def decision_tree(train_features, train_labels, test_features, test_labels, feature_names):
regressor = tree.DecisionTreeRegressor()
regressor.fit(train_features, train_labels)
test_results = cap_results(regressor.predict(test_features))
train_results = cap_results(regressor.predict(train_features))
print "test result", metrics.mean_squared_error(test_labels, test_results)
print "test r2", metrics.r2_score(test_labels, test_results)
print "train result", metrics.mean_squared_error(train_labels, train_results)
print "train r2", metrics.r2_score(train_labels, train_results)
# print "importances"
# temp = []
# for index, val in enumerate(regressor.feature_importances_):
# if val > 0.001:
# temp.append((index, val))
# print sorted(temp, key=lambda x: x[1])
'''graph stuff'''
dot_data = StringIO()
tree.export_graphviz(regressor, out_file=dot_data,
special_characters=True,
class_names=regressor.classes_,
impurity=False,
feature_names=feature_names)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("tree.pdf")
return (test_results, train_results)
def dt_graph(treeest, cv, scores, features, labels, featnames, outfile):
''' Retrains the tree estimator using the fold with the best results
from the cross-validation process. Prints out a graph pdf file of
that estimator.'''
# Hacky way to get the training data for the best fold
bestfold = np.argmax(scores)
cnt = 0
for train, _ in cv:
# Only do stuff when you've got the training indices for the best fold
if(cnt == bestfold):
# Fit
treeest.fit(features[train], labels[train])
# Get the dot file
dot_data = StringIO()
tree.export_graphviz(treeest, out_file=dot_data, \
feature_names=featnames)
# Convert the dot file to a graph
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf(outfile)
return
else:
cnt += 1
print("You should never see this text from dt_graph!")
return
def visualize_tree(clf, outname, headers):
from sklearn.externals.six import StringIO
import pydot
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data, feature_names=list(headers))
graph = pydot.graph_from_dot_data(dot_data.getvalue().decode('latin1').encode('utf8'))
graph.write_pdf(outname)
def main():
if (len(sys.argv) < 2):
print("One Argument Required; Training Set")
return
X_train, Y_train = ParseTraining(sys.argv[1])
#X_test, Y_test = ParseTraining(sys.argv[2])
#X_train, X_test, Y_train, Y_test = cross_validation.train_test_split(X, Y, test_size=0.2, random_state=99)
#X_train, X_test, Y_train, Y_test = X, X, Y, Y
#clf = tree.DecisionTreeClassifier()
clf = tree.DecisionTreeClassifier(max_depth=6)
#clf = OneVsRestClassifier(SVC(kernel="linear", C=0.025))
#clf = RandomForestClassifier(max_depth=6, n_estimators=10, max_features=1)
#clf = SVC(kernel="linear", C=0.025)
#clf = AdaBoostClassifier()
#clf = SVC(gamma=2, C=1)
clf = clf.fit(X_train, Y_train)
#feature_names = ["partAvg", "recavg", "latency", "ReadRate"]
feature_names = ["partConf", "recAvg", "latency", "ReadRate", "homeconf"]
#feature_names = ["partAvg", "recAvg", "recVar", "ReadRate"]
#feature_names = ["partAvg", "recAvg", "recVar"]
#feature_names = ["recAvg", "recVar", "Read"]
#feature_names = ["partAvg", "recVar"]
##class_names = ["Partition", "OCC", "2PL"]
#class_names = ["OCC", "2PL"]
class_names = ["Partition", "No Partition"]
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data,
feature_names=feature_names,
class_names=class_names,
filled=True, rounded=True,
special_characters=True)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_png("partition.png")
def tree3():
global final_html
global df,df_train,df_test,test_train_created,origin_df
chi_key = list()
init_style_string = template.style_string
if request.method == 'POST':
Listkey1 = list(MultiDict(request.form).values())
Listkey2 = MultiDict(request.form)
DV_tree = Listkey2.get('DV')
df1 = df
for key1 in Listkey1:
if(key1 <> "Build Tree" and key1 <> DV_tree):
chi_key.append(key1)
df1 = df.loc[:,chi_key]
df2 = df1.values
Y = df[DV_tree]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(df2,Y.values)
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
k = dot_data.getvalue()
left_px = 600
width_px = 150
top_px = 50
height_px = 309
s = build_tree_html(k,init_style_string,left_px,width_px,top_px,height_px)
temp_df = df[0:15]
t = """</div><div style="width:600px; height:700px; position: absolute; top: 20px; left:500px;"><br> Decision Tree result <br>"""
final_html = template.s1 + t + k + "<br><br></div>" + temp_df.to_html()
return final_html
return 'helloo'
def mainTree():
header=re.sub(' |\t','','id|gender|age|height|edu|salary|nation|car|house|body|face|hair|\
smoke|drink|child|parent|bmi|where0|where1|\
marriage0|marriage1|look0|look1|where2').split('|')
MaleData=pd.read_csv('/home/idanan/jiayuan/code/resources/transed_M.txt',names=header,sep='|')
FemaleData=pd.read_csv('/home/idanan/jiayuan/code/resources/cluster_female.txt',names=header+['class'],sep='|')
matches=matchDict('/home/idanan/jiayuan/code/resources/lovers_ids.txt')
FemaleData['id']=FemaleData['id'].map(partial(match,matches=matches))
FemaleClass=FemaleData[['id','class']]
newMaleData=concatData(MaleData,FemaleClass)
MaleArrays=scaleData(newMaleData,['id','gender'])
pca=factors(MaleArrays[:,:-1],17)
print 'PCA explained variance:', sum(pca.explained_variance_ratio_)
pcaMaleArray=pca.transform(MaleArrays[:,:-1])
MaleArrays=np.c_[pcaMaleArray,MaleArrays]
trainData,testData=departData(MaleArrays,0.9)
trainModel=decisionModel(trainData)
dot_data = StringIO()
tree.export_graphviz(trainModel, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("/home/idanan/jiayuan/code/resources/marriage.pdf")
rate=test(trainModel,testData)
print 'Decision Model true rate',rate
def run_DT_model_2(df, criteria_col):
# run the tree for various 0,1 lebel (e.g. : high value or not..)
from sklearn.metrics import confusion_matrix
from sklearn.cross_validation import train_test_split
from sklearn.externals.six import StringIO
from IPython.display import Image
import pydotplus
print ('criteria_col = ', criteria_col)
tree_col = [criteria_col,'Frequency', 'LTV', 'period_no_use','AverageTimeToOrder',
'late_by_collection', 'late_by_delivery', 'tickets', 'recleaned_orders',
'cancalled_orders', 'voucher_used']
df_train_ = df
#df_train_tree = df_train_[tree_col]
tree_data = df_train_[tree_col]
tree_data = tree_data.dropna()
tree_train, tree_test = train_test_split(tree_data,
test_size=0.2,
random_state=200,
stratify=tree_data[criteria_col])
clf = tree.DecisionTreeClassifier()
clf = clf.fit(tree_train.iloc[:,1:], tree_train[criteria_col])
print (clf.score(tree_test.iloc[:,1:], tree_test[criteria_col]))
# confusion matrix
print (confusion_matrix(tree_test[criteria_col], clf.predict(tree_test.iloc[:,1:])))
# visualize the tree
dot_data = StringIO()
tree.export_graphviz(clf,
out_file=dot_data,
feature_names=tree_col[1:],
filled=True,
rounded=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
return Image(graph.create_png()), tree_train, tree_test
def decisionTree():
iris = load_iris()
clf = tree.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,
class_weight=None
)
clf = clf.fit(iris.data, iris.target)
dot_data = StringIO()
tree.export_graphviz(
clf,
out_file=dot_data,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=False,
rounded=True,
special_characters=True
)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("iris.pdf")
def classifyTree(Xtr, ytr, Xte, yte, splitCriterion="gini", maxDepth=0, visualizeTree=False):
""" Classifies data using CART """
try:
accuracyRate, probabilities, timing = 0.0, [], 0.0
# Perform classification
cartClassifier = tree.DecisionTreeClassifier(criterion=splitCriterion, max_depth=maxDepth)
startTime = time.time()
prettyPrint("Training a CART tree for classification using \"%s\" and maximum depth of %s" % (splitCriterion, maxDepth), "debug")
cartClassifier.fit(numpy.array(Xtr), numpy.array(ytr))
prettyPrint("Submitting the test samples", "debug")
predicted = cartClassifier.predict(Xte)
endTime = time.time()
# Compare the predicted and ground truth and append result to list
accuracyRate = round(metrics.accuracy_score(predicted, yte), 2)
# Also append the probability estimates
probs = cartClassifier.predict_proba(Xte)
probabilities.append(probs)
timing = endTime-startTime # Keep track of performance
if visualizeTree:
# Visualize the tree
dot_data = StringIO()
tree.export_graphviz(cartClassifier, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
prettyPrint("Saving learned CART to \"tritonTree_%s.pdf\"" % getTimestamp(), "debug")
graph.write_pdf("tree_%s.pdf" % getTimestamp())
except Exception as e:
prettyPrint("Error encountered in \"classifyTree\": %s" % e, "error")
return accuracyRate, timing, probabilities, predicted
def tree2():
global final_html
global df,origin_df
chi_key = list()
firstkey = ""
init_style_string = """<p style="position: absolute; font-size: 12px; top: <top>px; width: <width>px; height: <height>px; left:<left>px; text-align: center;">tree_text_here</p>"""
if request.method == 'POST':
Listkey1 = list(MultiDict(request.form).values())
Listkey2 = MultiDict(request.form)
DV_tree = Listkey2.get('DV')
df1 = df
for key1 in Listkey1:
if(key1 <> "Build Tree" and key1 <> DV_tree):
chi_key.append(key1)
df1 = df.loc[:,chi_key]
df2 = df1.values
temp_count = 0
Y = df[DV_tree]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(df2,Y.values)
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
k = dot_data.getvalue()
k1 = k.split(";")
left_px = 600
width_px = 150
top_px = 50
height_px = 309
s = build_tree_html(k,init_style_string,left_px,width_px,top_px,height_px)
temp_df = df[0:15]
t = """</div><div style="float:right;"><br> Decision Tree result <br>"""
final_html = template.s1 + t + k + "</div><br><br><br>" + temp_df.to_html()
return final_html
return 'helloo'
def generate_plot(clf):
print "\nGenerating plot..."
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("weather_forecast.pdf")
print "Plot generated!"
def visualize_tree(dtree):
dot_data = StringIO()
tree.export_graphviz(dtree, out_file=dot_data,
filled=True, rounded=True,
special_characters=True)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
display(Image(graph.create_png()))
def train_network(self):
""" Pure virtual method for training the network
"""
db_query = self._database_session.query(PregameHitterGameEntry)
mlb_training_data, mlb_evaluation_data = self.get_train_eval_data(db_query, 0.8)
X_train, Y_train = self.get_stochastic_batch(mlb_training_data, self.SIZE_TRAINING_BATCH)
self._decision_tree.fit(X_train, Y_train)
dot_data = StringIO()
tree.export_graphviz(self._decision_tree, out_file=dot_data,
feature_names=PregameHitterGameEntry.get_input_vector_labels())
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("hitter_tree.pdf")
x_test_actual = list()
y_test_actual = list()
for data in mlb_evaluation_data:
try:
postgame_entry = self._database_session.query(PostgameHitterGameEntry).filter(PostgameHitterGameEntry.rotowire_id == data.rotowire_id,
PostgameHitterGameEntry.game_date == data.game_date).one()
y_test_actual.append([postgame_entry.actual_draftkings_points])
x_test_actual.append(data.to_input_vector())
except NoResultFound:
print "Ignoring hitter %s since his postgame stats were not found." % data.rotowire_id
continue
self._database_session.close()
def create_tree(X, Y):
clf = tree.DecisionTreeClassifier(criterion="entropy")
clf = clf.fit(X, Y)
from IPython.display import Image
import pydotplus
dot_data = StringIO()
# tree.export_graphviz(clf, out_file=dot_data)
# feature_names = ['Gender', 'Age']
feature_names = ["Gender", "0-5", "6-12", "13-19", "20-27", "28-35", "36-50", "55+"]
target_names = []
for i in range(1, len(Y) + 1):
target_names.append("Ad #" + str(i))
tree.export_graphviz(
clf,
out_file=dot_data,
feature_names=feature_names,
class_names=target_names,
filled=True,
rounded=True,
special_characters=True,
)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("Tree.pdf")
return clf
def applyDecisionTree(trainData, trainTargets, testData, testTargets, featureNames):
"""Train and classify using a Decision Tree and prints the decision Tree."""
decisionTree = DecisionTreeClassifier()
model = decisionTree.fit(trainData, trainTargets)
# Create graph description of the Decision Tree
dot_data = StringIO()
#export_graphviz(model, out_file=dot_data, max_depth=5)
print("Feature names:", featureNames)
export_graphviz(model, out_file=dot_data, feature_names=featureNames,
max_depth=5)
export_graphviz(model, out_file="DecisionTree.dot", feature_names=featureNames,
max_depth=5)
#with open("DecisionTree.dot", 'r') as dotFile:
# dotFile.write(exportFile)
# Create PDF from dot
graph = pydot.graph_from_dot_data(dot_data.getvalue())
#path = "/Users/konstantin/Documents/University/Bachelorthesis/paper/src/DecisionTree.dot"
#graph = pydot.graph_from_dot_file(path)
#graph.write_pdf("DecisionTree.pdf")
classification = [model.predict(d)[0] for d in testData]
print("\nUsing a Decision Tree:")
showPerformance(testTargets, classification)
def drawDecisionTree(dt, filename, featureNames, classNames):
dot_data = StringIO()
print featureNames
print classNames
tree.export_graphviz(dt, out_file=dot_data, feature_names=featureNames, class_names=classNames, rounded=True, special_characters=True, filled=True)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_png(filename)
def printTreePDF(self, path = './tree.pdf'):
if self.clf == None:
raise NameError('Tree was not created!')
else:
dot_data = StringIO()
tree.export_graphviz(self.clf, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf(path)
def createGraph(clf):
with open("portScan.dot", 'w') as f:
f = tree.export_graphviz(clf, out_file=f)
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("portScan.pdf")
def printPdf(clf, dataTrain):
from sklearn.externals.six import StringIO
import pydot
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf('sentiment.pdf')
print dataTrain.data[0]
def export_tree(clf, filename, feature_names=None, max_depth=None):
from sklearn.externals.six import StringIO
import pydot
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data, feature_names=feature_names, max_depth=max_depth)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf(filename)
def drawDecisionTree(classIndex): clf = tree.DecisionTreeClassifier() clf = clf.fit(preference,y[classIndex]) dot_data = StringIO() # change it: class_names = cnames[classIndex] tree.export_graphviz(clf,out_file=dot_data,feature_names= fname,filled=True, rounded=True,special_characters=True) graph = pydot.graph_from_dot_data(dot_data.getvalue()) filename = "decisionTree_" + str(classIndex) + ".pdf" graph.write_pdf(filename)
def tree_vis(clf):
#fn = ''.join([random.choice(string.ascii_lowercase + string.digits) for _ in range(10)])
fn = 'tree'
fn = 'data/trees/{0}.png'.format(fn)
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_png(fn)
return Image(filename=fn)
def view(classifier):
""" Renders a graph representation of classifier, and
saves it to "MyTree.pdf" in the same folder
as the executing script.
"""
tree_dot = StringIO()
tree.export_graphviz(classifier, out_file=tree_dot)
graph = pydot.graph_from_dot_data(tree_dot.getvalue())
graph.write_pdf("MyTree.pdf")
def __plotTree(clf,name):
tree.export_graphviz(clf,out_file=outputdir + name)
dot_data = StringIO()
tree.export_graphviz(clf,out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf(outputdir + name + '.pdf')
os.remove(outputdir + name)
#plot utilities
def train_decision_tree_elite_status_classifier():
"""Trains and validates a decision tree model for predicting users' Elite status."""
model = train_and_validate_elite_status_classifier(DecisionTreeClassifier, DECISION_TREE_USER_ATTRIBUTES)
# Output tree representation showing decision rules
dot_data = StringIO()
tree.export_graphviz(model, out_file=dot_data, class_names=True, filled=True)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf('analysis/analysis_results/decision_tree.pdf')
def save_tree_png(self, store): import pydot from sklearn.externals.six import StringIO dot_data = StringIO() tree.export_graphviz(self.clf, out_file=dot_data, feature_names=self.feature_names) graph = pydot.graph_from_dot_data(dot_data.getvalue())[0] with open(store.dataset_path + '/tree.png','wb') as f: f.write(graph.create_png())
def export(self, fpath):
"""
Export the decision tree as a PDF file
:return: None
"""
dot_data = StringIO()
tree.export_graphviz(self.model, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf(fpath)
def drawTree(X, y, names, depth, outFile, writePickle, pickleFile):
clf = tree.DecisionTreeClassifier(max_depth=depth) # criterion="entropy"
clf = clf.fit(X, y)
dot_data = StringIO()
tree.export_graphviz(clf, feature_names=names, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf(outFile)
if writePickle:
pickle.dump(clf, open(pickleFile, "wb"))
return clf
def createTreePdf(self):
try:
import pydot
except:
return
dot_data = StringIO()
tree.export_graphviz(self.getClf(),
out_file = dot_data, feature_names = self.featureNames)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("DT" + "-".join(self.classNames) + ".pdf")
def printTree(self, export_pdf=True, pdf_name="Decision_Tree.pdf"):
dot_data = StringIO()
export_graphviz(self.alg, out_file=dot_data, feature_names=self.predictors,
filled=True, rounded=True, special_characters=True)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
if export_pdf:
graph.write_pdf(pdf_name)
return graph
# Getting the test data from dataset | Obtendo os dados de teste do conjunto de dados
test_target = iris.target[test_id_test]
test_data = iris.data[test_id_test]
# Classifying training data | Classificando os dados de treino
classifier = tree.DecisionTreeClassifier()
classifier.fit(train_data, train_target)
# Showing the real rating and the predicted rating | Mostrando a classificação real e a previsão.
print(f'Test target: {test_target}')
print(f'Predict target: {classifier.predict(test_data)}')
# Visualization Code of Decision Tree | Código de visualização da Árvore de Decisão
from sklearn.externals.six import StringIO
import pydot
dot_data = StringIO()
tree.export_graphviz(classifier,
out_file=dot_data,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True,
rounded=True,
impurity=False)
# I used this module (graphviz) to generate the graph
import graphviz as gp
graph = gp.Source(dot_data.getvalue())
graph.render("iris", view=True)
def train_holdout(base_dir, classifier_name, classifier):
base = pd.read_csv(f'{base_dir}/features.csv', sep=';', header=None)
# fetch folder from first index of base
images_dirs = base.iloc[:, 0]
# Separate X to a new DataFrame and convert to numpy array
X = base.iloc[:, 1:]
# Load classes names
y = pd.read_csv(f'{base_dir}/Y.csv', sep=';', header=None)
# HOLDOUT
# separate the base in 70% to train and 30% to test
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.3,
random_state=42, stratify=y)
all_classes = np.unique(y.to_numpy())
def overfitting_prevent_train(X_train, y_train):
# separate the train base in 70% to train and 30% do validation
X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=.2,
random_state=42, stratify=y_train)
chunk_size = 10
graphic_data = []
current_epoch = 0
alpha = 0.8
smooth_error_training = []
smooth_error_validation = []
overfitting_count = 0
for chunk in chunk_generator(pd.concat([X_train, y_train], axis=1), chunk_size=chunk_size):
classifier.partial_fit(chunk[0], np.ravel(chunk[1]), classes=all_classes)
current_error_on_training = 1 - classifier.score(X_train, np.ravel(y_train))
current_error_on_validation = 1 - classifier.score(X_validation, np.ravel(y_validation))
graphic_data.append(
GraphicData(x=current_epoch,
error_on_val=current_error_on_validation,
error_on_train=current_error_on_training)
)
if current_epoch == 0:
smooth_error_training.append(current_error_on_training)
smooth_error_validation.append(current_error_on_validation)
else:
smooth_error_training.append(
alpha * smooth_error_training[current_epoch - 1] + (1 - alpha) * graphic_data[
current_epoch - 1].error_on_train)
smooth_error_validation.append(
alpha * smooth_error_validation[current_epoch - 1] + (1 - alpha) * graphic_data[
current_epoch - 1].error_on_val)
if current_epoch >= 1:
if smooth_error_validation[-2] - smooth_error_validation[-1] < 1e-03:
overfitting_count += 1
if overfitting_count >= 5:
print(f'Overfitting Detected on {classifier_name}, epoch: {current_epoch}')
break
else:
overfitting_count = 0
current_epoch += 1
print(f'Acurácia {classifier_name} Validação: {classifier.score(X_validation, np.ravel(y_validation))}')
plt.title(f'Error Compare --> {classifier_name} --> {base_dir}')
# plt.plot([data.x for data in graphic_data],
# [data.error_on_train for data in graphic_data], label='Error on Train')
# plt.plot([data.x for data in graphic_data],
# [data.error_on_val for data in graphic_data], label='Error on Validation')
plt.plot([data.x for data in graphic_data],
smooth_error_training, label='Error on Train Smooth')
plt.plot([data.x for data in graphic_data],
smooth_error_validation, label='Error on Validation Smooth')
plt.ylabel('Error')
plt.xlabel('Epoch')
plt.legend()
plt.show()
def normal_fit():
classifier.fit(X_train, y_train)
def visualizate_data():
x_tsne = TSNE(n_components=2).fit_transform(X)
y_aux = pd.DataFrame()
y_aux['classes'] = y[0]
tsne_df = pd.DataFrame()
tsne_df['tsne-x'] = x_tsne[:, 0]
tsne_df['tsne-y'] = x_tsne[:, 1]
tsne_df = pd.concat([tsne_df, y_aux], axis=1)
plt.figure(figsize=(16, 10))
sns.scatterplot('tsne-x', 'tsne-y',
hue="classes",
legend='full',
palette=sns.color_palette("hls", 10),
alpha=0.3,
data=tsne_df)
plt.show()
if hasattr(classifier, 'partial_fit'):
overfitting_prevent_train(X_train, y_train)
else:
normal_fit()
predicated_rows = classifier.predict(X_test)
predicated_proba = classifier.predict_proba(X_test)
# aux for find proba for class
aux = 0
for predicated, expected in zip(predicated_rows, y_test.iterrows()):
if expected[1][0] != predicated:
img_dir = images_dirs[expected[0]]
percent = predicated_proba[aux][np.where(all_classes == predicated)]
print(f'Confundiu {img_dir} com {predicated}, proba: {percent}')
aux += 1
print(f'Acurácia {classifier_name} Teste : {classifier.score(X_test, y_test)}')
cm = confusion_matrix(y_test, predicated_rows)
df_cm = pd.DataFrame(cm, index=all_classes, columns=all_classes)
sns.heatmap(df_cm, annot=True)
plt.title(f'Confusion Matrix --> {classifier_name} --> {base_dir}')
plt.ylabel('True label')
plt.xlabel('Predicted label')
plt.show()
# visualizate_data()
if isinstance(classifier, DecisionTreeClassifier):
from sklearn.externals.six import StringIO
from sklearn.tree import export_graphviz
import pydot
dot_data = StringIO()
export_graphviz(classifier, out_file=dot_data, rounded=True,
filled=True)
graph = pydot.graph_from_dot_data(dot_data.getvalue())[0]
graph.write_pdf(f'{classifier_name}.pdf')
return classifier, X_test, y_test
tree.fit(x_train, y_train)
print(tree.score(x_train, y_train))
print(tree.score(x_test, y_test))
# (fix it...)
from sklearn.tree import export_graphviz
export_graphviz(tree, out_file='cancertree.dot', class_names=['m','b'], feature_names=cancer.feature_names, filled=True)
import pydot
import graphviz
from sklearn.externals.six import StringIO
dotfile = StringIO()
export_graphviz(tree, out_file=dotfile, class_names=['m','b'], feature_names=cancer.feature_names, filled=True)
pydot.graph_from_dot_data(dotfile.getvalue()).write_png("dtree2.png")
import matplotlib.image as mpimg
img = mpimg.imread('dtree2.png.png')
plt.imshow(img)
plt.show()
from sklearn.externals.six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
import pydotplus
dot_data = StringIO()
def plot(seed):
filename = 'cryo.csv'
random.seed(seed)
np.random.seed(seed)
data = pd.read_csv(filename, sep='\t')
data = data.sample(frac=1).reset_index(drop=True)
X = data.values[:, 0:6]
y = data.values[:, -1]
#indexList=[0,1,2,3,4,5,6,8,23,24,25]
names = list(data)[0:6]
#print(X.shape)
#print(y.shape)
#X=X[:,np.r_[indexList]]
params = createHyperParameters(seed)
kf = KFold(n_splits=5, random_state=seed, shuffle=False)
acc_history = []
split = 0
reg = None
reg = DecisionTreeClassifier()
reg.set_params(**params)
for (train_indices, val_indices) in kf.split(X, y):
split = split + 1
xtrain, xval = X[train_indices], X[val_indices]
ytrain, yval = y[train_indices], y[val_indices]
# print(xval)
# ytrain = ytrain.reshape(-1,1)
# yval = yval.reshape(-1,1)
reg.fit(xtrain, ytrain)
ypred = reg.predict(xval)
# print(reg.predict_proba(xval))
# ypred2=reg.predict(xtrain)
accuracy = accuracy_score(yval, ypred)
# print(accuracy)
acc_history.append(accuracy)
ACCVALMIN = np.min(acc_history)
ACCVALMAX = np.max(acc_history)
ACCVALMEAN = np.mean(acc_history)
from sklearn.externals.six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
import pydotplus
from sklearn import tree
import collections
dot_data = StringIO()
export_graphviz(reg,
out_file=dot_data,
feature_names=names,
filled=True,
rounded=True,
special_characters=True)
colors = ('turquoise', 'orange')
edges = collections.defaultdict(list)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
for edge in graph.get_edge_list():
edges[edge.get_source()].append(int(edge.get_destination()))
for edge in edges:
edges[edge].sort()
for i in range(2):
dest = graph.get_node(str(edges[edge][i]))[0]
dest.set_fillcolor(colors[i])
predictions = reg.predict_proba(X)
ROCAREA = roc_auc_score(y, predictions[:, 1])
ypr = reg.predict(X)
print("Global Set Accuracy Score : %.4f" % (accuracy_score(y, ypr)))
print("Area under ROC Curve : %.4f" % (ROCAREA))
fpr, tpr, _ = roc_curve(y, predictions[:, 1])
plt.clf()
plt.plot(fpr, tpr)
plt.xlabel('FPR')
plt.ylabel('TPR')
plt.title('ROC curve')
plt.show()
return (ACCVALMIN, ACCVALMAX, ACCVALMEAN, ROCAREA, params, graph)
ac1
#for decision tree
ac2=100*accuracy_score(y_test,pred2)
ac2
#for Naive Bayes
ac3=100*accuracy_score(y_test,pred3)
ac3
from sklearn.externals.six import StringIO
from IPython.display import Image
from sklearn.tree import export_graphviz
import pydotplus
import pydot
dotfile = StringIO()
export_graphviz(clf2, out_file=dotfile, filled=True, rounded=True, special_characters=True)
(graph,) = pydot.graph_from_dot_data(dotfile.getvalue())
Image(graph.create_png())
objects = ('NaiveBayes','Decision Tree', 'RandomForest',)
y_pos = np.arange(len(objects))
performance = [ac3,ac2,ac1]
plt.bar(y_pos, performance, align='center', alpha=0.5)
plt.xticks(y_pos, objects)
plt.ylabel('Usage')
plt.title('Classifier Accuracy')
plt.show()
def customer_segment(customerslist, productname):
g = globals()
xx='match(c:customerid)-[r:Bought_this]->(s:stockcode) ' \
'set r.Actual_Price=toFloat(r.Actual_Price)' \
'return s.Category,collect(DISTINCT r.Actual_Price) as pricelist ' \
'order by s.Category, pricelist'
pricebucket = session.run(xx)
for i in pricebucket:
# print(i)
actualcategoryspresent.append(i['s.Category'])
if len(i['pricelist']) == 1:
start = (i['pricelist'][0]) - 1
end = (i['pricelist'][0]) + 1
else:
start = min(i['pricelist'])
end = max(i['pricelist'])
bucketsize = (end - start) / 10
starts.append(start)
ends.append(end)
bucketsizes.append(bucketsize)
cat.append(i['s.Category'])
# print('start :',start)
# print('end :',end)
# print('bucket_size:',bucketsize)
for i in customerslist:
x = 'optional MATCH(c:customerid{CustomerID: "' + i + '"})-[r:Bought_this]->(s:stockcode{Description: "' + productname + '"}) ' \
'return distinct ' \
'case ' \
'when r.Quantity IS NULL THEN 0 ' \
'when r.Quantity IS NOT NULL THEN 1 ' \
'else r.Quantity END AS Quantity '
for ii in session.run(x):
target.append(ii[0])
for i in customerslist:
x = 'match(c1:customerid{CustomerID: "' + i + '"})-[r1:Bought_this]->(s1:stockcode) return c1.Country limit 1'
result = session.run(x)
for ii in result:
countryofcustomer.append(ii[0])
for i in customerslist:
g[i + ' vector'] = [0] * len(cat)
g[i + ' category-wise purchase vector'] = [0] * len(cat)
g[i + ' bucket_vector'] = [0] * 4
x='match(c1:customerid{CustomerID: "'+i+'"})-[r1:Bought_this]->(s1:stockcode) ' \
'set r1.Quantity=toInteger(r1.Quantity),r1.Price=toFloat(r1.Price)' \
'return c1.CustomerID as CustomerID, s1.Category as Category,reduce(sum=0, i in collect(r1.Quantity * r1.Price) | sum + i) as totalspent , reduce(sum=0, i in collect(r1.Quantity) | sum + i) as Quantity ' \
'order by totalspent desc ' \
result = session.run(x)
count = 0
for i in result:
count = count + 1
if count == 1:
totalspent.append(i['totalspent'])
category.append(i['Category'])
#print(i['totalspent'])
if i['Category'] in cat:
vv = cat.index(i['Category'])
g[i['CustomerID'] +
' category-wise purchase vector'][vv] = i['Quantity']
else:
if i['Category'] in cat:
vv = cat.index(i['Category'])
g[i['CustomerID'] +
' category-wise purchase vector'][vv] = i['Quantity']
for i in range(0, len(customerslist)):
x='match(c1:customerid{CustomerID:"'+customerslist[i]+'"})-[r1:Bought_this]->(s1:stockcode{Category:"'+category[i]+'"}) ' \
'set r1.Quantity=toInteger(r1.Quantity),r1.Price=toFloat(r1.Price) ' \
'return c1.CustomerID as CustomerID,collect(r1.Actual_Price) as prices_in_category'
result = session.run(x)
for yy in result:
# print(yy[1])
most_buyed_item_cost.append(float(Most_Common(yy[1])))
for i in customerslist:
dd = 'match(c1:customerid{CustomerID: "' + i + '"})-[r1:Bought_this]->(s1:stockcode) set r1.Quantity=toInteger(r1.Quantity),r1.Price=toFloat(r1.Price) return c1.CustomerID as CustomerID, collect( distinct s1.Category) as Categoryss'
result = session.run(dd)
for uu in result:
for hh in uu['Categoryss']:
#print(hh)
if hh in cat:
vv = cat.index(hh)
g[i + ' vector'][vv] = 1
for t in range(0, len(customerslist)):
if category[t] in cat:
vv = cat.index(category[t])
l = drop_in_bucket(starts[vv], ends[vv], bucketsizes[vv],
most_buyed_item_cost[t])
pricesensi.append(l)
ageassign(pricesensi)
df = pd.DataFrame(columns=[
'age', 'p_s', 'category', 'totalspent', 'total_cat_bought',
'country'
])
for t in range(0, len(customerslist)):
#print(t)
if category[t] in cat:
vv = cat.index(category[t])
# print('------------------------------------------------------------------------------------------------------------------')
# print('customer :',customerslist[t])
# print('customer age :',ages[t])
# print('country :',countryofcustomer[t])
# print('total spent :',totalspent[t])
# print('category spent :',category[t])
# print('customer category vector :',g[customerslist[t]+' vector'])
if pricesensi[t] == 'High':
g[customerslist[t] + ' bucket_vector'][0] = 1
if pricesensi[t] == 'Medium High':
g[customerslist[t] + ' bucket_vector'][1] = 1
if pricesensi[t] == 'Medium Low':
g[customerslist[t] + ' bucket_vector'][2] = 1
if pricesensi[t] == 'Low':
g[customerslist[t] + ' bucket_vector'][3] = 1
# print('customer category-wise purchase vector :', g[customerslist[t] + ' category-wise purchase vector'])
# print('customer bucket vector :',g[customerslist[t] +' bucket_vector'])
# print('starting price of that category :',starts[vv])
# print('ending price of that category :',ends[vv])
# print('bucket size of category :',bucketsizes[vv])
# print('most buyed item cost :',most_buyed_item_cost[t])
# print('price sensitivity :',pricesensi[t])
# print('total catogories bought :',sum(g[customerslist[t]+' vector']))
# print('Yes/No :',target[t])
df = df.append(
{
'age': ages[t],
'p_s': pricesensi[t],
'category': category[t],
'totalspent': totalspent[t],
'total_cat_bought': int(
sum(g[customerslist[t] + ' vector'])),
'country': countryofcustomer[t]
},
ignore_index=True)
# print(df)
df = df.drop('country', axis=1)
hot_ps = pd.get_dummies(df.p_s)
df = df.join(hot_ps)
df = df.drop('p_s', axis=1)
hot_category = pd.get_dummies(df.category)
df = df.join(hot_category)
df = df.drop('category', axis=1)
# hot_country = pd.get_dummies(df.country)
# df=df.join(hot_country)
# df=df.drop('country',axis=1)
data = df.values
train_target = target
train_data = data
# print(train_target)
x = tree.DecisionTreeClassifier()
print("##################____________________productname", productname)
print(train_data)
print(train_target)
x.fit(train_data, train_target)
dot_data = StringIO()
tree.export_graphviz(x,
out_file=dot_data,
feature_names=df.columns.tolist(),
class_names=['No', 'Yes'],
filled=True,
rounded=True,
impurity=False)
# tree.export_graphviz(x,out_file='tree.dot')
for i in dot_data:
print(i)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
# print(graph)
#os.remove("D:\E-commerce_data_visualization\search\static\stuff/segment.png")
# os.remove(file) for file in os.listdir('path/to/directory') if file.endswith('.png')
# os.system(rm ,"D:\E-commerce_data_visualization\search\static\stuff/segment.png")
graph.write_png(
os.path.join(base_dir, 'search/static/stuff/segment.png'))
print(productname, "productname727")
def buildTree(self):
k = 0
#Until now I considered as threshold for test reward:
# 1- Without considering FER: TEST_REWARD = 76.0
# 2 - Without considering SER: TEST_REWARD = 76.0
# 4 - Without considering ES: TEST_REWARD = 76.0
TEST_REWARD = 76.0
# I want to know the average reward of all features (in this case three features) after 25 run
avgRewAllFeatures = []
# I want to know the average error of all features (in this case three features) after 25 run
total_error = np.array([])
# confidenceIntervalFeatures is a dictionary containing the Monte Carlo error considered for the self.num_trees_optimal policy trees for each run of the algorithm without considering the i-th feature (in this case the i-th key)
confidenceIntervalFeatures = dict()
for elem in self.features:
confidenceIntervalFeatures[elem] = np.array([])
# Average reward after self.n_runs for trees without a feature
averageRewardFeatures = dict()
for elem in self.features:
averageRewardFeatures[elem] = np.array([])
total_importance = [
0.0, 0.0, 0.0
] # it considers the importance of all runs of the algorithm
while (k < self.num_run):
print("RUN " + str(k))
# first tree based on the randomly generated buffer
self.initializeBuffer()
# Fit regression model
self.current_tree.fit(self.X, self.y)
# i is used to count the number of updates a tree has done, j is used to update the value of epsilon
# lastRun is used to count how many trees you want to build considering the optimal policy (epsilon = 0) after reaching the stopping criteria for training
i = j = flag = lastRun = 0
scores = []
slidingWindowReward = []
while (lastRun < self.num_trees_optimal_policy):
print("Tree number: " + str(j))
total_reward = 0
epsilon = self.get_epsilon(j)
current_state = self.env.reset()
if (len(slidingWindowReward) == self.slidingWindow):
if (sum(slidingWindowReward) / len(slidingWindowReward) >
TEST_REWARD):
total_importance += self.current_tree.feature_importances_
print("Total importance: " + str(total_importance))
lastRun += 1
epsilon = 0 # in order to consider the optimal policy we set epsilon = 0
else:
slidingWindowReward = [
] # we clear the slidingWindow if the samples considered don't match our threshold (TEST_REWARD)
while (i < self.update):
if ("Speech Emotion Recognition" not in self.features):
listx = list(current_state)
listx.remove(current_state[1])
tuplex = tuple(listx)
current_state = tuplex
if ("Object State" not in self.features):
listx = list(current_state)
listx.remove(current_state[2])
tuplex = tuple(listx)
current_state = tuplex
if (("Speech Emotion Recognition" in self.features)
and ("Object State" in self.features)):
current_state = current_state[self.initialIndex:self.
finalIndex]
action = self.choose_action(current_state, epsilon)
obs, reward, done, _ = self.env.step(action)
temp = obs
if ("Speech Emotion Recognition" not in self.features):
listx = list(obs)
listx.remove(obs[1])
tuplex = tuple(listx)
obs = tuplex
if ("Object State" not in self.features):
listx = list(obs)
listx.remove(obs[2])
tuplex = tuple(listx)
obs = tuplex
if (("Speech Emotion Recognition" in self.features)
and ("Object State" in self.features)):
obs = obs[self.initialIndex:self.finalIndex]
total_reward += reward
q_current = self.current_tree.predict([current_state])
q_new = self.current_tree.predict([obs])
q_current[0][action] = reward + self.gamma * np.max(
q_new[0])
self.buffer.append(
[current_state, action, obs, q_current[0]])
self.X.append(current_state)
self.y.append(q_current[0])
if (
not lastRun == 0
): # it means until we did not build self.num_trees_optimal_policy
self.X_final.append(current_state)
self.y_final.append(q_current[0])
current_state = temp
i += 1
if done:
current_state = self.env.reset()
if (not flag):
scores.append(total_reward)
flag = 1
if (lastRun == 0):
slidingWindowReward.append(self.test_reward())
i = 0
if (not flag):
scores.append(total_reward)
if (lastRun == 0):
slidingWindowReward.append(total_reward)
self.current_tree = DecisionTreeRegressor()
self.current_tree.fit(self.X, self.y)
j += 1
flag = 0
# Testing considering all the variables but training the model on samples generated by last self.num_trees_optimal_policy with epsilon = 0
print("Testing the tree considering all variables")
self.current_tree = DecisionTreeRegressor()
self.current_tree.fit(self.X_final, self.y_final)
# I want to know the average reward after 25 run considering all variables
avgRewAllFeatures.append(self.test_reward())
# Visualize data
if (self.MAX_LEN > 2 and self.update > 2):
dot_data = StringIO()
export_graphviz(self.current_tree,
out_file=dot_data,
filled=True,
rounded=True,
special_characters=True,
feature_names=self.features)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
Image(graph.create_png())
graph.write_png("final_tree.png")
# Now we build four trees (each one without considering one of the four features), we generate a test-set for each tree (so we generate some episodes)
# From this episodes we compute the Monte-Carlo error that is (r + gamma * G_{t+1} - q^predicted(x,a))^{2} e we sum all these differences to compute the total error
# Higher is this error, more important is the removed feature (so the feature we didn't consider)
"""for i in range(0, len(self.features)):
print("Testing the final tree without considering " + str(self.features[i]))
self.current_tree = DecisionTreeRegressor()
X_feature = self.dataFilter(i)
self.current_tree.fit(X_feature, self.y_final)
r, e = self.getMonteCarloError(i)
averageRewardFeatures[self.features[i]] = np.append(averageRewardFeatures[self.features[i]], r)
confidenceIntervalFeatures[self.features[i]] = np.append(confidenceIntervalFeatures[self.features[i]], e)"""
self.current_tree = DecisionTreeRegressor()
self.current_tree.fit(self.X_final, self.y_final)
r, e = self.getMonteCarloError(len(self.features))
total_error = np.append(total_error, e)
k += 1
""""# This is the final average reward after 25 run considering all features
# Split data into training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1) # 70% training and 30% testing
# Create decision tree classifier object
clf = DecisionTreeClassifier(max_depth=5) # note: arguments are optional (used for pruning)
# Train decision tree classifier
clf = clf.fit(X_train, y_train)
# Predict the response for the test dataset
y_pred = clf.predict(X_test)
# Evaluate model by printing the model accuracy (how often the classifier is correct)
print("\nAccuracy:", metrics.accuracy_score(y_test, y_pred))
print()
# attempting to visualize the data
print("Beginning Visualization...")
dotData = StringIO()
print("\nExporting Graphviz...")
export_graphviz(clf, out_file=dotData, filled=True, rounded=True, special_characters=True, feature_names = featureCols, class_names=['0','1'])
print("\nGraphing using pydotplus library...")
graph = pydotplus.graph_from_dot_data(dotData.getvalue())
graph.write_png('BigTheta.png')
print("Printing now...")
Image(graph.create_png())
from sklearn.datasets import load_iris
from sklearn.externals.six import StringIO
from sklearn import tree
iris = load_iris()
clf = tree.DecisionTreeClassifier()
clf = clf.fit(iris.data, iris.target)
with open("iris.dot", 'w') as f:
f = tree.export_graphviz(clf, out_file=f)
import os
os.unlink('iris.dot')
import pydot
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("iris.pdf")
from IPython.display import Image
dot_data = StringIO()
tree.export_graphviz(clf,
out_file=dot_data,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True,
rounded=True,
special_characters=True)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
Image(graph.create_png())
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.tree import export_graphviz
from sklearn.externals.six import StringIO
from matplotlib import pyplot as plt
from pydot import graph_from_dot_data
import pandas as pd
import numpy as np
iris = load_iris()
X = pd.DataFrame(iris.data, columns=iris.feature_names)
y = pd.Categorical.from_codes(iris.target, iris.target_names)
X.head()
y = pd.get_dummies(y)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=1)
dt = DecisionTreeClassifier()
dt.fit(X_train, y_train)
dot_data = StringIO()
export_graphviz(dt, out_file=dot_data, feature_names=iris.feature_names)
(graph, ) = graph_from_dot_data(dot_data.getvalue())
graph.write_png("tmp.png")
plt.imshow(plt.imread("tmp.png"))
plt.show()
y_pred = dt.predict(X_test)
species = np.array(y_test).argmax(axis=1)
predictions = np.array(y_pred).argmax(axis=1)
confusion_matrix(species, predictions)
def plotTree(treeName,tree,featureNames): treePic_dot = StringIO() export_graphviz(tree, out_file=treePic_dot, feature_names=featureNames, filled=True, rounded=True) graph = pydotplus.graph_from_dot_data(treePic_dot.getvalue()) Image(graph.create_png()) graph.write_png(treeName+'.png')
def draw_tree(model, name):
dot_data = StringIO()
_tree.export_graphviz(model, out_file=dot_data)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf(name + ".pdf")
kyphosis_train = kyphosis[kyphosis.is_train]
kyphosis_test = kyphosis[kyphosis.is_train == False]
# Train model
kyphosis_features = kyphosis.columns[1:]
kyphosis_dt_clf = DecisionTreeClassifier(criterion='entropy',
max_depth=None,
min_samples_split=2,
min_samples_leaf=1)
kyphosis_dt_clf = kyphosis_dt_clf.fit(kyphosis_train[kyphosis_features],
kyphosis_train['Kyphosis'])
# Print a string representation of the tree.
# If you have graphviz (www.graphviz.org) installed, you can write a pdf
# visualization using graph.write_pdf(filename)
kyphosis_dt_data = StringIO()
tree.export_graphviz(kyphosis_dt_clf, out_file=kyphosis_dt_data)
kyphosis_dt_graph = pydotplus.parser.parse_dot_data(
kyphosis_dt_data.getvalue())
print(kyphosis_dt_graph.to_string())
# Predict classes of test set and evaluate
kyphosis_dt_pred = kyphosis_dt_clf.predict(kyphosis_test[kyphosis_features])
kyphosis_dt_cm = metrics.confusion_matrix(kyphosis_test['Kyphosis'],
kyphosis_dt_pred,
labels=['absent', 'present'])
print(kyphosis_dt_cm)
kyphosis_dt_acc = metrics.accuracy_score(kyphosis_test['Kyphosis'],
kyphosis_dt_pred)
kyphosis_dt_prec = metrics.precision_score(kyphosis_test['Kyphosis'],
#!/usr/bin/env python
'''Read and write a string as a file-like object.'''
from sklearn.externals.six import StringIO
# create a sample
mysample = StringIO()
mysample.write('My first testing line.')
print(
mysample) #this only will indicate the location of the file in the memory
# retrieve contents using getvalue()
content = mysample.getvalue()
print(content)
# close my sample
mysample.close()
from IPython.display import Image
import os
import sys
def conda_fix(graph):
path = os.path.join(sys.base_exec_prefix, "Library", "bin", "graphviz")
paths = ("dot", "twopi", "neato", "circo", "fdp")
paths = {p: os.path.join(path, "{}.exe".format(p)) for p in paths}
graph.set_graphviz_executables(paths)
from sklearn import tree
buffer = StringIO()
tree.export_graphviz(dt,
out_file=buffer,
feature_names=X.columns,
class_names=X.columns,
filled=True,
rounded=True,
special_characters=True)
graph = pydotplus.graph_from_dot_data(buffer.getvalue())
conda_fix(graph)
graph.write_pdf("loan_tree.pdf")
Image(graph.create_png())
#ada-booster #boosting
#https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html
from sklearn.ensemble import AdaBoostClassifier
ada = AdaBoostClassifier(base_estimator=dt,
def test_graphviz_toy():
# Check correctness of export_graphviz
clf = DecisionTreeClassifier(max_depth=3,
min_samples_split=2,
criterion="gini",
random_state=2)
clf.fit(X, y)
# Test export code
out = StringIO()
export_graphviz(clf, out_file=out)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box] ;\n' \
'0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
'value = [3, 3]"] ;\n' \
'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
'headlabel="True"] ;\n' \
'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="False"] ;\n' \
'}'
assert_equal(contents1, contents2)
# Test with feature_names
out = StringIO()
export_graphviz(clf, out_file=out, feature_names=["feature0", "feature1"])
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box] ;\n' \
'0 [label="feature0 <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
'value = [3, 3]"] ;\n' \
'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
'headlabel="True"] ;\n' \
'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="False"] ;\n' \
'}'
assert_equal(contents1, contents2)
# Test with class_names
out = StringIO()
export_graphviz(clf, out_file=out, class_names=["yes", "no"])
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box] ;\n' \
'0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
'value = [3, 3]\\nclass = yes"] ;\n' \
'1 [label="gini = 0.0\\nsamples = 3\\nvalue = [3, 0]\\n' \
'class = yes"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
'headlabel="True"] ;\n' \
'2 [label="gini = 0.0\\nsamples = 3\\nvalue = [0, 3]\\n' \
'class = no"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="False"] ;\n' \
'}'
assert_equal(contents1, contents2)
# Test plot_options
out = StringIO()
export_graphviz(clf,
out_file=out,
filled=True,
impurity=False,
proportion=True,
special_characters=True,
rounded=True)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box, style="filled, rounded", color="black", ' \
'fontname=helvetica] ;\n' \
'edge [fontname=helvetica] ;\n' \
'0 [label=<X<SUB>0</SUB> ≤ 0.0<br/>samples = 100.0%<br/>' \
'value = [0.5, 0.5]>, fillcolor="#e5813900"] ;\n' \
'1 [label=<samples = 50.0%<br/>value = [1.0, 0.0]>, ' \
'fillcolor="#e58139ff"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
'headlabel="True"] ;\n' \
'2 [label=<samples = 50.0%<br/>value = [0.0, 1.0]>, ' \
'fillcolor="#399de5ff"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="False"] ;\n' \
'}'
assert_equal(contents1, contents2)
# Test max_depth
out = StringIO()
export_graphviz(clf, out_file=out, max_depth=0, class_names=True)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box] ;\n' \
'0 [label="X[0] <= 0.0\\ngini = 0.5\\nsamples = 6\\n' \
'value = [3, 3]\\nclass = y[0]"] ;\n' \
'1 [label="(...)"] ;\n' \
'0 -> 1 ;\n' \
'2 [label="(...)"] ;\n' \
'0 -> 2 ;\n' \
'}'
assert_equal(contents1, contents2)
# Test max_depth with plot_options
out = StringIO()
export_graphviz(clf, out_file=out, max_depth=0, filled=True, node_ids=True)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box, style="filled", color="black"] ;\n' \
'0 [label="node #0\\nX[0] <= 0.0\\ngini = 0.5\\n' \
'samples = 6\\nvalue = [3, 3]", fillcolor="#e5813900"] ;\n' \
'1 [label="(...)", fillcolor="#C0C0C0"] ;\n' \
'0 -> 1 ;\n' \
'2 [label="(...)", fillcolor="#C0C0C0"] ;\n' \
'0 -> 2 ;\n' \
'}'
assert_equal(contents1, contents2)
# Test multi-output with weighted samples
clf = DecisionTreeClassifier(max_depth=2,
min_samples_split=2,
criterion="gini",
random_state=2)
clf = clf.fit(X, y2, sample_weight=w)
out = StringIO()
export_graphviz(clf, out_file=out, filled=True, impurity=False)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box, style="filled", color="black"] ;\n' \
'0 [label="X[0] <= 0.0\\nsamples = 6\\n' \
'value = [[3.0, 1.5, 0.0]\\n' \
'[3.0, 1.0, 0.5]]", fillcolor="#e5813900"] ;\n' \
'1 [label="samples = 3\\nvalue = [[3, 0, 0]\\n' \
'[3, 0, 0]]", fillcolor="#e58139ff"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=45, ' \
'headlabel="True"] ;\n' \
'2 [label="X[0] <= 1.5\\nsamples = 3\\n' \
'value = [[0.0, 1.5, 0.0]\\n' \
'[0.0, 1.0, 0.5]]", fillcolor="#e5813986"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="False"] ;\n' \
'3 [label="samples = 2\\nvalue = [[0, 1, 0]\\n' \
'[0, 1, 0]]", fillcolor="#e58139ff"] ;\n' \
'2 -> 3 ;\n' \
'4 [label="samples = 1\\nvalue = [[0.0, 0.5, 0.0]\\n' \
'[0.0, 0.0, 0.5]]", fillcolor="#e58139ff"] ;\n' \
'2 -> 4 ;\n' \
'}'
assert_equal(contents1, contents2)
# Test regression output with plot_options
clf = DecisionTreeRegressor(max_depth=3,
min_samples_split=2,
criterion="mse",
random_state=2)
clf.fit(X, y)
out = StringIO()
export_graphviz(clf,
out_file=out,
filled=True,
leaves_parallel=True,
rotate=True,
rounded=True)
contents1 = out.getvalue()
contents2 = 'digraph Tree {\n' \
'node [shape=box, style="filled, rounded", color="black", ' \
'fontname=helvetica] ;\n' \
'graph [ranksep=equally, splines=polyline] ;\n' \
'edge [fontname=helvetica] ;\n' \
'rankdir=LR ;\n' \
'0 [label="X[0] <= 0.0\\nmse = 1.0\\nsamples = 6\\n' \
'value = 0.0", fillcolor="#e5813980"] ;\n' \
'1 [label="mse = 0.0\\nsamples = 3\\nvalue = -1.0", ' \
'fillcolor="#e5813900"] ;\n' \
'0 -> 1 [labeldistance=2.5, labelangle=-45, ' \
'headlabel="True"] ;\n' \
'2 [label="mse = 0.0\\nsamples = 3\\nvalue = 1.0", ' \
'fillcolor="#e58139ff"] ;\n' \
'0 -> 2 [labeldistance=2.5, labelangle=45, ' \
'headlabel="False"] ;\n' \
'{rank=same ; 0} ;\n' \
'{rank=same ; 1; 2} ;\n' \
'}'
assert_equal(contents1, contents2)
classifier = DecisionTreeClassifier(random_state=0)
params = {"max_depth": range(1, 11)}
scoring_fnc = make_scorer(performance_metric)
grid = GridSearchCV(classifier, param_grid=params, scoring=scoring_fnc, cv=cv_sets)
grid = grid.fit(X, y)
print(pd.DataFrame(grid.cv_results_))
return grid.best_estimator_
reg = fit_model(X_train, y_train)
reg.fit(X_train, y_train)
Z = reg.predict(X_test)
s = pickle.dumps(reg)
print(metrics.confusion_matrix(y_test, Z))
print(metrics.classification_report(y_test, Z))
dot_data = StringIO()
export_graphviz(reg, out_file="dot.dot", feature_names=list(data)[1:], class_names=["edible", "poisonous"])
#export_graphviz(reg, out_file=dot_data, feature_names=list(data)[1:])
# graph_ = pydot.graph_from_dot_data(dot_data.getvalue())
# graph_.write_pdf("tree.pdf")
feature_importances = reg.feature_importances_
fi = dict(zip(feature_importances, list(data)[1:]))
fi_S = sorted(fi.items(), key=operator.itemgetter(0), reverse=True)
print(fi_S)
def _decision_tree_regression_train(
table,
feature_cols,
label_col, # fig_size=np.array([6.4, 4.8]),
criterion='mse',
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,
presort=False,
sample_weight=None,
check_input=True,
X_idx_sorted=None):
regressor = DecisionTreeRegressor(criterion, splitter, max_depth,
min_samples_split, min_samples_leaf,
min_weight_fraction_leaf, max_features,
random_state, max_leaf_nodes,
min_impurity_decrease,
min_impurity_split, presort)
regressor.fit(table[feature_cols], table[label_col], sample_weight,
check_input, X_idx_sorted)
try:
from sklearn.externals.six import StringIO
from sklearn.tree import export_graphviz
import pydotplus
dot_data = StringIO()
export_graphviz(regressor,
out_file=dot_data,
feature_names=feature_cols,
filled=True,
rounded=True,
special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
from brightics.common.repr import png2MD
fig_tree = png2MD(graph.create_png())
except:
fig_tree = "Graphviz is needed to draw a Decision Tree graph. Please download it from http://graphviz.org/download/ and install it to your computer."
# json
model = _model_dict('decision_tree_regression_model')
model['feature_cols'] = feature_cols
model['label_col'] = label_col
feature_importance = regressor.feature_importances_
model['feature_importance'] = feature_importance
model['max_features'] = regressor.max_features_
model['n_features'] = regressor.n_features_
model['n_outputs'] = regressor.n_outputs_
model['tree'] = regressor.tree_
get_param = regressor.get_params()
model['parameters'] = get_param
model['regressor'] = regressor
# report
indices = np.argsort(feature_importance)
sorted_feature_cols = np.array(feature_cols)[indices]
plt.title('Feature Importances')
plt.barh(range(len(indices)),
feature_importance[indices],
color='b',
align='center')
for i, v in enumerate(feature_importance[indices]):
plt.text(v,
i,
" {:.2f}".format(v),
color='b',
va='center',
fontweight='bold')
plt.yticks(range(len(indices)), sorted_feature_cols)
plt.xlabel('Relative Importance')
plt.tight_layout()
fig_feature_importances = plt2MD(plt)
plt.clf()
params = dict2MD(get_param)
feature_importance_df = pd.DataFrame(data=feature_importance,
index=feature_cols).T
# Add tree plot
rb = BrtcReprBuilder()
rb.addMD(
strip_margin("""
| ## Decision Tree Regression Train Result
| ### Decision Tree
| {fig_tree}
|
| ### Feature Importance
| {fig_feature_importances}
|
| ### Parameters
| {list_parameters}
|
""".format(fig_tree=fig_tree,
fig_feature_importances=fig_feature_importances,
list_parameters=params)))
model['_repr_brtc_'] = rb.get()
return {'model': model}
print(predTree[0:5])
print(y_testset[0:5])
#Evaluation
from sklearn import metrics
import matplotlib.pyplot as plt
print("Decision Tree Accuracy: ", metrics.accuracy_score(y_testset, predTree))
#Tree visualization
from sklearn.externals.six import StringIO
import pydotplus
import matplotlib.image as mpimg
from sklearn import tree
dot_data = StringIO()
filename = "DrugTree.png"
feature_names = my_data.columns[0:5]
targetNames = my_data["Drug"].unique().tolist()
out = tree.export_graphviz(drugTree,
feature_names=feature_names,
out_file=dot_data,
class_names=np.unique(y_trainset),
filled=True,
special_characters=True,
rotate=False)
graph = pydotplus.graph_from_dot_data(
dot_data.getvalue()
) #use dot data, which is the string representation of the tree and forms a graph
graph.write_png(filename)
img = mpimg.imread(filename)
labelSet = data.iloc[:, 4]
dataConvertList = {}
labelConvertList = {}
#convert string attribute values to integers
for feat in np.array(dataSet).transpose():
i = 1
for data in feat:
if data not in dataConvertList.keys():
dataConvertList[data] = i
i = i + 1
i = 1
for data in labelSet:
if data not in labelConvertList.keys():
labelConvertList[data] = i
i = i + 1
for key in dataConvertList:
dataSet = dataSet.replace(key, dataConvertList[key])
for key in labelConvertList:
labelSet = labelSet.replace(key, labelConvertList[key])
#train a model
model = tree.DecisionTreeClassifier(criterion='entropy', random_state=0)
s = model.fit(dataSet, labelSet)
#plot the decision tree by pydotplus
tree_file = StringIO()
tree.export_graphviz(model, out_file=tree_file)
graph = pydotplus.graph_from_dot_data(tree_file.getvalue())
graph.write_pdf("tree.pdf")
def product_segment(productslist, customerid):
descriptions = []
mrp = []
categoryofproduct = []
yes_no = []
cat = []
xx='match(c:customerid)-[r:Bought_this]->(s:stockcode) ' \
'set r.Actual_Price=toFloat(r.Actual_Price)' \
'return s.Category,collect(DISTINCT r.Actual_Price) as pricelist ' \
'order by s.Category, pricelist'
pricebucket = session.run(xx)
for i in pricebucket:
# print(i)
actualcategoryspresent.append(i['s.Category'])
if len(i['pricelist']) == 1:
start = (i['pricelist'][0]) - 1
end = (i['pricelist'][0]) + 1
else:
start = min(i['pricelist'])
end = max(i['pricelist'])
bucketsize = (end - start) / 10
starts.append(start)
ends.append(end)
bucketsizes.append(bucketsize)
cat.append(i['s.Category'])
# print('start :',start)
# print('end :',end)
# print('bucket_size:',bucketsize)
for i in productslist:
x='match() - [r:Bought_this]->(s:stockcode{StockCode:"'+i+'"}) ' \
'return distinct s.Description as description, s.Category as category, r.Actual_Price as MRP ' \
'limit 1'
result = session.run(x)
for i in result:
descriptions.append(i['description'])
mrp.append(i['MRP'])
categoryofproduct.append(i['category'])
df1 = pd.DataFrame(
columns=['Description', 'MRP', 'category', 'PriceBucket'])
for i in productslist:
x = 'optional MATCH(c:customerid{CustomerID: "' + customerid + '"})-[r:Bought_this]->(s:stockcode{StockCode: "' + i + '"}) ' \
'return distinct ' \
'case ' \
'when r.Quantity IS NULL THEN 0 ' \
'when r.Quantity IS NOT NULL THEN 1 ' \
'else r.Quantity END AS Quantity '
for ii in session.run(x):
yes_no.append(ii[0])
for i in range(0, len(productslist)):
# print('=======================================================================================================')
# print('StockCode of product :',productslist[i])
# print('Description of product :',descriptions[i])
# print('MRP :',mrp[i])
# print('Category of product :',categoryofproduct[i])
# if categoryofproduct[i] in cat:
vv = cat.index(categoryofproduct[i])
z = price_bucket_of_product(starts[vv], ends[vv], bucketsizes[vv],
mrp[i])
# print('Price bucket :',z)
# print('yes_no :',yes_no[i])
df1 = df1.append(
{
'Description': descriptions[i],
'MRP': mrp[i],
'category': categoryofproduct[i],
'PriceBucket': z
},
ignore_index=True)
# hot_descp = pd.get_dummies(df1.Description)
# df1 = df1.join(hot_descp)
df1 = df1.drop('Description', axis=1)
hot_category = pd.get_dummies(df1.category)
df1 = df1.join(hot_category)
df1 = df1.drop('category', axis=1)
hot_PriceBucket = pd.get_dummies(df1.PriceBucket)
df1 = df1.join(hot_PriceBucket)
df1 = df1.drop('PriceBucket', axis=1)
# print(df1)
data = df1.values
train_target = yes_no
train_data = data
# print(train_target)
# print(train_data)
x = tree.DecisionTreeClassifier()
x.fit(train_data, train_target)
# print(x)
dot_data = StringIO()
tree.export_graphviz(x,
out_file=dot_data,
feature_names=df1.columns.tolist(),
class_names=['No', 'Yes'],
filled=True,
rounded=True,
impurity=False)
for i in dot_data:
print(i)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_png('D:\project\search\static\stuff/segment.png')
print("")
print("Data 1's Upper p value with Gini -> ", gini_upper_p_data1)
print("")
print("Data 2's Lower p value with Gini -> ", gini_lower_p_data2)
print("")
print("Data 2's Upper p value with Gini -> ", gini_upper_p_data2)
print("")
#Part i
data_1_col_names = [
"age", "job", "marital", "education", "balance", "housing", "duration",
"poutcome"
]
data_2_col_names = ["job", "marital", "education", "housing"]
dot_data1_entropy = StringIO()
dot_data2_entropy = StringIO()
dot_data1_gini = StringIO()
dot_data2_gini = StringIO()
export_graphviz(entropy_data_1,
out_file=dot_data1_entropy,
filled=True,
rounded=True,
special_characters=True,
feature_names=data_1_col_names,
class_names=["0", "1"])
data_1_entropy_graph = pydotplus.graph_from_dot_data(
dot_data1_entropy.getvalue())
data_1_entropy_graph.write_png('data_1_entropy.png')
Image(data_1_entropy_graph.create_png())
def wlasne_drzewo():
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("my-tree.pdf")
RMSECvAll.append(math.sqrt( sum( (Originaly-PredictedYcv)**2 )/ OriginalX.shape[0]))
plt.figure()
plt.plot(CandidatesOfDTDepth, RMSECvAll, 'k', linewidth=2)
plt.xlabel("Depth of tree for DT")
plt.ylabel("RMSE in CV for DT")
plt.show()
OptimalMaxDepthDT = CandidatesOfDTDepth[np.where( RMSECvAll == np.min(RMSECvAll) )[0][0] ]
DTResult = tree.DecisionTreeRegressor(max_depth=OptimalMaxDepthDT, min_samples_leaf=MinSamplesLeafDT)
DTResult.fit( OriginalX, Originaly )
CalculatedYAll[:,7] = DTResult.predict(OriginalX)
np.random.seed(10000)
PredictedYcvAll[:,7] = model_selection.cross_val_predict(DTResult, OriginalX, Originaly, cv=FoldNumber)
np.random.seed()
# Check rules of DT
datapdDT = pd.read_csv("data.csv", encoding='SHIFT-JIS', index_col=0)
with contextlib.closing(StringIO()) as DTfile:
tree.export_graphviz(DTResult, out_file=DTfile,
feature_names=datapdDT.columns[1:],
class_names=datapdDT.columns[0])
output = DTfile.getvalue().splitlines()
output.insert(1, 'node[fontname="meiryo"];')
with open('DTResult.dot', 'w') as f:
f.write('\n'.join(output))
# Estimate Y for new samples based on DT in 1. and 2.
PredictedY1All[:,7] = DTResult.predict(OriginalX_prediction1)
PredictedY2All[:,7] = DTResult.predict(OriginalX_prediction2)
# 9. Random Forest (RF)
NumberOfTreesRF = 500 # 1. Number of decision trees
CandidatesOfXvariablesRateRF = np.arange( 1, 10, dtype=float)/10 #candidates of the ratio of the number of explanatory variables (X) for decision trees
# Run RFR for every candidate of X-ratio and estimate values of objective variable (Y) for Out Of Bag (OOB) samples
def plot_tree(clf, file_name, **kwargs):
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data, **kwargs)
graph = pydot.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf(file_name)
def main(data):
y = data['Species']
print(" ")
print("Before going into our task do you want to check out the data ? ")
print(" ")
print("y- Yes")
print("n-No")
choice = input("Select your choice : ")
choice = choice.lower()
if (('y' in choice) or ('yes' in choice)):
print(" ")
print(" ")
print("Ok ! Let's explore the data")
print('\nOur dataset looks like : \n', data.head())
print(" ")
print('\nThe shape of the data is: ', data.shape)
print(" ")
print("\nWhat about the datatypes: \n", data.dtypes)
print(" ")
print("\nThe whole data can be described as : \n", data.describe())
print(" ")
print(" ")
from sklearn.tree import DecisionTreeClassifier
from sklearn.externals.six import StringIO
from sklearn.tree import export_graphviz
import pydotplus
print(" ")
print(" ")
print("Ok, now let's train the model and make predictions using it")
print(" ")
print(" ")
print("\nDivide the data into attributes(inputs) and labels(outputs)")
x = data.iloc[:, [0, 1, 2, 3]].values
le = LabelEncoder()
data['Species'] = le.fit_transform(data['Species'])
y = data['Species'].values
print("\nAttributes:\n", x)
print("\nLables :", y)
print("Next step is to split this data into training and test sets.")
print(" ")
print(" ")
print("\nTrain-Test-Split : ")
print("The test size in default is 20. Would you like to change it ?")
print("y- Yes")
print("n- No")
size = input("\nYour choice : ")
if (('y' in size) or ('yes' in size)):
tsize = int(input("Specify the test size you want : "))
tsize /= 100
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=tsize,
random_state=0)
print("Splitted with test size ", tsize)
else:
x_train, x_test, y_train, y_test = train_test_split(x,
y,
test_size=0.2,
random_state=0)
print("Splitted with default test size")
print(" ")
print(" ")
print("\nLets explore the splitted data : ")
print("X_Train data : ", x_train.shape)
print("X_Test data : ", x_test.shape)
print("Y_Train data : ", y_train.shape)
print("Y_Test data : ", y_test.shape)
print(y_test)
print(" ")
print(" ")
print("Training the Algorithm")
print("We are going to train the model")
print("Which training method do you want ?")
print("g - Gini")
print("e - Entropy")
method = input("\nYour choice : ")
method = method.lower()
if (('g' in method) or ('gini' in method)):
dtmodel = DecisionTreeClassifier(criterion="gini",
random_state=0,
max_depth=3,
min_samples_leaf=5)
dtmodel.fit(x_train, y_train)
elif (('e' in method) or ('entropy' in method)):
dtmodel = DecisionTreeClassifier(criterion='entropy',
random_state=0,
max_depth=3,
min_samples_leaf=5)
dtmodel.fit(x_train, y_train)
else:
print("Wrong Choice")
return ()
print(" ")
print(" ")
print("Testing the Algorithm")
y_pred = dtmodel.predict(x_test)
print("Predicted values:")
print(y_pred)
print("Completed")
print("Accuracy:", accuracy_score(y_test, y_pred) * 100)
print("Report:", classification_report(y_test, y_pred))
print("Confusion Matrix : ")
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(9, 9))
sns.heatmap(cm,
annot=True,
fmt=".3f",
linewidths=.5,
square=True,
cmap='Blues')
plt.ylabel('Actual label')
plt.xlabel('Predicted label')
all_sample_title = 'Accuracy Score: {0}'.format(
dtmodel.score(x_test, y_test))
plt.title(all_sample_title, size=15)
plt.show()
print(" ")
print(" ")
print("Now, let's visualize the Decision Tree to understand it better.")
df = data.copy()
df = df.drop('Species', axis=1)
dot_data = StringIO()
export_graphviz(dtmodel,
out_file=dot_data,
feature_names=df.columns,
filled=True,
rounded=True,
special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_png("dtree.png")
im = Image.open(r"dtree.png")
im.show()
print(" ")
print(" ")
Flag = True
while Flag == True:
print("\nWould you like to give try another input ?")
print("y - Yes")
print("n - No")
sp = input("Your answer : ")
sp = sp.lower()
if (('y' in sp) or ('yes' in sp)):
spe = []
slen = float(input("Give Sepal Length in cm : "))
spe.append(slen)
swid = float(input("Give Sepal width in cm : "))
spe.append(swid)
plen = float(input("Give Petal Length in cm : "))
spe.append(plen)
pwid = float(input("Give Petal width in cm : "))
spe.append(pwid)
y_pred = dtmodel.predict([spe])
print(" ")
print(" ")
print("Species according to encoding : ")
print("0 - Iris-setosa")
print("1 - Iris-versicolor")
print("2 - Iris-virginica")
print(" ")
print(" ")
print("The predicted species is ", y_pred)
print("Were you expecting the same ?")
print("OK")
print("Do you want to try again ?")
print("y - Yes")
print("n - No")
ans = input("Your choice : ")
if (('y' in ans) or ('yes' in ans)):
Flag = True
else:
Flag = False
else:
Flag = False
print(" ")
print(" ")
print("Yippee... We learned to use Decision Tree")
print("I really had fun")
print("Hope you enjoyed it too")
print("Bye")
iris = load_iris()
df = pd.DataFrame(data=np.c_[iris['data'], iris['target']],
columns=iris['feature_names'] + ['target'])
# df['label'] = df.target.replace(dict(enumerate(df.target_names)))
print(df.head()) # to check the top results
print(iris.feature_names)
print(iris.target_names)
print(df.describe()) # to check difference between min and maxmium value
x = iris['data']
y = iris['target']
iris_df = pd.DataFrame(x, columns=iris['feature_names'])
print(iris_df.head)
x, y = shuffle(x, y, random_state=0) # random shuffle
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)
classifier=DecisionTreeClassifier(criterion="entropy", max_depth=3) # To check accuracy ,applied algorithm
clf = classifier.fit(x_train,y_train)
y_pred = classifier.predict(x_test)
print("Accuracy:",metrics.accuracy_score(y_test, y_pred)) # accuracy result shoecase in console
dot_data = StringIO()
tree.export_graphviz(classifier,
out_file=dot_data,
feature_names=iris.feature_names,
class_names=iris.target_names,
filled=True, rounded=True,
impurity=False,
proportion=True)
graph=pydot.graph_from_dot_data(dot_data.getvalue()) # plotting the graph
graph[0].write_pdf("iris3.pdf") # run the file.
def train():
balance_data_excel = DecisionTreeQuestionnaire.get_csv_file_data()
''' Clean the Data and replace with nan
'''
balance_data_excel = balance_data_excel.replace(r'^\s*$',
str(np.nan),
regex=True).replace(
'', str(np.nan))
balance_data_excel = balance_data_excel.applymap(str)
balance_data_excel
print("Dataset Length:: ", len(balance_data_excel))
print("Dataset Shape:: ", balance_data_excel.shape)
X = balance_data_excel.iloc[:, :-1]
y = balance_data_excel.iloc[:, 22]
X = DecisionTreeQuestionnaire.encode_onehot(
X,
X.columns.get_values().tolist())
X.head()
le_y = LabelEncoder()
y = le_y.fit_transform(y)
cols = X.columns
for c in cols:
x = c
if x.split('=')[1] == 'nan':
X.drop(c, axis=1, inplace=True)
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.1,
random_state=100)
clf_entropy = DecisionTreeClassifier(criterion="entropy",
random_state=100,
max_depth=100,
min_samples_leaf=5,
min_samples_split=8)
abc = clf_entropy.fit(X_train, y_train)
y_pred = clf_entropy.predict(X_test)
print("Accuracy is ", accuracy_score(y_test, y_pred) * 100)
''' Convert Target Classes to key-value pairs
'''
print("le2")
class_names = {}
for i in range(len(le_y.classes_)):
class_names[i] = le_y.classes_[i]
print(class_names)
features = {}
for i in range(len(list(X.columns[0:56]))):
features[i] = X.columns[i]
#features
for i in range(len(list(X.columns[0:56]))):
DecisionTreeQuestionnaire.feature_names.append(X.columns[i])
#feature_names
dot_data = StringIO()
export_graphviz(clf_entropy,
out_file=dot_data,
filled=True,
rounded=True,
special_characters=True,
feature_names=features,
class_names=class_names)
# graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph = pydot.graph_from_dot_data(dot_data.getvalue())
# graph_from_dot_file()
graph.write_png('decisiontree.png')
DecisionTreeQuestionnaire.tree_to_code(abc)
DecisionTreeQuestionnaire.tree_to_code2(abc, class_names)
DecisionTreeQuestionnaire.paths
DecisionTreeQuestionnaire.isModelTrained = True
for i in DecisionTreeQuestionnaire.paths:
for k, v in i.items():
print(k + ' : ' + v)
print("-----------------------------------")
print('Your inputs are not defined')
def printTree(clf):
dot_data = StringIO()
tree.export_graphviz(clf, out_file=dot_data, filled=True, rounded=True,
special_characters=True)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
return graph.write_png('tree.png')
def decision_tree_training(self):
self.target_names = ['lying', 'lie on the side', 'sitting', 'standing']
self.feature_names = [
'1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12',
'13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23',
'24', '25', '26', '27', '28', '29', '30', '31', '32', '33'
]
print('---start training decision tree---')
#split dataset in two equal parts
#print(np.shape(self.feature), np.shape(self.label))
X_train, X_test, Y_train, Y_test = train_test_split(self.feature,
self.label,
test_size=0.25,
random_state=0)
np.savetxt(
'/home/hts/posture_classification_based_pose/decision_tree/X_train.txt',
X_train,
fmt='%f')
np.savetxt(
'/home/hts/posture_classification_based_pose/decision_tree/X_test.txt',
X_test,
fmt='%f')
np.savetxt(
'/home/hts/posture_classification_based_pose/decision_tree/Y_train.txt',
Y_train,
fmt='%d')
np.savetxt(
'/home/hts/posture_classification_based_pose/decision_tree/Y_test.txt',
Y_test,
fmt='%d')
print('---split data done!---')
print()
clf = DecisionTreeClassifier(criterion='gini',
random_state=0) # 默认使用CART算法
print(np.shape(X_train), np.shape(Y_train.ravel()))
clf.fit(X_train, Y_train.ravel())
# cross_val_score(classifier, X_train, Y_train, cv=5)
# visualization
dot_data = StringIO()
tree.export_graphviz(clf,
out_file=dot_data,
feature_names=self.feature_names,
class_names=self.target_names,
filled=True,
rounded=True,
impurity=False)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("decision_tree.pdf")
# classifier.fit(X_train, Y_train)
#验证测试集
print("Detailed classification report:")
print()
print("The model is trained on the full development set.")
print("The scores are computed on the full evaluation set.")
print()
Y_true, Y_pred = Y_test.ravel(), clf.predict(X_test)
np.savetxt(
'/home/hts/posture_classification_based_pose/decision_tree/Y_true.txt',
Y_true,
fmt='%d')
np.savetxt(
'/home/hts/posture_classification_based_pose/decision_tree/Y_pred.txt',
Y_pred,
fmt='%d')
print(
classification_report(Y_true,
Y_pred,
target_names=self.target_names))
print()
print('Decision Tree model saving ......')
model_save_path = '/home/hts/posture_classification_based_pose/decision_tree/train_decision_tree_model.m'
joblib.dump(clf, model_save_path)
return
