Experiment: Decision Tree code finished

2017-11-08 16:48:42 +01:00 · 2017-11-08 16:48:42 +01:00 · 2ac14e25a5
parent 2fb57232fc
commit 2ac14e25a5
17 changed files with 2362 additions and 104 deletions
--- a/CourseExample.ipynb
+++ b/CourseExample.ipynb
@ -1535,7 +1535,9 @@
  {
   "cell_type": "code",
   "execution_count": 70,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
   "outputs": [],
   "source": [
    "# make class predictions for X_test_dtm\n",
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--- a/Experiments/learningmethod/experimentDT.py
+++ b/Experiments/learningmethod/experimentDT.py
@ -0,0 +1,294 @@
+from sklearn.model_selection import train_test_split
+from sklearn.feature_extraction.text import CountVectorizer
+from sklearn.tree import DecisionTreeClassifier
+from sklearn import metrics
+
+import pandas
+from pandas import DataFrame
+
+import numpy
+
+import os
+
+workspace = "/home/toshuumilia/Workspace/SML/"  # Insert the working directory here.
+datasetPath = workspace + "data/sms.tsv"  # Tells where is located the data
+
+smsCount = 5574
+
+if not os.path.exists(workspace + "results/"):
+    os.makedirs(workspace + "results/")
+
+###################
+# Loading dataset #
+###################
+
+smsDF = pandas.read_table(datasetPath, header=None, names=["label", "message"])
+smsDF["label_numerical"] = smsDF.label.map({"ham": 0, "spam": 1})
+
+smsDataset = smsDF.message
+smsLabel = smsDF.label_numerical
+
+methodArray = []
+measureArray = []
+valueArray = []
+availableMeasures = ["Accuracy", "F1Score"]
+
+
+dataset_train, dataset_test, label_train, label_test = train_test_split(smsDataset, smsLabel, random_state=1)
+
+# Note: DTM=documentTermMatrix
+vectorizer = CountVectorizer()
+trainDTM = vectorizer.fit_transform(dataset_train)
+testDTM = vectorizer.transform(dataset_test)
+
+# DEPTH EXPERIMENT
+# availableDepths = [None, 50, 25, 10, 5, 3]
+#
+# print("Depth Experiment")
+# for x in range(0, 4):
+#     for depth in availableDepths:
+#         print("Step", x, "for depth:", depth)
+#         # SEE: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
+#         decisionTree = DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=depth,
+#                                               min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0,
+#                                               max_features=None, random_state=None, max_leaf_nodes=None,
+#                                               min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None,
+#                                               presort=False)
+#         decisionTree.fit(trainDTM, label_train)
+#
+#         label_predicted = decisionTree.predict(testDTM)
+#
+#         # SEE: https://en.wikipedia.org/wiki/Precision_and_recall
+#         valueArray.append(metrics.accuracy_score(label_test, label_predicted))
+#         valueArray.append(metrics.f1_score(label_test, label_predicted))
+#
+#         for index in range(0, 2):
+#             measureArray.append(availableMeasures[index])
+#             methodArray.append("Depth-" + str(depth))
+#
+# # Save the experiments
+# experimentDTDepthDF = DataFrame()
+# experimentDTDepthDF["Measure"] = measureArray
+# experimentDTDepthDF["Value"] = valueArray
+# experimentDTDepthDF["Depth"] = methodArray
+#
+# experimentDTDepthDF.to_csv(workspace + "results/experimentDTDepth.csv")
+
+# CRITERION EXPERIMENT
+# availableCriterion = ["gini", "entropy"]
+#
+# methodArray = []
+# measureArray = []
+# valueArray = []
+#
+# print("Criteron Experiment")
+# for x in range(0, 4):
+#     for criterion in availableCriterion:
+#         print("Step", x, "for criterion:", criterion)
+#         decisionTree = DecisionTreeClassifier(criterion=criterion, splitter='best', max_depth=None,
+#                                               min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0,
+#                                               max_features=None, random_state=None, max_leaf_nodes=None,
+#                                               min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None,
+#                                               presort=False)
+#         decisionTree.fit(trainDTM, label_train)
+#
+#         label_predicted = decisionTree.predict(testDTM)
+#
+#         valueArray.append(metrics.accuracy_score(label_test, label_predicted))
+#         valueArray.append(metrics.f1_score(label_test, label_predicted))
+#
+#         for index in range(0, 2):
+#             measureArray.append(availableMeasures[index])
+#             methodArray.append("Criterion-" + criterion)
+#
+# # Save the experiments
+# experimentDTCriteronDF = DataFrame()
+# experimentDTCriteronDF["Measure"] = measureArray
+# experimentDTCriteronDF["Value"] = valueArray
+# experimentDTCriteronDF["Criterion"] = methodArray
+#
+# experimentDTCriteronDF.to_csv(workspace + "results/experimentDTCriterion.csv")
+
+# MIN_SAMPLES_SPLIT EXPERIMENT
+
+# availableMinSampleSplit = [2, 10, 25, 50, 100, 250]
+#
+# methodArray = []
+# measureArray = []
+# valueArray = []
+#
+# print("MinSampleSplit Experiment")
+# for x in range(0, 20):
+#     for minSampleSplit in availableMinSampleSplit:
+#         print("Step", x, "for minSampleSplit:", minSampleSplit)
+#         decisionTree = DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None,
+#                                               min_samples_split=minSampleSplit, min_samples_leaf=1,
+#                                               min_weight_fraction_leaf=0.0,
+#                                               max_features=None, random_state=None, max_leaf_nodes=None,
+#                                               min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None,
+#                                               presort=False)
+#         decisionTree.fit(trainDTM, label_train)
+#
+#         label_predicted = decisionTree.predict(testDTM)
+#
+#         valueArray.append(metrics.accuracy_score(label_test, label_predicted))
+#         valueArray.append(metrics.f1_score(label_test, label_predicted))
+#
+#         for index in range(0, 2):
+#             measureArray.append(availableMeasures[index])
+#             methodArray.append("MinSampleSplit-" + str(minSampleSplit))
+#
+# # Save the experiments
+# experimentDTMinSampleSplitDF = DataFrame()
+# experimentDTMinSampleSplitDF["Measure"] = measureArray
+# experimentDTMinSampleSplitDF["Value"] = valueArray
+# experimentDTMinSampleSplitDF["MinSampleSplit"] = methodArray
+#
+# experimentDTMinSampleSplitDF.to_csv(workspace + "results/experimentDTMinSampleSplit.csv")
+
+# MAX_FEATURE EXPERIMENT
+
+# availableMaxFeature = [None, "sqrt", "log2", 0.25, 0.5, 0.75]
+#
+# methodArray = []
+# measureArray = []
+# valueArray = []
+#
+# print("MaxFeature Experiment")
+# for x in range(0, 10):
+#     for maxFeature in availableMaxFeature:
+#         print("Step", x, "for MaxFeature:", maxFeature)
+#         decisionTree = DecisionTreeClassifier(max_features=maxFeature)
+#         decisionTree.fit(trainDTM, label_train)
+#
+#         label_predicted = decisionTree.predict(testDTM)
+#
+#         valueArray.append(metrics.accuracy_score(label_test, label_predicted))
+#         valueArray.append(metrics.f1_score(label_test, label_predicted))
+#
+#         for index in range(0, 2):
+#             measureArray.append(availableMeasures[index])
+#             methodArray.append("MaxFeature-" + str(maxFeature))
+#
+# # Save the experiments
+# experimentDTMaxFeatureDF = DataFrame()
+# experimentDTMaxFeatureDF["Measure"] = measureArray
+# experimentDTMaxFeatureDF["Value"] = valueArray
+# experimentDTMaxFeatureDF["MaxFeature"] = methodArray
+#
+# experimentDTMaxFeatureDF.to_csv(workspace + "results/experimentDTMaxFeature.csv")
+
+# MAX_LEAF_NODES EXPERIMENT
+
+# availableMaxLeafNodes = []
+# for ratio in numpy.arange(1/6, 1.01, 1/6):
+#     availableMaxLeafNodes.append(int(ratio * smsCount))
+
+# availableMaxLeafNodes = numpy.concatenate([[2], numpy.arange(10, 270, 10)])
+
+# methodArray = []
+# measureArray = []
+# valueArray = []
+#
+# print("MaxLeafNodes Experiment")
+# for x in range(0, 5):
+#     for maxLeafNodes in availableMaxLeafNodes:
+#         print("Step", x, "for MaxLeafNodes:", maxLeafNodes)
+#         decisionTree = DecisionTreeClassifier(max_leaf_nodes=maxLeafNodes)
+#         decisionTree.fit(trainDTM, label_train)
+#
+#         label_predicted = decisionTree.predict(testDTM)
+#
+#         valueArray.append(metrics.accuracy_score(label_test, label_predicted))
+#         valueArray.append(metrics.f1_score(label_test, label_predicted))
+#
+#         for index in range(0, 2):
+#             measureArray.append(availableMeasures[index])
+#             methodArray.append(maxLeafNodes)
+#
+# # Save the experiments
+# experimentDTMaxLeafNodesDF = DataFrame()
+# experimentDTMaxLeafNodesDF["Measure"] = measureArray
+# experimentDTMaxLeafNodesDF["Value"] = valueArray
+# experimentDTMaxLeafNodesDF["MaxLeafNodes"] = methodArray
+#
+# experimentDTMaxLeafNodesDF.to_csv(workspace + "results/experimentDTMaxLeafNodes.csv")
+
+# MIN_IMPURITY_DECREASE
+
+# availableMinImpurityDecrease = numpy.arange(0., 0.061, 0.005)
+#
+# methodArray = []
+# measureArray = []
+# valueArray = []
+#
+# print("MaxFeature Experiment")
+# for x in range(0, 10):
+#     for minImpurityDecrease in availableMinImpurityDecrease:
+#         print("Step", x, "for MinImpurityDecrease:", minImpurityDecrease)
+#         decisionTree = DecisionTreeClassifier(min_impurity_decrease=minImpurityDecrease)
+#         decisionTree.fit(trainDTM, label_train)
+#
+#         label_predicted = decisionTree.predict(testDTM)
+#
+#         valueArray.append(metrics.accuracy_score(label_test, label_predicted))
+#         valueArray.append(metrics.f1_score(label_test, label_predicted))
+#
+#         for index in range(0, 2):
+#             measureArray.append(availableMeasures[index])
+#             methodArray.append(str(minImpurityDecrease*100) + "%")
+#
+# # Save the experiments
+# experimentDTMinImpurityDecreaseDF = DataFrame()
+# experimentDTMinImpurityDecreaseDF["Measure"] = measureArray
+# experimentDTMinImpurityDecreaseDF["Value"] = valueArray
+# experimentDTMinImpurityDecreaseDF["MinImpurityDecrease"] = methodArray
+#
+# experimentDTMinImpurityDecreaseDF.to_csv(workspace + "results/experimentDTMinImpurityDecrease.csv")
+
+# DEFAULT DT VS OPTIMIZED DT EXPERIMENT
+
+availableMeasures = ["Precision", "Recall", "Accuracy", "F1Score"]
+methodArray = []
+measureArray = []
+valueArray = []
+
+print("MaxFeature Experiment")
+for x in range(0, 20):
+    print("Step", x, "for Basic Decision Tree")
+    decisionTree = DecisionTreeClassifier()
+    decisionTree.fit(trainDTM, label_train)
+
+    label_predicted = decisionTree.predict(testDTM)
+
+    valueArray.append(metrics.precision_score(label_test, label_predicted))
+    valueArray.append(metrics.recall_score(label_test, label_predicted))
+    valueArray.append(metrics.accuracy_score(label_test, label_predicted))
+    valueArray.append(metrics.f1_score(label_test, label_predicted))
+
+    for measure in availableMeasures:
+        measureArray.append(measure)
+        methodArray.append("Basic Decision Tree")
+
+    print("Step", x, "for Custom Decision Tree")
+    decisionTree = DecisionTreeClassifier(max_features=0.25, criterion="gini")
+    decisionTree.fit(trainDTM, label_train)
+
+    label_predicted = decisionTree.predict(testDTM)
+
+    valueArray.append(metrics.precision_score(label_test, label_predicted))
+    valueArray.append(metrics.recall_score(label_test, label_predicted))
+    valueArray.append(metrics.accuracy_score(label_test, label_predicted))
+    valueArray.append(metrics.f1_score(label_test, label_predicted))
+
+    for measure in availableMeasures:
+        measureArray.append(measure)
+        methodArray.append("Custom Decision Tree")
+
+# Save the experiments
+experimentDTBasicVsOptimizedDF = DataFrame()
+experimentDTBasicVsOptimizedDF["Measure"] = measureArray
+experimentDTBasicVsOptimizedDF["Value"] = valueArray
+experimentDTBasicVsOptimizedDF["Tuning"] = methodArray
+
+experimentDTBasicVsOptimizedDF.to_csv(workspace + "results/experimentDTBasicVsOptimized.csv")
--- a/Experiments/learningmethod/experimentLR.py
+++ b/Experiments/learningmethod/experimentLR.py
--- a/Experiments/learningmethod/experimentMethod.py
+++ b/Experiments/learningmethod/experimentMethod.py
@ -1,10 +1,11 @@
 from sklearn.model_selection import train_test_split
-
 from sklearn.feature_extraction.text import CountVectorizer
+from sklearn import metrics
+
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.linear_model import LogisticRegression
-from sklearn import metrics
 from sklearn.neural_network import MLPClassifier
+from sklearn.naive_bayes import MultinomialNB

 import pandas
 from pandas import DataFrame
@ -13,8 +14,9 @@ import os

 workspace = "/home/toshuumilia/Workspace/SML/"  # Insert the working directory here.
 datasetPath = workspace + "data/sms.tsv"  # Tells where is located the data
-experimentOnePath = workspace + "experiment/experimentOne.csv"  # Location of the first experiment result

+if not os.path.exists(workspace + "results/"):
+    os.makedirs(workspace + "results/")

 smsDF = pandas.read_table(datasetPath, header=None, names=["label", "message"])
 smsDF["label_numerical"] = smsDF.label.map({"ham": 0, "spam": 1})
@ -26,8 +28,11 @@ methodArray = []
 measureArray = []
 valueArray = []

+availableMeasures = ["Precision", "Recall", "Accuracy", "F1Score"]
+availableMethods = ["Decision Tree", "Logistic Regression", "Neural Network", "Naive Bayesian"]
+
 # Simulate ten trees so we can have an average.
-for x in range(0, 15):
+for x in range(0, 10):
    # Create the datasets and the labels used for the ML.
    # TODO: Parameter to test: how to split the smsDataset into train and test.
    dataset_train, dataset_test, label_train, label_test = train_test_split(smsDataset, smsLabel, random_state=1)
@ -40,98 +45,77 @@ for x in range(0, 15):
    # DECISION TREE
    # TODO: Explore which parameters could be used.
    # SEE: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html
-    decisionTree = DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=None,
-                                          min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0,
-                                          max_features=None, random_state=None, max_leaf_nodes=None,
-                                          min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None,
-                                          presort=False)
+    decisionTree = DecisionTreeClassifier()
    decisionTree.fit(trainDTM, label_train)
    
    label_predicted = decisionTree.predict(testDTM)
    
    # SEE: https://en.wikipedia.org/wiki/Precision_and_recall
    valueArray.append(metrics.precision_score(label_test, label_predicted))
-    measureArray.append("precision")
-    methodArray.append("Decision Tree")
-
    valueArray.append(metrics.recall_score(label_test, label_predicted))
-    measureArray.append("recall")
-    methodArray.append("Decision Tree")
-
    valueArray.append(metrics.accuracy_score(label_test, label_predicted))
-    measureArray.append("accuracy")
-    methodArray.append("Decision Tree")
-
    valueArray.append(metrics.f1_score(label_test, label_predicted))
-    measureArray.append("f1score")
-    methodArray.append("Decision Tree")
+    
+    for index in range(0, 4):
+        measureArray.append(availableMeasures[index])
+        methodArray.append(availableMethods[0])
    
    # LOGISTIC REGRESSION
    # TODO: Explore which parameters could be used.
    # SEE: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
-    logisticRegression = LogisticRegression(penalty='l2', dual=False, tol=0.0001,
-                                            C=1.0, fit_intercept=True, intercept_scaling=1,
-                                            class_weight=None, random_state=None, solver='liblinear',
-                                            max_iter=100, multi_class='ovr', verbose=0,
-                                            warm_start=False, n_jobs=1)
+    logisticRegression = LogisticRegression()
    logisticRegression.fit(trainDTM, label_train)
    
    label_predicted = logisticRegression.predict(testDTM)
-    
+
    valueArray.append(metrics.precision_score(label_test, label_predicted))
-    measureArray.append("precision")
-    methodArray.append("Logistic Regression")
-
    valueArray.append(metrics.recall_score(label_test, label_predicted))
-    measureArray.append("recall")
-    methodArray.append("Logistic Regression")
-
    valueArray.append(metrics.accuracy_score(label_test, label_predicted))
-    measureArray.append("accuracy")
-    methodArray.append("Logistic Regression")
-
    valueArray.append(metrics.f1_score(label_test, label_predicted))
-    measureArray.append("f1score")
-    methodArray.append("Logistic Regression")
+
+    for index in range(0, 4):
+        measureArray.append(availableMeasures[index])
+        methodArray.append(availableMethods[1])
    
    # NEURAL NETWORK
    # SEE: http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
-    neuralNetwork = MLPClassifier(hidden_layer_sizes=(5,), activation='relu', solver='adam',
-                                  alpha=0.0001, batch_size='auto', learning_rate='constant',
-                                  learning_rate_init=0.001, power_t=0.5, max_iter=200,
-                                  shuffle=True, random_state=None, tol=0.0001,
-                                  verbose=False, warm_start=False, momentum=0.9,
-                                  nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1,
-                                  beta_1=0.9, beta_2=0.999, epsilon=1e-08)
+    neuralNetwork = MLPClassifier()
    
    neuralNetwork.fit(trainDTM, label_train)
    
    label_predicted = neuralNetwork.predict(testDTM)

    valueArray.append(metrics.precision_score(label_test, label_predicted))
-    measureArray.append("precision")
-    methodArray.append("Neural Network")
-
    valueArray.append(metrics.recall_score(label_test, label_predicted))
-    measureArray.append("recall")
-    methodArray.append("Neural Network")
-
    valueArray.append(metrics.accuracy_score(label_test, label_predicted))
-    measureArray.append("accuracy")
-    methodArray.append("Neural Network")
-
    valueArray.append(metrics.f1_score(label_test, label_predicted))
-    measureArray.append("f1score")
-    methodArray.append("Neural Network")
+
+    for index in range(0, 4):
+        measureArray.append(availableMeasures[index])
+        methodArray.append(availableMethods[2])
+    
+    # NAIVE BAYESIAN
+    # SEE: http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html
+    naiveBayesian = MultinomialNB(alpha=1.0, fit_prior=True, class_prior=None)
+
+    naiveBayesian.fit(trainDTM, label_train)
+
+    label_predicted = naiveBayesian.predict(testDTM)
+
+    valueArray.append(metrics.precision_score(label_test, label_predicted))
+    valueArray.append(metrics.recall_score(label_test, label_predicted))
+    valueArray.append(metrics.accuracy_score(label_test, label_predicted))
+    valueArray.append(metrics.f1_score(label_test, label_predicted))
+
+    for index in range(0, 4):
+        measureArray.append(availableMeasures[index])
+        methodArray.append(availableMethods[3])

    print("Step", x, "done.")
    
-experimentOneDF = DataFrame()
-experimentOneDF["measure"] = measureArray
-experimentOneDF["value"] = valueArray
-experimentOneDF["method"] = methodArray
+experimentBasicMethodsDF = DataFrame()
+experimentBasicMethodsDF["Measure"] = measureArray
+experimentBasicMethodsDF["Value"] = valueArray
+experimentBasicMethodsDF["Method"] = methodArray

-if not os.path.exists(workspace + "results/"):
-    os.makedirs(workspace + "results/")
-    
-experimentOneDF.to_csv(experimentOnePath)
+experimentBasicMethodsDF.to_csv(workspace + "results/experimentBasicMethods.csv")
--- a/Experiments/learningmethod/experimentNN.py
+++ b/Experiments/learningmethod/experimentNN.py
--- a/Experiments/learningmethod/showGraph.py
+++ b/Experiments/learningmethod/showGraph.py
@ -5,16 +5,135 @@ import pandas

 workspace = "/home/toshuumilia/Workspace/SML/"  # Insert the working directory here.
 datasetPath = workspace + "data/sms.tsv"  # Tells where is located the data
-experimentOnePath = workspace + "results/experimentOne.csv"  # Location of the first experiment result
-globalFigsize = (15, 6)  # Graphs parameters

-experimentOneDF = pandas.read_csv(experimentOnePath)
+# Experiment location
+
+# Graphs parameters
+globalFigsize = (12, 6)
+
+
+# Comparison Experiment #
+#
+# experimentOneDF = pandas.read_csv(experimentOnePath)
+#
+# seaborn.set_style("darkgrid")
+# pyplot.figure(figsize=globalFigsize)
+# seaborn.barplot(x="Value", y="Measure", hue="Method",
+#                 data=experimentOneDF)
+# pyplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+# pyplot.ylabel('Measure', fontsize=12)
+# pyplot.xlabel('Value', fontsize=12)
+# pyplot.xlim(0.5, 1)
+# pyplot.title('Performance comparison between four learning methods', fontsize=15)
+# pyplot.show()
+
+
+# Decision Tree #
+# Depth Experiment
+#
+# experimentDTDepthDF = pandas.read_csv(workspace + "results/experimentDTDepth.csv")
+#
+# seaborn.set_style("whitegrid")
+# pyplot.figure(figsize=globalFigsize)
+# seaborn.barplot(x="Value", y="Measure", hue="Depth",
+#                 data=experimentDTDepthDF)
+# pyplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+# pyplot.ylabel('Measure', fontsize=12)
+# pyplot.xlabel('Value', fontsize=12)
+# pyplot.xlim(0.5, 1)
+# pyplot.title('Performance comparison of a Decision Tree relative to a maximum depth', fontsize=15)
+# pyplot.show()
+
+# Criterion Experiment
+#
+# experimentDTCriterionDF = pandas.read_csv(workspace + "results/experimentDTCriterion.csv")
+#
+# seaborn.set_style("whitegrid")
+# pyplot.figure(figsize=globalFigsize)
+# seaborn.barplot(x="Value", y="Measure", hue="Criterion",
+#                 data=experimentDTCriterionDF)
+# pyplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+# pyplot.ylabel('Measure', fontsize=12)
+# pyplot.xlabel('Value', fontsize=12)
+# pyplot.xlim(0.5, 1)
+# pyplot.title('Performance comparison of a Decision Tree relative to a splitting quality criterion', fontsize=15)
+# pyplot.show()
+
+# MinSampleSplit Experiment
+#
+# experimentDTMinSampleSplitDF = pandas.read_csv(workspace + "results/experimentDTMinSampleSplit.csv")
+#
+# seaborn.set_style("whitegrid")
+# pyplot.figure(figsize=globalFigsize)
+# seaborn.barplot(x="Value", y="Measure", hue="MinSampleSplit",
+#                 data=experimentDTMinSampleSplitDF)
+# pyplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+# pyplot.ylabel('Measure', fontsize=12)
+# pyplot.xlabel('Value', fontsize=12)
+# pyplot.xlim(0.5, 1)
+# pyplot.title('Insert Title', fontsize=15)
+# pyplot.xticks(rotation='vertical')
+# pyplot.show()
+
+# MaxFeature Experiment
+#
+# experimentDTMaxFeatureDF = pandas.read_csv(workspace + "results/experimentDTMaxFeature.csv")
+#
+# seaborn.set_style("whitegrid")
+# pyplot.figure(figsize=globalFigsize)
+# seaborn.barplot(x="Value", y="Measure", hue="MaxFeature",
+#                 data=experimentDTMaxFeatureDF)
+# pyplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+# pyplot.ylabel('Measure', fontsize=12)
+# pyplot.xlabel('Value', fontsize=12)
+# pyplot.xlim(0.5, 1)
+# pyplot.title('Insert Title', fontsize=15)
+# pyplot.xticks(rotation='vertical')
+# pyplot.show()
+
+# MaxLeafNodes Experiment
+
+# experimentDTMaxLeafNodesDF = pandas.read_csv(workspace + "results/experimentDTMaxLeafNodes.csv")
+#
+# seaborn.set_style("whitegrid")
+# pyplot.figure(figsize=globalFigsize)
+# seaborn.pointplot(y="Value", hue="Measure", x="MaxLeafNodes",
+#                 data=experimentDTMaxLeafNodesDF)
+# pyplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+# pyplot.ylabel('Measure', fontsize=12)
+# pyplot.xlabel('Value', fontsize=12)
+# pyplot.xlim(0.5, 1)
+# pyplot.title('Insert Title', fontsize=15)
+# pyplot.xticks(rotation='vertical')
+# pyplot.show()
+
+# MinImpurityDecrease Experiment
+#
+# experimentDTMinImpurityDecreaseDF = pandas.read_csv(workspace + "results/experimentDTMinImpurityDecrease.csv")
+#
+# seaborn.set_style("whitegrid")
+# pyplot.figure(figsize=globalFigsize)
+# seaborn.pointplot(y="Value", hue="Measure", x="MinImpurityDecrease",
+#                   data=experimentDTMinImpurityDecreaseDF, palette="Greens_d")
+# pyplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+# pyplot.ylabel('Measure Value', fontsize=12)
+# pyplot.xlabel('Min Impurity Decrease', fontsize=12)
+# pyplot.title('', fontsize=15)
+# pyplot.xticks(rotation='vertical')
+# pyplot.show()
+
+# BasicVsOptimized Experiment
+
+experimentDTBasicVsOptimizedDF = pandas.read_csv(workspace + "results/experimentDTBasicVsOptimized.csv")

 seaborn.set_style("whitegrid")
 pyplot.figure(figsize=globalFigsize)
-seaborn.barplot(x="measure", y="value", hue="method",
-                data=experimentOneDF, palette="Blues_d")
-pyplot.ylabel('value', fontsize=12)
-pyplot.xlabel('measure', fontsize=12)
+seaborn.barplot(x="Value", y="Measure", hue="Tuning",
+                data=experimentDTBasicVsOptimizedDF)
+pyplot.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)
+pyplot.ylabel('Measure', fontsize=12)
+pyplot.xlabel('Value', fontsize=12)
+pyplot.xlim(0.5, 1)
 pyplot.title('Insert Title', fontsize=15)
+pyplot.xticks(rotation='vertical')
 pyplot.show()
--- a/SML-ComparisonLearningMethod.ipynb
+++ b/SML-ComparisonLearningMethod.ipynb
--- a/results/experimentBasicMethods.csv
+++ b/results/experimentBasicMethods.csv
@ -0,0 +1,121 @@
+,Measure,Value,Method
+0,Precision,0.8333333333333334,Decision Tree
+1,Recall,0.8648648648648649,Decision Tree
+2,Accuracy,0.95908111988514,Decision Tree
+3,F1Score,0.8488063660477454,Decision Tree
+4,Precision,0.9941176470588236,Logistic Regression
+5,Recall,0.9135135135135135,Logistic Regression
+6,Accuracy,0.9877961234745154,Logistic Regression
+7,F1Score,0.9521126760563381,Logistic Regression
+8,Precision,1.0,Neural Network
+9,Recall,0.9297297297297298,Neural Network
+10,Accuracy,0.990667623833453,Neural Network
+11,F1Score,0.9635854341736695,Neural Network
+12,Precision,0.8631578947368421,Decision Tree
+13,Recall,0.8864864864864865,Decision Tree
+14,Accuracy,0.9662598707824839,Decision Tree
+15,F1Score,0.8746666666666667,Decision Tree
+16,Precision,0.9941176470588236,Logistic Regression
+17,Recall,0.9135135135135135,Logistic Regression
+18,Accuracy,0.9877961234745154,Logistic Regression
+19,F1Score,0.9521126760563381,Logistic Regression
+20,Precision,1.0,Neural Network
+21,Recall,0.9297297297297298,Neural Network
+22,Accuracy,0.990667623833453,Neural Network
+23,F1Score,0.9635854341736695,Neural Network
+24,Precision,0.8465608465608465,Decision Tree
+25,Recall,0.8648648648648649,Decision Tree
+26,Accuracy,0.9612347451543432,Decision Tree
+27,F1Score,0.8556149732620321,Decision Tree
+28,Precision,0.9941176470588236,Logistic Regression
+29,Recall,0.9135135135135135,Logistic Regression
+30,Accuracy,0.9877961234745154,Logistic Regression
+31,F1Score,0.9521126760563381,Logistic Regression
+32,Precision,1.0,Neural Network
+33,Recall,0.9351351351351351,Neural Network
+34,Accuracy,0.9913854989231874,Neural Network
+35,F1Score,0.9664804469273743,Neural Network
+36,Precision,0.8743169398907104,Decision Tree
+37,Recall,0.8648648648648649,Decision Tree
+38,Accuracy,0.9655419956927495,Decision Tree
+39,F1Score,0.8695652173913042,Decision Tree
+40,Precision,0.9941176470588236,Logistic Regression
+41,Recall,0.9135135135135135,Logistic Regression
+42,Accuracy,0.9877961234745154,Logistic Regression
+43,F1Score,0.9521126760563381,Logistic Regression
+44,Precision,1.0,Neural Network
+45,Recall,0.9297297297297298,Neural Network
+46,Accuracy,0.990667623833453,Neural Network
+47,F1Score,0.9635854341736695,Neural Network
+48,Precision,0.8578947368421053,Decision Tree
+49,Recall,0.8810810810810811,Decision Tree
+50,Accuracy,0.964824120603015,Decision Tree
+51,F1Score,0.8693333333333333,Decision Tree
+52,Precision,0.9941176470588236,Logistic Regression
+53,Recall,0.9135135135135135,Logistic Regression
+54,Accuracy,0.9877961234745154,Logistic Regression
+55,F1Score,0.9521126760563381,Logistic Regression
+56,Precision,0.9942857142857143,Neural Network
+57,Recall,0.9405405405405406,Neural Network
+58,Accuracy,0.9913854989231874,Neural Network
+59,F1Score,0.9666666666666667,Neural Network
+60,Precision,0.8624338624338624,Decision Tree
+61,Recall,0.8810810810810811,Decision Tree
+62,Accuracy,0.9655419956927495,Decision Tree
+63,F1Score,0.8716577540106951,Decision Tree
+64,Precision,0.9941176470588236,Logistic Regression
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+66,Accuracy,0.9877961234745154,Logistic Regression
+67,F1Score,0.9521126760563381,Logistic Regression
+68,Precision,0.9942528735632183,Neural Network
+69,Recall,0.9351351351351351,Neural Network
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+71,F1Score,0.9637883008356545,Neural Network
+72,Precision,0.8695652173913043,Decision Tree
+73,Recall,0.8648648648648649,Decision Tree
+74,Accuracy,0.964824120603015,Decision Tree
+75,F1Score,0.8672086720867209,Decision Tree
+76,Precision,0.9941176470588236,Logistic Regression
+77,Recall,0.9135135135135135,Logistic Regression
+78,Accuracy,0.9877961234745154,Logistic Regression
+79,F1Score,0.9521126760563381,Logistic Regression
+80,Precision,1.0,Neural Network
+81,Recall,0.9297297297297298,Neural Network
+82,Accuracy,0.990667623833453,Neural Network
+83,F1Score,0.9635854341736695,Neural Network
+84,Precision,0.8797814207650273,Decision Tree
+85,Recall,0.8702702702702703,Decision Tree
+86,Accuracy,0.9669777458722182,Decision Tree
+87,F1Score,0.875,Decision Tree
+88,Precision,0.9941176470588236,Logistic Regression
+89,Recall,0.9135135135135135,Logistic Regression
+90,Accuracy,0.9877961234745154,Logistic Regression
+91,F1Score,0.9521126760563381,Logistic Regression
+92,Precision,0.9942528735632183,Neural Network
+93,Recall,0.9351351351351351,Neural Network
+94,Accuracy,0.990667623833453,Neural Network
+95,F1Score,0.9637883008356545,Neural Network
+96,Precision,0.8601036269430051,Decision Tree
+97,Recall,0.8972972972972973,Decision Tree
+98,Accuracy,0.9669777458722182,Decision Tree
+99,F1Score,0.8783068783068783,Decision Tree
+100,Precision,0.9941176470588236,Logistic Regression
+101,Recall,0.9135135135135135,Logistic Regression
+102,Accuracy,0.9877961234745154,Logistic Regression
+103,F1Score,0.9521126760563381,Logistic Regression
+104,Precision,0.9942196531791907,Neural Network
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+107,F1Score,0.9608938547486033,Neural Network
+108,Precision,0.8609625668449198,Decision Tree
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+111,F1Score,0.8655913978494624,Decision Tree
+112,Precision,0.9941176470588236,Logistic Regression
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+116,Precision,1.0,Neural Network
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+118,Accuracy,0.990667623833453,Neural Network
+119,F1Score,0.9635854341736695,Neural Network
--- a/results/experimentDTBasicVsOptimized.csv
+++ b/results/experimentDTBasicVsOptimized.csv
@ -0,0 +1,161 @@
+,Measure,Value,Tuning
+0,Precision,0.8602150537634409,Basic Decision Tree
+1,Recall,0.8648648648648649,Basic Decision Tree
+2,Accuracy,0.9633883704235463,Basic Decision Tree
+3,F1Score,0.8625336927223719,Basic Decision Tree
+4,Precision,0.9010989010989011,Custom Decision Tree
+5,Recall,0.8864864864864865,Custom Decision Tree
+6,Accuracy,0.9720028715003589,Custom Decision Tree
+7,F1Score,0.8937329700272478,Custom Decision Tree
+8,Precision,0.8563829787234043,Basic Decision Tree
+9,Recall,0.8702702702702703,Basic Decision Tree
+10,Accuracy,0.9633883704235463,Basic Decision Tree
+11,F1Score,0.8632707774798928,Basic Decision Tree
+12,Precision,0.9585798816568047,Custom Decision Tree
+13,Recall,0.8756756756756757,Custom Decision Tree
+14,Accuracy,0.9784637473079684,Custom Decision Tree
+15,F1Score,0.9152542372881357,Custom Decision Tree
+16,Precision,0.8804347826086957,Basic Decision Tree
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+19,F1Score,0.8780487804878049,Basic Decision Tree
+20,Precision,0.9209039548022598,Custom Decision Tree
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+23,F1Score,0.9005524861878452,Custom Decision Tree
+24,Precision,0.8412698412698413,Basic Decision Tree
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+27,F1Score,0.8502673796791443,Basic Decision Tree
+28,Precision,0.9349112426035503,Custom Decision Tree
+29,Recall,0.8540540540540541,Custom Decision Tree
+30,Accuracy,0.9727207465900933,Custom Decision Tree
+31,F1Score,0.8926553672316384,Custom Decision Tree
+32,Precision,0.8586956521739131,Basic Decision Tree
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+34,Accuracy,0.9619526202440776,Basic Decision Tree
+35,F1Score,0.8563685636856369,Basic Decision Tree
+36,Precision,0.9081081081081082,Custom Decision Tree
+37,Recall,0.9081081081081082,Custom Decision Tree
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+39,F1Score,0.9081081081081082,Custom Decision Tree
+40,Precision,0.8534031413612565,Basic Decision Tree
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+43,F1Score,0.8670212765957447,Basic Decision Tree
+44,Precision,0.8695652173913043,Custom Decision Tree
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+48,Precision,0.8709677419354839,Basic Decision Tree
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+51,F1Score,0.8733153638814016,Basic Decision Tree
+52,Precision,0.8950276243093923,Custom Decision Tree
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+59,F1Score,0.870026525198939,Basic Decision Tree
+60,Precision,0.9239766081871345,Custom Decision Tree
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+63,F1Score,0.8876404494382022,Custom Decision Tree
+64,Precision,0.8518518518518519,Basic Decision Tree
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+66,Accuracy,0.9626704953338119,Basic Decision Tree
+67,F1Score,0.8609625668449198,Basic Decision Tree
+68,Precision,0.9005524861878453,Custom Decision Tree
+69,Recall,0.8810810810810811,Custom Decision Tree
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+71,F1Score,0.8907103825136612,Custom Decision Tree
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+75,F1Score,0.8625336927223719,Basic Decision Tree
+76,Precision,0.8852459016393442,Custom Decision Tree
+77,Recall,0.8756756756756757,Custom Decision Tree
+78,Accuracy,0.968413496051687,Custom Decision Tree
+79,F1Score,0.8804347826086957,Custom Decision Tree
+80,Precision,0.8473684210526315,Basic Decision Tree
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+83,F1Score,0.8586666666666666,Basic Decision Tree
+84,Precision,0.873015873015873,Custom Decision Tree
+85,Recall,0.8918918918918919,Custom Decision Tree
+86,Accuracy,0.968413496051687,Custom Decision Tree
+87,F1Score,0.8823529411764706,Custom Decision Tree
+88,Precision,0.8541666666666666,Basic Decision Tree
+89,Recall,0.8864864864864865,Basic Decision Tree
+90,Accuracy,0.964824120603015,Basic Decision Tree
+91,F1Score,0.870026525198939,Basic Decision Tree
+92,Precision,0.9204545454545454,Custom Decision Tree
+93,Recall,0.8756756756756757,Custom Decision Tree
+94,Accuracy,0.9734386216798278,Custom Decision Tree
+95,F1Score,0.8975069252077563,Custom Decision Tree
+96,Precision,0.8315789473684211,Basic Decision Tree
+97,Recall,0.8540540540540541,Basic Decision Tree
+98,Accuracy,0.9576453697056713,Basic Decision Tree
+99,F1Score,0.8426666666666667,Basic Decision Tree
+100,Precision,0.873015873015873,Custom Decision Tree
+101,Recall,0.8918918918918919,Custom Decision Tree
+102,Accuracy,0.968413496051687,Custom Decision Tree
+103,F1Score,0.8823529411764706,Custom Decision Tree
+104,Precision,0.8638743455497382,Basic Decision Tree
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+108,Precision,0.9044943820224719,Custom Decision Tree
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+111,F1Score,0.8870523415977961,Custom Decision Tree
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+114,Accuracy,0.964824120603015,Basic Decision Tree
+115,F1Score,0.8686327077747988,Basic Decision Tree
+116,Precision,0.8950276243093923,Custom Decision Tree
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+119,F1Score,0.8852459016393444,Custom Decision Tree
+120,Precision,0.8549222797927462,Basic Decision Tree
+121,Recall,0.8918918918918919,Basic Decision Tree
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+123,F1Score,0.8730158730158729,Basic Decision Tree
+124,Precision,0.9005524861878453,Custom Decision Tree
+125,Recall,0.8810810810810811,Custom Decision Tree
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+127,F1Score,0.8907103825136612,Custom Decision Tree
+128,Precision,0.8617021276595744,Basic Decision Tree
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+131,F1Score,0.8686327077747988,Basic Decision Tree
+132,Precision,0.8978494623655914,Custom Decision Tree
+133,Recall,0.9027027027027027,Custom Decision Tree
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+139,F1Score,0.8677248677248677,Basic Decision Tree
+140,Precision,0.8776595744680851,Custom Decision Tree
+141,Recall,0.8918918918918919,Custom Decision Tree
+142,Accuracy,0.9691313711414213,Custom Decision Tree
+143,F1Score,0.8847184986595175,Custom Decision Tree
+144,Precision,0.8556149732620321,Basic Decision Tree
+145,Recall,0.8648648648648649,Basic Decision Tree
+146,Accuracy,0.9626704953338119,Basic Decision Tree
+147,F1Score,0.860215053763441,Basic Decision Tree
+148,Precision,0.8350515463917526,Custom Decision Tree
+149,Recall,0.8756756756756757,Custom Decision Tree
+150,Accuracy,0.9605168700646087,Custom Decision Tree
+151,F1Score,0.8548812664907652,Custom Decision Tree
+152,Precision,0.8663101604278075,Basic Decision Tree
+153,Recall,0.8756756756756757,Basic Decision Tree
+154,Accuracy,0.9655419956927495,Basic Decision Tree
+155,F1Score,0.8709677419354839,Basic Decision Tree
+156,Precision,0.9034090909090909,Custom Decision Tree
+157,Recall,0.8594594594594595,Custom Decision Tree
+158,Accuracy,0.9691313711414213,Custom Decision Tree
+159,F1Score,0.8808864265927977,Custom Decision Tree
--- a/results/experimentDTCriterion.csv
+++ b/results/experimentDTCriterion.csv
@ -0,0 +1,17 @@
+,Measure,Value,Criterion
+0,Accuracy,0.9619526202440776,Criterion-gini
+1,F1Score,0.8579088471849866,Criterion-gini
+2,Accuracy,0.9698492462311558,Criterion-entropy
+3,F1Score,0.8870967741935484,Criterion-entropy
+4,Accuracy,0.9655419956927495,Criterion-gini
+5,F1Score,0.8709677419354839,Criterion-gini
+6,Accuracy,0.9691313711414213,Criterion-entropy
+7,F1Score,0.8853333333333333,Criterion-entropy
+8,Accuracy,0.9641062455132807,Criterion-gini
+9,F1Score,0.8655913978494624,Criterion-gini
+10,Accuracy,0.968413496051687,Criterion-entropy
+11,F1Score,0.8810810810810811,Criterion-entropy
+12,Accuracy,0.9698492462311558,Criterion-gini
+13,F1Score,0.8864864864864865,Criterion-gini
+14,Accuracy,0.9691313711414213,Criterion-entropy
+15,F1Score,0.8847184986595175,Criterion-entropy
--- a/results/experimentDTDepth.csv
+++ b/results/experimentDTDepth.csv
@ -0,0 +1,49 @@
+,Measure,Value,Depth
+0,Accuracy,0.9612347451543432,Depth-None
+1,F1Score,0.8586387434554974,Depth-None
+2,Accuracy,0.9633883704235463,Depth-50
+3,F1Score,0.8632707774798928,Depth-50
+4,Accuracy,0.9597989949748744,Depth-25
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+6,Accuracy,0.9612347451543432,Depth-10
+7,F1Score,0.8524590163934427,Depth-10
+8,Accuracy,0.9361091170136396,Depth-5
+9,F1Score,0.7613941018766757,Depth-5
+10,Accuracy,0.9411342426417804,Depth-3
+11,F1Score,0.7630057803468209,Depth-3
+12,Accuracy,0.9662598707824839,Depth-None
+13,F1Score,0.8733153638814016,Depth-None
+14,Accuracy,0.9676956209619526,Depth-50
+15,F1Score,0.8780487804878049,Depth-50
+16,Accuracy,0.9655419956927495,Depth-25
+17,F1Score,0.8695652173913042,Depth-25
+18,Accuracy,0.9612347451543432,Depth-10
+19,F1Score,0.8524590163934427,Depth-10
+20,Accuracy,0.9361091170136396,Depth-5
+21,F1Score,0.7613941018766757,Depth-5
+22,Accuracy,0.9411342426417804,Depth-3
+23,F1Score,0.7630057803468209,Depth-3
+24,Accuracy,0.9633883704235463,Depth-None
+25,F1Score,0.8640000000000001,Depth-None
+26,Accuracy,0.9641062455132807,Depth-50
+27,F1Score,0.8648648648648649,Depth-50
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+29,F1Score,0.8719346049046321,Depth-25
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+31,F1Score,0.861878453038674,Depth-10
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+33,F1Score,0.7593582887700535,Depth-5
+34,Accuracy,0.9404163675520459,Depth-3
+35,F1Score,0.7608069164265131,Depth-3
+36,Accuracy,0.968413496051687,Depth-None
+37,F1Score,0.8823529411764706,Depth-None
+38,Accuracy,0.9641062455132807,Depth-50
+39,F1Score,0.8655913978494624,Depth-50
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+41,F1Score,0.8587257617728532,Depth-25
+42,Accuracy,0.9605168700646087,Depth-10
+43,F1Score,0.8501362397820164,Depth-10
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+45,F1Score,0.7547169811320754,Depth-5
+46,Accuracy,0.9411342426417804,Depth-3
+47,F1Score,0.7630057803468209,Depth-3
--- a/results/experimentDTMaxFeature.csv
+++ b/results/experimentDTMaxFeature.csv
@ -0,0 +1,121 @@
+,Measure,Value,MaxFeature
+0,Accuracy,0.9612347451543432,MaxFeature-None
+1,F1Score,0.8571428571428572,MaxFeature-None
+2,Accuracy,0.9540559942569993,MaxFeature-sqrt
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+4,Accuracy,0.9526202440775305,MaxFeature-log2
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--- a/results/experimentDTMaxLeafNodes.csv
+++ b/results/experimentDTMaxLeafNodes.csv
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--- a/results/experimentDTMinImpurityDecrease.csv
+++ b/results/experimentDTMinImpurityDecrease.csv
@ -0,0 +1,261 @@
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--- a/results/experimentDTMinSampleSplit.csv
+++ b/results/experimentDTMinSampleSplit.csv
@ -0,0 +1,241 @@
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--- a/results/experimentOne.csv
+++ b/results/experimentOne.csv
@ -0,0 +1,161 @@
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