Experiment: Code of the first experiment on the learning methods. Added a file to show the results
Gregory Martin
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bd89b21967
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SML-Homework
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|
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||||||
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||||||
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|
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|
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|
||||||
|
<envs />
|
||||||
|
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|
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|
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|
||||||
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|
||||||
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|
||||||
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||||||
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|
||||||
|
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|
||||||
|
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|
||||||
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|
||||||
|
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|
||||||
|
<option name="IS_MODULE_SDK" value="true" />
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||||||
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|
||||||
|
<option name="ADD_SOURCE_ROOTS" value="true" />
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||||||
|
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||||||
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|
||||||
|
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||||||
|
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|
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|
||||||
|
</project>
|
||||||
Binary file not shown.
|
|
@ -0,0 +1,40 @@
|
||||||
|
from sklearn.datasets import load_iris
|
||||||
|
from sklearn.feature_extraction.text import CountVectorizer
|
||||||
|
import pandas
|
||||||
|
|
||||||
|
# Example of dataset
|
||||||
|
|
||||||
|
iris = load_iris()
|
||||||
|
|
||||||
|
featureMatrix = iris.data
|
||||||
|
labelVector = iris.target
|
||||||
|
|
||||||
|
print(featureMatrix.shape)
|
||||||
|
print(labelVector.shape)
|
||||||
|
print(iris.feature_names)
|
||||||
|
print(featureMatrix[1])
|
||||||
|
|
||||||
|
# ----------------------#
|
||||||
|
# Text training example #
|
||||||
|
# ----------------------#
|
||||||
|
|
||||||
|
print("---")
|
||||||
|
|
||||||
|
dataset = ["call you tonight", "Call me a cab", "please call me... PLEASE!"]
|
||||||
|
vector = CountVectorizer()
|
||||||
|
|
||||||
|
# Learn the "vocabulary" of the training data (occurs in-place)
|
||||||
|
vector.fit(dataset)
|
||||||
|
featureNames = vector.get_feature_names()
|
||||||
|
|
||||||
|
print(featureNames)
|
||||||
|
|
||||||
|
# Transform training data into a "document-term matrix'
|
||||||
|
documentTermMatrix = vector.transform(dataset)
|
||||||
|
|
||||||
|
# convert sparse matrix to a dense matrix
|
||||||
|
documentTermMatrix.toarray()
|
||||||
|
|
||||||
|
# examine the vocabulary and document-term matrix together
|
||||||
|
df = pandas.DataFrame(documentTermMatrix.toarray(), columns=vector.get_feature_names())
|
||||||
|
print(df.head())
|
||||||
|
|
@ -0,0 +1,137 @@
|
||||||
|
from sklearn.model_selection import train_test_split
|
||||||
|
|
||||||
|
from sklearn.feature_extraction.text import CountVectorizer
|
||||||
|
from sklearn.tree import DecisionTreeClassifier
|
||||||
|
from sklearn.linear_model import LogisticRegression
|
||||||
|
from sklearn import metrics
|
||||||
|
from sklearn.neural_network import MLPClassifier
|
||||||
|
|
||||||
|
import pandas
|
||||||
|
from pandas import DataFrame
|
||||||
|
|
||||||
|
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
|
||||||
|
|
||||||
|
|
||||||
|
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 = []
|
||||||
|
|
||||||
|
# Simulate ten trees so we can have an average.
|
||||||
|
for x in range(0, 15):
|
||||||
|
# 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)
|
||||||
|
|
||||||
|
# Note: DTM=documentTermMatrix
|
||||||
|
vectorizer = CountVectorizer()
|
||||||
|
trainDTM = vectorizer.fit_transform(dataset_train)
|
||||||
|
testDTM = vectorizer.transform(dataset_test)
|
||||||
|
|
||||||
|
# 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.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")
|
||||||
|
|
||||||
|
# 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.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")
|
||||||
|
|
||||||
|
# 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.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")
|
||||||
|
|
||||||
|
print("Step", x, "done.")
|
||||||
|
|
||||||
|
experimentOneDF = DataFrame()
|
||||||
|
experimentOneDF["measure"] = measureArray
|
||||||
|
experimentOneDF["value"] = valueArray
|
||||||
|
experimentOneDF["method"] = methodArray
|
||||||
|
|
||||||
|
if not os.path.exists(workspace + "results/"):
|
||||||
|
os.makedirs(workspace + "results/")
|
||||||
|
|
||||||
|
experimentOneDF.to_csv(experimentOnePath)
|
||||||
|
|
@ -0,0 +1,20 @@
|
||||||
|
import matplotlib.pyplot as pyplot
|
||||||
|
import seaborn
|
||||||
|
|
||||||
|
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)
|
||||||
|
|
||||||
|
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)
|
||||||
|
pyplot.title('Insert Title', fontsize=15)
|
||||||
|
pyplot.show()
|
||||||
Loading…
Reference in New Issue