty_tokens = (data for data in no_integers if data not in stop_words)
# Ensure it is not empty
tokens = [data for data in dirty_tokens if data.strip()]
# Ensure there is more than 1 character to make up the word
tokens = [data for data in tokens if len(data) > 1]
# Return the tokens
return tokens
def minmaxscale(data):
scaler = MinMaxScaler()
df_scaled = pd.DataFrame(scaler.fit_transform(data), columns=data.columns)
return df_scaled
# This function takes the first n items of a dictionary
def take(n, iterable):
#https://stackoverflow.com/questions/7971618/python-return-first-n-keyvalue-pairs-from-dict
#Return first n items of the iterable as a dict
return dict(islice(iterable, n))
def main():
tfidf_vectorizer = pickle.load(open("filepath\\tfidf_vectorizer.pkl", 'rb'))
tsv_file = "filepath\\dev.tsv"
csv_table=pd.read_csv(tsv_file, sep='\t', header=None)
csv_table.columns = ['class', 'ID', 'text']
s = pd.Series(csv_table['text'])
new = s.str.cat(sep=' ')
vocab = get_words(new)
s = pd.Series(csv_table['text'])
corpus = s.apply(lambda s: ' '.join(get_words(s)))
csv_table['dirty'] = csv_table['text'].str.split().apply(len)
csv_table['clean'] = csv_table['text'].apply(lambda s: len(get_words(s)))
print(type(corpus))
print(corpus.head())
X = tfidf_vectorizer.transform(corpus)
print(X)
df = pd.DataFrame(data=X.todense(), columns=tfidf_vectorizer.get_feature_names())
result = pd.concat([csv_table, df], axis=1, sort=False)
Y = result['class']
result = result.drop('text', axis=1)
result = result.drop('ID', axis=1)
result = result.drop('class', axis=1)
X = result
mlp_opt = load("filepath\\Models\\mlp_opt.joblib")
mlp = load("filepath\\Models\\mlp.joblib")
rf = load("filepath\\Models\\rf.joblib")
print("Testing Classifiers")
mlp_opt_preds = mlp_opt.predict(X)
mlp_preds = mlp.predict(X)
rf_preds = rf.predict(X)
mlp_opt_performance = check_performance(mlp_opt_preds, Y)
mlp_performance = check_performance(mlp_preds, Y)
rf_performance = check_performance(rf_preds, Y)
print("MLP OPT PERF: {}".format(mlp_opt_performance))
print("MLP PERF: {}".format(mlp_performance))
print("RF PERF: {}".format(rf_performance))
main()
But that yields:
Traceback (most recent call last):
File "Filepath\Reader.py", line 128, in <module>
main()
File "Filepath\Reader.py", line 95, in main
X = tfidf_vectorizer.transform(corpus)
File "C:\Users\Jerry\AppData\Local\Programs\Python\Python37\lib\site-packages\scipy\sparse\base.py", line 689, in __getattr__
raise AttributeError(attr + " not found")
AttributeError: transform not found
You shouldn't use fit_transform() on test-dataset. You should only use the vocabulary that had been learned from train-dataset.
Here's an example solution,
import pickle
tfidf_vectorizer = TfidfVectorizer()
train_data = tfidf_vectorizer.fit_transform(train_corpus) # fit on train
# You could just save the vectorizer with pickle
pickle.dump(tfidf_vectorizer, open('tfidf_vectorizer.pkl', 'wb'))
# then later load the vectorizer and transform on test-dataset.
tfidf_vectorizer = pickle.load(open('tfidf_vectorizer.pkl', 'rb'))
test_data = tfidf_vectorizer.transform(test_corpus)
When you use transform() it only considers the vocabulary learned from train-corpus ignoring any new words found in test-set.