def evaluatePOIidentifier():
import pickle
import sys
sys.path.append("../tools/")
from feature_format import featureFormat, targetFeatureSplit
data_dict = pickle.load(
open("../final_project/final_project_dataset.pkl", "rb"))
### first element is our labels, any added elements are predictor
### features. Keep this the same for the mini-project, but you'll
### have a different feature list when you do the final project.
features_list = ["poi", "salary"]
data = featureFormat(data_dict,
features_list,
sort_keys='../tools/python2_lesson14_keys.pkl')
# data = featureFormat(data_dict, features_list, sort_keys = True)
labels, features = targetFeatureSplit(data)
### it's all yours from here forward!
### Decision Tree
from time import time
from sklearn import tree
### Using min_samples_split = 2 accuracy = 90.8%
### Using min_samples_split = 50 accuracy = 91.2%
#clf = tree.DecisionTreeClassifier(min_samples_split=40)
clf = tree.DecisionTreeClassifier()
t0 = time()
clf.fit(features, labels)
print("training time for all data:", round(time() - t0, 3), "s")
### print accuracy
print("all data accuracy: ", clf.score(features, labels))
# from email_preprocess import preprocess
from classifyDT import classify
from sklearn.model_selection import train_test_split
features_train, features_test, labels_train, labels_test = \
train_test_split(features, labels, test_size=0.3, random_state=42)
### features_train and features_test are the features for the training
### and testing datasets, respectively
### labels_train and labels_test are the corresponding item labels
# features_train, features_test, labels_train, labels_test = preprocess()
clf = classify(features_train, labels_train, features_test, labels_test)
### expected result was 0.724
print("#Features in data: ", len(features_train[0]))
sys.path.append("../tools/")
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData
import matplotlib.pyplot as plt
import numpy as np
import pylab as pl
from classifyDT import classify
features_train, labels_train, features_test, labels_test = makeTerrainData()
### the classify() function in classifyDT is where the magic
### happens--fill in this function in the file 'classifyDT.py'!
clf = classify(features_train, labels_train)
#### store your predictions in a list named pred
pred = clf.predict(features_test)
from sklearn.metrics import accuracy_score
acc = accuracy_score(pred, labels_test)
#### grader code, do not modify below this line
prettyPicture(clf, features_test, labels_test)
def submitAccuracy():
return acc
print(submitAccuracy())
import sys
from class_vis import prettyPicture, output_image
from prep_terrain_data import makeTerrainData
import matplotlib.pyplot as plt
import numpy as np
import pylab as pl
from classifyDT import classify
features_train, labels_train, features_test, labels_test = makeTerrainData()
### the classify() function in classifyDT is where the magic
### happens--it's your job to fill this in!
clf = classify(features_train, labels_train)
#### grader code, do not modify below this line
prettyPicture(clf, features_test, labels_test)
output_image("test.png", "png", open("test.png", "rb").read())
print clf.score(features_test, labels_test)
#!/usr/bin/python
"""
This is the code to accompany the Lesson 3 (decision tree) mini-project.
Use a Decision Tree to identify emails from the Enron corpus by author:
Sara has label 0
Chris has label 1
"""
import sys
sys.path.append("../tools/")
from email_preprocess import preprocess
from classifyDT import classify
### features_train and features_test are the features for the training
### and testing datasets, respectively
### labels_train and labels_test are the corresponding item labels
features_train, features_test, labels_train, labels_test = preprocess()
clf = classify(features_train, labels_train, features_test, labels_test)
print("#Features in data: ", len(features_train[0]))
#########################################################
### your code goes here ###
#########################################################
#!/usr/bin/python
""" lecture and example code for decision tree unit """
import sys
from class_vis import prettyPicture, output_image
from prep_terrain_data import makeTerrainData
import matplotlib.pyplot as plt
import numpy as np
import pylab as pl
from classifyDT import classify
from sklearn.metrics import accuracy_score
features_train, labels_train, features_test, labels_test = makeTerrainData()
### the classify() function in classifyDT is where the magic
### happens--fill in this function in the file 'classifyDT.py'!
clf2 = classify(features_train, labels_train, 2)
clf50 = classify(features_train, labels_train, 50)
#### grader code, do not modify below this line
prettyPicture(clf2, features_test, labels_test)
output_image("test.png", "png", open("test.png", "rb").read())
acc_min_samples_split_2 = accuracy_score(clf2.predict(features_test), labels_test)
acc_min_samples_split_50 = accuracy_score(clf50.predict(features_test), labels_test)
print "acc_min_samples_split_2:",round(acc_min_samples_split_2,3)
print "acc_min_samples_split_50:",round(acc_min_samples_split_50,3)
def submitAccuracies():
return {
"acc_min_samples_split_2": round(acc_min_samples_split_2, 3),
"acc_min_samples_split_50": round(acc_min_samples_split_50, 3)
}
########################## DECISION TREE #################################
### your code goes here--now create 2 decision tree classifiers,
### one with min_samples_split=2 and one with min_samples_split=50
### compute the accuracies on the testing data and store
### the accuracy numbers to acc_min_samples_split_2 and
### acc_min_samples_split_5, respectively
from classifyDT import classify
from sklearn.metrics.metrics import accuracy_score
clf = classify(features_train, labels_train, 50.0)
pred = clf.predict_proba(features_test)
roundedNumber = []
for i in range(0, len(pred)):
roundedNumber.append(round(pred[i, 1]))
acc_min_samples_split_50 = accuracy_score(labels_test,
roundedNumber) ### you fill this in!
clf = classify(features_train, labels_train, 2.0)
pred = clf.predict_proba(features_test)
acc_min_samples_split_2 = accuracy_score(labels_test,
pred[:, 1]) ### you fill this in!
print submitAccuracies()
from class_vis import prettyPicture, output_image from prep_terrain_data import makeTerrainData import matplotlib.pyplot as plt import numpy as np import pylab as pl from classifyDT import classify features_train, labels_train, features_test, labels_test = makeTerrainData() ### the classify() function in classifyDT is where the magic ### happens--fill in this function in the file 'classifyDT.py'! minsplit = 2 clf1 = classify(features_train, labels_train, minsplit) minsplit = 50 clf2 = classify(features_train, labels_train, minsplit) ''' different ways to print the accuracy of the GNB classifier ''' #print clf1.score(features_test,labels_test) # find the prediction score for the test data pred1 = clf1.predict(features_test) from sklearn.metrics import accuracy_score acc_min_samples_split_2 = accuracy_score(labels_test, pred1) print acc_min_samples_split_2 #print clf2.score(features_test,labels_test) # find the prediction score for the test data pred2 = clf2.predict(features_test) from sklearn.metrics import accuracy_score acc_min_samples_split_50 = accuracy_score(labels_test, pred2)
features_train, labels_train, features_test, labels_test = makeTerrainData()
def submitAccuracies():
return {"acc_min_samples_split_2":round(acc_min_samples_split_2,3),
"acc_min_samples_split_50":round(acc_min_samples_split_50,3)}
########################## DECISION TREE #################################
### your code goes here--now create 2 decision tree classifiers,
### one with min_samples_split=2 and one with min_samples_split=50
### compute the accuracies on the testing data and store
### the accuracy numbers to acc_min_samples_split_2 and
### acc_min_samples_split_5, respectively
from classifyDT import classify
from sklearn.metrics.metrics import accuracy_score
clf = classify(features_train, labels_train,50.0)
pred = clf.predict_proba(features_test)
roundedNumber = []
for i in range(0,len(pred)):
roundedNumber.append(round(pred[i,1]))
acc_min_samples_split_50 = accuracy_score(labels_test,roundedNumber)### you fill this in!
clf = classify(features_train, labels_train,2.0)
pred = clf.predict_proba(features_test)
acc_min_samples_split_2 = accuracy_score(labels_test,pred[:,1])### you fill this in!
print submitAccuracies()
except ValueError:
return False
features_train, labels_train, features_test, labels_test = makeTerrainData()
#adding taking parameter from the commend line to have different min split sample
s = sys.argv[1] #taking the first parameter
if (RepresentsInt(s) == True):
min_sample_split = int(s)
else:
min_sample_split = 2 #default value
### the classify() function in classifyDT is where the magic
### happens--it's your job to fill this in!
clf = classify(features_train, labels_train, min_sample_split)
#getting the acuuracy
pred = clf.predict(features_test)
from sklearn.metrics import accuracy_score
acc = accuracy_score(pred, labels_test)
prettyPicture(clf, features_test, labels_test)
output_image("test.png", "png", open("test.png", "rb").read())
plt.show() #I add this line to show the image from matplotlib
#accuracy result will be shown after the show window is close
print ('Accuacy from decision tree = ', acc)
# print 'Number of arguments:', len(sys.argv), 'arguments.'
# print 'Argument List:', str(sys.argv)
import sys
import time
from class_vis import prettyPicture
from prep_terrain_data import makeTerrainData
import numpy as np
import pylab as pl
features_train, labels_train, features_test, labels_test = makeTerrainData()
#################################################################################
########################## DECISION TREE #################################
#### your code goes here
from classifyDT import classify
Tree_Test = classify(features_train, labels_train)
t0 = time()
terrain_pred = Tree_Test.predict(features_test)
print("prediction time:", round(time() - t0, 3), "s")
print(terrain_pred)
from sklearn.metrics import accuracy_score
acc = accuracy_score(labels_test, terrain_pred)
### be sure to compute the accuracy on the test set
def submitAccuracies():
return {"acc": round(acc, 3)}
