Create multiclass_classifier.py

python-processing/classification/multiclass_classifier.py

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+__author__ = 'Federico'
+# Multiclass Naive-Bayes classifier for categorization of WoN e-mail dataset
+# It uses MultinomialNB classifier
+
+from numpy import *
+from tools.tensor_utils import read_input_tensor, SparseTensor
+from sklearn import metrics
+from sklearn.naive_bayes import MultinomialNB
+from sklearn.pipeline import Pipeline
+from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
+from nltk.corpus import stopwords
+
+# Get the input from a folder in C:
+def get_example_data():
+
+    header_file = 'C:/Users/Federico/Desktop/test/evaluation/tensor_content_NEW/headers.txt'
+    data_file_prefix = 'C:/Users/Federico/Desktop/test/evaluation/tensor_content_NEW'
+    data_files = [data_file_prefix + "/connection.mtx",
+                  data_file_prefix + "/needtype.mtx",
+                  data_file_prefix + "/subject.mtx",
+                  data_file_prefix + "/content.mtx",
+                  data_file_prefix + "/category.mtx"]
+    slices = [SparseTensor.CONNECTION_SLICE, SparseTensor.NEED_TYPE_SLICE, SparseTensor.ATTR_SUBJECT_SLICE,
+              SparseTensor.ATTR_CONTENT_SLICE, SparseTensor.CATEGORY_SLICE]
+
+    tensor = read_input_tensor(header_file, data_files, slices, False)
+
+    data = []
+    target = []
+
+    # Store the chosen input into lists.
+    # The "if" statement is meant to include only samples with a single category (No multilabel)
+    for need_index in tensor.getNeedIndices():
+        content = ""
+        categories = tensor.getAttributesForNeed(need_index, SparseTensor.CATEGORY_SLICE)
+        numCategories = len(categories)
+        if numCategories >= 1:
+            category_index = tensor.getSliceMatrix(SparseTensor.CATEGORY_SLICE)[need_index,].nonzero()[1][0]
+            target.append(category_index)
+            for word in tensor.getAttributesForNeed(need_index, SparseTensor.ATTR_SUBJECT_SLICE):
+                content += word + " "
+            data.append(content)
+
+    # Include only few of all the categories (e.g. with samples > n)
+    newdata = []
+    newtarget = []
+    for i in range(len(target)):
+
+        if target.count(target[i]) > 50:
+            newtarget.append(target[i])
+            newdata.append(data[i])
+
+    data = newdata
+    target = newtarget
+
+    # Print out the input, just a check:
+    target_names = tensor.getHeaders()
+    print("test")
+    print data
+    print target_names
+    print target
+
+    return data, target, target_names
+
+# Call for the input
+my_data, my_target, my_targetname = get_example_data()
+
+# A little information about dimensions and format of the input:
+print type(my_data), type(my_target),   # format of data and targets
+print len(my_data)  # number of samples
+print len(my_target)
+
+
+# Let's build the training and testing datasets:
+SPLIT_PERC = 0.80  # 80% goes into training, 20% into test
+split_size = int(len(my_data)*SPLIT_PERC)
+X_train = my_data[:split_size]
+X_test = my_data[split_size:]
+y_train = my_target[:split_size]
+y_test = my_target[split_size:]
+
+
+# Training, prediction and evaluation of the classifier(s):
+def train_and_evaluate(clf, X_train, X_test, y_train, y_test, y_name):
+
+    # Training
+    clf.fit(X_train, y_train)
+    # Prediction of testing sets
+    y_pred = clf.predict(X_test)
+
+    # Precision, recall and support (i.e. nr. of samples used for the testing)
+    print "Classification Report:"
+    print metrics.classification_report(y_test, y_pred)
+    # Confusion Matrix
+    print "Confusion Matrix:"
+    print metrics.confusion_matrix(y_test, y_pred)
+
+    # Visualization of Categories / Assigned / Data
+    print "Tested data => assigned category,    data:"
+    for i in range(len(X_test)):
+        print str(i) + ")   Real category: " + str(y_name[y_test[i]]) + ",    Assigned category: " + \
+            str(y_name[y_pred[i]]) + ",     Data: " + str(X_test[i])
+
+    # Assign names to the categories (defined by numbers)
+    print "\n Categories: \n"
+    categories = set()
+    for cat in y_pred:
+        categories.add(cat)
+    categories = sorted(categories)
+    for cat in categories:
+        print str(cat) + "    " + y_name[cat]
+
+# Introducing stop words
+stopset = set(stopwords.words('english'))
+
+# Two different classifiers: Count and Tfidf vectors
+clf_count = Pipeline([
+    ('vect', CountVectorizer(
+        stop_words=stopset,
+        token_pattern=ur"\b[a-z0-9_\-\.]+[a-z][a-z0-9_\-\.]+\b",
+    )),
+    ('clf', MultinomialNB(alpha=1)),
+    ])
+
+clf_tfidf = Pipeline([
+     ('vect', TfidfVectorizer(
+         stop_words=stopset,
+         token_pattern=ur"\b[a-z0-9_\-\.]+[a-z][a-z0-9_\-\.]+\b",
+     )),
+     ('clf', MultinomialNB(alpha=1)),
+     ])
+
+# List of classifiers
+clfs = [clf_count, clf_tfidf]
+
+# Run the evaluation/classification
+for clf in clfs:
+    train_and_evaluate(clf, X_train, X_test, y_train, y_test, my_targetname)