v2.0-classify.py

######################################################################################## Remove warnings

import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)

def warn(*args, **kwargs):
    pass
import warnings
warnings.warn = warn

######################################################################################## Importing Modules

from sklearn.model_selection import train_test_split as tts
from sklearn import svm
from sklearn import tree
import pandas as pd
import numpy
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import AdaBoostClassifier
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import VotingClassifier
from sklearn.model_selection import cross_val_score
import re



######################################################################################## Loading data & Preprocessing



# Load the dataset - CSV input
data = pd.read_csv('data/class_full.csv',
                    encoding='latin1',
                    error_bad_lines=False,
                    delimiter=';')


# Define column names & Change label to Pandas "Category"
data.columns  = ['desc', 'value', 'label']
data['desc'] = data['desc'].str.replace('[^\w\s]','')
data['label'] = data['label'].astype('category')


# Assign data as "string"
labels   = data['label'].values.astype(str)
feature1 = data['desc'].values.astype(str)
feature2 = data['value'].values


# Vectorizes strings with Tf-IDF Vectorizer
vectorizer = TfidfVectorizer()
vectors    = vectorizer.fit_transform(feature1)   # Train Data
vectors_   = vectorizer.transform(feature1)       # Test Data



######################################################################################## Fitting the models



#Test
train_features, test_features, train_labels, test_labels = tts(vectors, labels, test_size=0.05)

# Random Forest Classifier
print('\nEstimating score with Random Forest Classifier...')
forest_model = RandomForestClassifier(random_state=42)
forest_model.fit(train_features, train_labels)
predictions_forest = forest_model.predict(vectors_)
print('Score: {:.2f}'.format(forest_model.score(test_features, test_labels)*100) + ' Random Forest Classifier')


# Decision Tree Classifier
print('\nEstimating score with Decision Tree Classifier...')
tree_model = tree.DecisionTreeClassifier(random_state=42)
tree_model.fit(train_features, train_labels)
predictions_tree = tree_model.predict(vectors_)
print('Score: {:.2f}'.format(tree_model.score(test_features, test_labels)*100) + ' Decistion Tree Classifier')


# SVC Linear Classifier
print('\nEstimating score with SVC Linear Classifier...')
svc_model = svm.SVC(kernel='linear', random_state=42, probability=True)
svc_model.fit(train_features, train_labels)
predictions_svc = svc_model.predict(vectors_)
print('Score: {:.2f}'.format(svc_model.score(test_features, test_labels)*100) + ' SVC Linear Classifier')


# ExtraTree Classifier
print('\nEstimating score with ExtraTree Classifier...')
extra_model = ExtraTreesClassifier(n_estimators=100, max_depth=None, min_samples_split=10, random_state=42)
extra_model.fit(train_features, train_labels)
predictions_extra = extra_model.predict(vectors_)
print('Score: {:.2f}'.format(extra_model.score(test_features, test_labels)*100) + ' ExtraTree Classifier')



######################################################################################## Print consolidated results and accuracy



# Print of all results
print('\nScore: {:.2f}'.format(forest_model.score(test_features, test_labels)*100) + ' Random Forest Classifier')
print('Score: {:.2f}'.format(tree_model.score(test_features, test_labels)*100)     + ' Decistion Tree Classifier')
print('Score: {:.2f}'.format(svc_model.score(test_features, test_labels)*100)      + ' SVC Linear Classifier')
print('Score: {:.2f}'.format(extra_model.score(test_features, test_labels)*100)    + ' ExtraTree Classifier\n')


# Voting Classifier
eclf = VotingClassifier(estimators=[
                                   ('lr', forest_model),
                                   ('rf', tree_model),
                                   ('svc', svc_model),
                                   ('extra', extra_model)
                                   ], voting='soft')

for clf, label in zip(   [forest_model, tree_model, svc_model, extra_model, eclf],
                         ['Random Forest', 'Decision Tree', 'SVC', 'Extra']):
     
     scores = cross_val_score(clf, test_features, test_labels, cv=5, scoring='accuracy')
     print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))



######################################################################################## Make Predictions



# Statement to be classified
statement = pd.read_csv('data/extrato.csv',
                    encoding='latin1',
                    error_bad_lines=False,
                    delimiter=',')

# Defining the column and vectorizing it
newFeatures 	= statement['memo'].values.astype(str)
newVectorizer 	= TfidfVectorizer()
newVector    	= newVectorizer.fit_transform(newFeatures)
newVector_    	= newVectorizer.transform(newFeatures)

# Predictions with Random Forest, Decision Tree & SVC models
predictions_forest  = forest_model.predict(vectorizer.transform(newFeatures))
predictions_forest  = numpy.asarray(predictions_forest)
predictions_forest  = pd.DataFrame(predictions_forest)

predictions_tree  = tree_model.predict(vectorizer.transform(newFeatures))
predictions_tree  = numpy.asarray(predictions_tree)
predictions_tree  = pd.DataFrame(predictions_tree)

predictions_svc  = svc_model.predict(vectorizer.transform(newFeatures))
predictions_svc  = numpy.asarray(predictions_svc)
predictions_svc  = pd.DataFrame(predictions_svc)

predictions_extra = extra_model.predict(vectorizer.transform(newFeatures))
predictions_extra  = numpy.asarray(predictions_extra)
predictions_extra  = pd.DataFrame(predictions_extra)

# Finding probabilities for each of the assigned categories
forest_prob = forest_model.predict_proba(vectorizer.transform(newFeatures))
forest_prob = numpy.asarray(forest_prob)
forest_prob = pd.DataFrame(forest_prob)
forest_prob = forest_prob.max(axis=1)

tree_prob = tree_model.predict_proba(vectorizer.transform(newFeatures))
tree_prob = numpy.asarray(tree_prob)
tree_prob = pd.DataFrame(tree_prob)
tree_prob = tree_prob.max(axis=1)

svc_prob = svc_model.predict_proba(vectorizer.transform(newFeatures))
svc_prob = numpy.asarray(svc_prob)
svc_prob = pd.DataFrame(svc_prob)
svc_prob = svc_prob.max(axis=1)

extra_prob = extra_model.predict_proba(vectorizer.transform(newFeatures))
extra_prob = numpy.asarray(extra_prob)
extra_prob = pd.DataFrame(extra_prob)
extra_prob = extra_prob.max(axis=1)



######################################################################################## Export and consolidate predictions



csv_pred = pd.concat([	predictions_forest, forest_prob,
					predictions_tree, tree_prob,
					predictions_svc, svc_prob,
                         predictions_extra, extra_prob],
					axis=1)

csv_pred.to_csv("data/predictions.csv")


consolidated = pd.concat([statement, csv_pred], axis=1)
consolidated.columns  = ['type', 'date', 'amount', 'memo', 'id',
					'forest_predict', 'forest_prob',
					'tree_predict', 'tree_prob',
					'svc_predict', 'svc_prob',
                         'extra_predict', 'extra_prob']

consolidated.to_csv("data/consolidated.csv")



######################################################################################## END