Chapter 3 – Classification
This notebook contains all the sample code and solutions to the exercises in chapter 3.
 Run in Google Colab
|
Warning: this is the code for the 1st edition of the book. Please visit https://github.com/ageron/handson-ml2 for the 2nd edition code, with up-to-date notebooks using the latest library versions.
Setup¶
First, let's make sure this notebook works well in both python 2 and 3, import a few common modules, ensure MatplotLib plots figures inline and prepare a function to save the figures:
In [1]:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rc('axes', labelsize=14)
mpl.rc('xtick', labelsize=12)
mpl.rc('ytick', labelsize=12)
# Where to save the figures
PROJECT_ROOT_DIR = "."
CHAPTER_ID = "classification"
IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID)
os.makedirs(IMAGES_PATH, exist_ok=True)
def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300):
path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension)
print("Saving figure", fig_id)
if tight_layout:
plt.tight_layout()
plt.savefig(path, format=fig_extension, dpi=resolution)
MNIST¶
Warning: fetch_mldata() is deprecated since Scikit-Learn 0.20. You should use fetch_openml() instead. However, it returns the unsorted MNIST dataset, whereas fetch_mldata() returned the dataset sorted by target (the training set and the test test were sorted separately). In general, this is fine, but if you want to get the exact same results as before, you need to sort the dataset using the following function:
In [2]:
def sort_by_target(mnist):
reorder_train = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[:60000])]))[:, 1]
reorder_test = np.array(sorted([(target, i) for i, target in enumerate(mnist.target[60000:])]))[:, 1]
mnist.data[:60000] = mnist.data[reorder_train]
mnist.target[:60000] = mnist.target[reorder_train]
mnist.data[60000:] = mnist.data[reorder_test + 60000]
mnist.target[60000:] = mnist.target[reorder_test + 60000]
In [3]:
try:
from sklearn.datasets import fetch_openml
mnist = fetch_openml('mnist_784', version=1, cache=True, as_frame=False)
mnist.target = mnist.target.astype(np.int8) # fetch_openml() returns targets as strings
sort_by_target(mnist) # fetch_openml() returns an unsorted dataset
except ImportError:
from sklearn.datasets import fetch_mldata
mnist = fetch_mldata('MNIST original')
mnist["data"], mnist["target"]
Out[3]:
(array([[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
...,
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.],
[0., 0., 0., ..., 0., 0., 0.]]),
array([0, 0, 0, ..., 9, 9, 9], dtype=int8))
In [4]:
mnist.data.shape
Out[4]:
(70000, 784)
In [5]:
X, y = mnist["data"], mnist["target"]
X.shape
Out[5]:
(70000, 784)
In [6]:
y.shape
Out[6]:
(70000,)
In [7]:
28*28
Out[7]:
784
In [8]:
some_digit = X[36000]
some_digit_image = some_digit.reshape(28, 28)
plt.imshow(some_digit_image, cmap = mpl.cm.binary,
interpolation="nearest")
plt.axis("off")
save_fig("some_digit_plot")
plt.show()
Saving figure some_digit_plot
In [9]:
def plot_digit(data):
image = data.reshape(28, 28)
plt.imshow(image, cmap = mpl.cm.binary,
interpolation="nearest")
plt.axis("off")
In [10]:
# EXTRA
def plot_digits(instances, images_per_row=10, **options):
size = 28
images_per_row = min(len(instances), images_per_row)
images = [instance.reshape(size,size) for instance in instances]
n_rows = (len(instances) - 1) // images_per_row + 1
row_images = []
n_empty = n_rows * images_per_row - len(instances)
images.append(np.zeros((size, size * n_empty)))
for row in range(n_rows):
rimages = images[row * images_per_row : (row + 1) * images_per_row]
row_images.append(np.concatenate(rimages, axis=1))
image = np.concatenate(row_images, axis=0)
plt.imshow(image, cmap = mpl.cm.binary, **options)
plt.axis("off")
In [11]:
plt.figure(figsize=(9,9))
example_images = np.r_[X[:12000:600], X[13000:30600:600], X[30600:60000:590]]
plot_digits(example_images, images_per_row=10)
save_fig("more_digits_plot")
plt.show()
Saving figure more_digits_plot
In [12]:
y[36000]
Out[12]:
5
In [13]:
X_train, X_test, y_train, y_test = X[:60000], X[60000:], y[:60000], y[60000:]
In [14]:
import numpy as np
shuffle_index = np.random.permutation(60000)
X_train, y_train = X_train[shuffle_index], y_train[shuffle_index]
Binary classifier¶
In [15]:
y_train_5 = (y_train == 5)
y_test_5 = (y_test == 5)
Note: a few hyperparameters will have a different default value in future versions of Scikit-Learn, so a warning is issued if you do not set them explicitly. This is why we set max_iter=5 and tol=-np.infty, to get the same results as in the book, while avoiding the warnings.
In [16]:
from sklearn.linear_model import SGDClassifier
sgd_clf = SGDClassifier(max_iter=5, tol=-np.infty, random_state=42)
sgd_clf.fit(X_train, y_train_5)
Out[16]:
SGDClassifier(alpha=0.0001, average=False, class_weight=None,
early_stopping=False, epsilon=0.1, eta0=0.0, fit_intercept=True,
l1_ratio=0.15, learning_rate='optimal', loss='hinge', max_iter=5,
n_iter=None, n_iter_no_change=5, n_jobs=None, penalty='l2',
power_t=0.5, random_state=42, shuffle=True, tol=-inf,
validation_fraction=0.1, verbose=0, warm_start=False)
In [17]:
sgd_clf.predict([some_digit])
Out[17]:
array([ True])
In [18]:
from sklearn.model_selection import cross_val_score
cross_val_score(sgd_clf, X_train, y_train_5, cv=3, scoring="accuracy")
Out[18]:
array([0.9502 , 0.96565, 0.96495])
In [19]:
from sklearn.model_selection import StratifiedKFold
from sklearn.base import clone
skfolds = StratifiedKFold(n_splits=3, random_state=42, shuffle=True)
for train_index, test_index in skfolds.split(X_train, y_train_5):
clone_clf = clone(sgd_clf)
X_train_folds = X_train[train_index]
y_train_folds = (y_train_5[train_index])
X_test_fold = X_train[test_index]
y_test_fold = (y_train_5[test_index])
clone_clf.fit(X_train_folds, y_train_folds)
y_pred = clone_clf.predict(X_test_fold)
n_correct = sum(y_pred == y_test_fold)
print(n_correct / len(y_pred))
0.9502 0.96565 0.96495
In [20]:
from sklearn.base import BaseEstimator
class Never5Classifier(BaseEstimator):
def fit(self, X, y=None):
pass
def predict(self, X):
return np.zeros((len(X), 1), dtype=bool)
In [21]:
never_5_clf = Never5Classifier()
cross_val_score(never_5_clf, X_train, y_train_5, cv=3, scoring="accuracy")
Out[21]:
array([0.909 , 0.90715, 0.9128 ])
In [22]:
from sklearn.model_selection import cross_val_predict
y_train_pred = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3)
In [23]:
from sklearn.metrics import confusion_matrix
confusion_matrix(y_train_5, y_train_pred)
Out[23]:
array([[53272, 1307],
[ 1077, 4344]])
In [24]:
y_train_perfect_predictions = y_train_5
In [25]:
confusion_matrix(y_train_5, y_train_perfect_predictions)
Out[25]:
array([[54579, 0],
[ 0, 5421]])
In [26]:
from sklearn.metrics import precision_score, recall_score
precision_score(y_train_5, y_train_pred)
Out[26]:
0.7687135020350381
In [27]:
4344 / (4344 + 1307)
Out[27]:
0.7687135020350381
In [28]:
recall_score(y_train_5, y_train_pred)
Out[28]:
0.801328168234643
In [29]:
4344 / (4344 + 1077)
Out[29]:
0.801328168234643
In [30]:
from sklearn.metrics import f1_score
f1_score(y_train_5, y_train_pred)
Out[30]:
0.7846820809248555
In [31]:
4344 / (4344 + (1077 + 1307)/2)
Out[31]:
0.7846820809248555
In [32]:
y_scores = sgd_clf.decision_function([some_digit])
y_scores
Out[32]:
array([161855.74572176])
In [33]:
threshold = 0
y_some_digit_pred = (y_scores > threshold)
In [34]:
y_some_digit_pred
Out[34]:
array([ True])
In [35]:
threshold = 200000
y_some_digit_pred = (y_scores > threshold)
y_some_digit_pred
Out[35]:
array([False])
In [36]:
y_scores = cross_val_predict(sgd_clf, X_train, y_train_5, cv=3,
method="decision_function")
Note: there was an issue in Scikit-Learn 0.19.0 (fixed in 0.19.1) where the result of cross_val_predict() was incorrect in the binary classification case when using method="decision_function", as in the code above. The resulting array had an extra first dimension full of 0s. Just in case you are using 0.19.0, we need to add this small hack to work around this issue:
In [37]:
y_scores.shape
Out[37]:
(60000,)
In [38]:
# hack to work around issue #9589 in Scikit-Learn 0.19.0
if y_scores.ndim == 2:
y_scores = y_scores[:, 1]
In [39]:
from sklearn.metrics import precision_recall_curve
precisions, recalls, thresholds = precision_recall_curve(y_train_5, y_scores)
In [40]:
def plot_precision_recall_vs_threshold(precisions, recalls, thresholds):
plt.plot(thresholds, precisions[:-1], "b--", label="Precision", linewidth=2)
plt.plot(thresholds, recalls[:-1], "g-", label="Recall", linewidth=2)
plt.xlabel("Threshold", fontsize=16)
plt.legend(loc="upper left", fontsize=16)
plt.ylim([0, 1])
plt.figure(figsize=(8, 4))
plot_precision_recall_vs_threshold(precisions, recalls, thresholds)
plt.xlim([-700000, 700000])
save_fig("precision_recall_vs_threshold_plot")
plt.show()
Saving figure precision_recall_vs_threshold_plot
In [41]:
(y_train_pred == (y_scores > 0)).all()
Out[41]:
True
In [42]:
y_train_pred_90 = (y_scores > 70000)
In [43]:
precision_score(y_train_5, y_train_pred_90)
Out[43]:
0.8659205116491548
In [44]:
recall_score(y_train_5, y_train_pred_90)
Out[44]:
0.6993174691016417
In [45]:
def plot_precision_vs_recall(precisions, recalls):
plt.plot(recalls, precisions, "b-", linewidth=2)
plt.xlabel("Recall", fontsize=16)
plt.ylabel("Precision", fontsize=16)
plt.axis([0, 1, 0, 1])
plt.figure(figsize=(8, 6))
plot_precision_vs_recall(precisions, recalls)
save_fig("precision_vs_recall_plot")
plt.show()
Saving figure precision_vs_recall_plot
ROC curves¶
In [46]:
from sklearn.metrics import roc_curve
fpr, tpr, thresholds = roc_curve(y_train_5, y_scores)
In [47]:
def plot_roc_curve(fpr, tpr, label=None):
plt.plot(fpr, tpr, linewidth=2, label=label)
plt.plot([0, 1], [0, 1], 'k--')
plt.axis([0, 1, 0, 1])
plt.xlabel('False Positive Rate', fontsize=16)
plt.ylabel('True Positive Rate', fontsize=16)
plt.figure(figsize=(8, 6))
plot_roc_curve(fpr, tpr)
save_fig("roc_curve_plot")
plt.show()
Saving figure roc_curve_plot
In [48]:
from sklearn.metrics import roc_auc_score
roc_auc_score(y_train_5, y_scores)
Out[48]:
0.9624496555967156
Note: we set n_estimators=10 to avoid a warning about the fact that its default value will be set to 100 in Scikit-Learn 0.22.
In [49]:
from sklearn.ensemble import RandomForestClassifier
forest_clf = RandomForestClassifier(n_estimators=10, random_state=42)
y_probas_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3,
method="predict_proba")
In [50]:
y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class
fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train_5,y_scores_forest)
In [51]:
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, "b:", linewidth=2, label="SGD")
plot_roc_curve(fpr_forest, tpr_forest, "Random Forest")
plt.legend(loc="lower right", fontsize=16)
save_fig("roc_curve_comparison_plot")
plt.show()
Saving figure roc_curve_comparison_plot
In [52]:
roc_auc_score(y_train_5, y_scores_forest)
Out[52]:
0.9931243366003829
In [53]:
y_train_pred_forest = cross_val_predict(forest_clf, X_train, y_train_5, cv=3)
precision_score(y_train_5, y_train_pred_forest)
Out[53]:
0.9852973447443494
In [54]:
recall_score(y_train_5, y_train_pred_forest)
Out[54]:
0.8282604685482383
Multiclass classification¶
In [55]:
sgd_clf.fit(X_train, y_train)
sgd_clf.predict([some_digit])
Out[55]:
array([5], dtype=int8)
In [56]:
some_digit_scores = sgd_clf.decision_function([some_digit])
some_digit_scores
Out[56]:
array([[-311402.62954431, -363517.28355739, -446449.5306454 ,
-183226.61023518, -414337.15339485, 161855.74572176,
-452576.39616343, -471957.14962573, -518542.33997148,
-536774.63961222]])
In [57]:
np.argmax(some_digit_scores)
Out[57]:
5
In [58]:
sgd_clf.classes_
Out[58]:
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int8)
In [59]:
sgd_clf.classes_[5]
Out[59]:
5
In [60]:
from sklearn.multiclass import OneVsOneClassifier
ovo_clf = OneVsOneClassifier(SGDClassifier(max_iter=5, tol=-np.infty, random_state=42))
ovo_clf.fit(X_train, y_train)
ovo_clf.predict([some_digit])
Out[60]:
array([5], dtype=int8)
In [61]:
len(ovo_clf.estimators_)
Out[61]:
45
In [62]:
forest_clf.fit(X_train, y_train)
forest_clf.predict([some_digit])
Out[62]:
array([5], dtype=int8)
In [63]:
forest_clf.predict_proba([some_digit])
Out[63]:
array([[0.1, 0. , 0. , 0.1, 0. , 0.8, 0. , 0. , 0. , 0. ]])
In [64]:
cross_val_score(sgd_clf, X_train, y_train, cv=3, scoring="accuracy")
Out[64]:
array([0.84063187, 0.84899245, 0.86652998])
In [65]:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train.astype(np.float64))
cross_val_score(sgd_clf, X_train_scaled, y_train, cv=3, scoring="accuracy")
Out[65]:
array([0.91011798, 0.90874544, 0.906636 ])
In [66]:
y_train_pred = cross_val_predict(sgd_clf, X_train_scaled, y_train, cv=3)
conf_mx = confusion_matrix(y_train, y_train_pred)
conf_mx
Out[66]:
array([[5725, 3, 24, 9, 10, 49, 50, 10, 39, 4],
[ 2, 6493, 43, 25, 7, 40, 5, 10, 109, 8],
[ 51, 41, 5321, 104, 89, 26, 87, 60, 166, 13],
[ 47, 46, 141, 5342, 1, 231, 40, 50, 141, 92],
[ 19, 29, 41, 10, 5366, 9, 56, 37, 86, 189],
[ 73, 45, 36, 193, 64, 4582, 111, 30, 193, 94],
[ 29, 34, 44, 2, 42, 85, 5627, 10, 45, 0],
[ 25, 24, 74, 32, 54, 12, 6, 5787, 15, 236],
[ 52, 161, 73, 156, 10, 163, 61, 25, 5027, 123],
[ 43, 35, 26, 92, 178, 28, 2, 223, 82, 5240]])
In [67]:
def plot_confusion_matrix(matrix):
"""If you prefer color and a colorbar"""
fig = plt.figure(figsize=(8,8))
ax = fig.add_subplot(111)
cax = ax.matshow(matrix)
fig.colorbar(cax)
In [68]:
plt.matshow(conf_mx, cmap=plt.cm.gray)
save_fig("confusion_matrix_plot", tight_layout=False)
plt.show()
Saving figure confusion_matrix_plot
In [69]:
row_sums = conf_mx.sum(axis=1, keepdims=True)
norm_conf_mx = conf_mx / row_sums
In [70]:
np.fill_diagonal(norm_conf_mx, 0)
plt.matshow(norm_conf_mx, cmap=plt.cm.gray)
save_fig("confusion_matrix_errors_plot", tight_layout=False)
plt.show()
Saving figure confusion_matrix_errors_plot
In [71]:
cl_a, cl_b = 3, 5
X_aa = X_train[(y_train == cl_a) & (y_train_pred == cl_a)]
X_ab = X_train[(y_train == cl_a) & (y_train_pred == cl_b)]
X_ba = X_train[(y_train == cl_b) & (y_train_pred == cl_a)]
X_bb = X_train[(y_train == cl_b) & (y_train_pred == cl_b)]
plt.figure(figsize=(8,8))
plt.subplot(221); plot_digits(X_aa[:25], images_per_row=5)
plt.subplot(222); plot_digits(X_ab[:25], images_per_row=5)
plt.subplot(223); plot_digits(X_ba[:25], images_per_row=5)
plt.subplot(224); plot_digits(X_bb[:25], images_per_row=5)
save_fig("error_analysis_digits_plot")
plt.show()
Saving figure error_analysis_digits_plot
Multilabel classification¶
In [72]:
from sklearn.neighbors import KNeighborsClassifier
y_train_large = (y_train >= 7)
y_train_odd = (y_train % 2 == 1)
y_multilabel = np.c_[y_train_large, y_train_odd]
knn_clf = KNeighborsClassifier()
knn_clf.fit(X_train, y_multilabel)
Out[72]:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=5, p=2,
weights='uniform')
In [73]:
knn_clf.predict([some_digit])
Out[73]:
array([[False, True]])
Warning: the following cell may take a very long time (possibly hours depending on your hardware).
In [74]:
y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3, n_jobs=-1)
f1_score(y_multilabel, y_train_knn_pred, average="macro")
Out[74]:
0.97709078477525
Multioutput classification¶
In [75]:
noise = np.random.randint(0, 100, (len(X_train), 784))
X_train_mod = X_train + noise
noise = np.random.randint(0, 100, (len(X_test), 784))
X_test_mod = X_test + noise
y_train_mod = X_train
y_test_mod = X_test
In [76]:
some_index = 5500
plt.subplot(121); plot_digit(X_test_mod[some_index])
plt.subplot(122); plot_digit(y_test_mod[some_index])
save_fig("noisy_digit_example_plot")
plt.show()
Saving figure noisy_digit_example_plot
In [77]:
knn_clf.fit(X_train_mod, y_train_mod)
clean_digit = knn_clf.predict([X_test_mod[some_index]])
plot_digit(clean_digit)
save_fig("cleaned_digit_example_plot")
Saving figure cleaned_digit_example_plot
Extra material¶
Dummy (ie. random) classifier¶
In [78]:
from sklearn.dummy import DummyClassifier
dmy_clf = DummyClassifier()
y_probas_dmy = cross_val_predict(dmy_clf, X_train, y_train_5, cv=3, method="predict_proba")
y_scores_dmy = y_probas_dmy[:, 1]
In [79]:
fprr, tprr, thresholdsr = roc_curve(y_train_5, y_scores_dmy)
plot_roc_curve(fprr, tprr)
KNN classifier¶
In [80]:
from sklearn.neighbors import KNeighborsClassifier
knn_clf = KNeighborsClassifier(n_jobs=-1, weights='distance', n_neighbors=4)
knn_clf.fit(X_train, y_train)
Out[80]:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=4, p=2,
weights='distance')
In [81]:
y_knn_pred = knn_clf.predict(X_test)
In [82]:
from sklearn.metrics import accuracy_score
accuracy_score(y_test, y_knn_pred)
Out[82]:
0.9714
In [83]:
from scipy.ndimage.interpolation import shift
def shift_digit(digit_array, dx, dy, new=0):
return shift(digit_array.reshape(28, 28), [dy, dx], cval=new).reshape(784)
plot_digit(shift_digit(some_digit, 5, 1, new=100))
In [84]:
X_train_expanded = [X_train]
y_train_expanded = [y_train]
for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):
shifted_images = np.apply_along_axis(shift_digit, axis=1, arr=X_train, dx=dx, dy=dy)
X_train_expanded.append(shifted_images)
y_train_expanded.append(y_train)
X_train_expanded = np.concatenate(X_train_expanded)
y_train_expanded = np.concatenate(y_train_expanded)
X_train_expanded.shape, y_train_expanded.shape
Out[84]:
((300000, 784), (300000,))
In [85]:
knn_clf.fit(X_train_expanded, y_train_expanded)
Out[85]:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=-1, n_neighbors=4, p=2,
weights='distance')
In [86]:
y_knn_expanded_pred = knn_clf.predict(X_test)
In [87]:
accuracy_score(y_test, y_knn_expanded_pred)
Out[87]:
0.9763
In [88]:
ambiguous_digit = X_test[2589]
knn_clf.predict_proba([ambiguous_digit])
Out[88]:
array([[0. , 0. , 0.5053645, 0. , 0. , 0. ,
0. , 0.4946355, 0. , 0. ]])
In [89]:
plot_digit(ambiguous_digit)
Exercise solutions¶
1. An MNIST Classifier With Over 97% Accuracy¶
Warning: the next cell may take hours to run, depending on your hardware.
In [90]:
from sklearn.model_selection import GridSearchCV
param_grid = [{'weights': ["uniform", "distance"], 'n_neighbors': [3, 4, 5]}]
knn_clf = KNeighborsClassifier()
grid_search = GridSearchCV(knn_clf, param_grid, cv=5, verbose=3, n_jobs=-1)
grid_search.fit(X_train, y_train)
Fitting 5 folds for each of 6 candidates, totalling 30 fits
[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers. [Parallel(n_jobs=-1)]: Done 26 out of 30 | elapsed: 640.0min remaining: 98.5min [Parallel(n_jobs=-1)]: Done 30 out of 30 | elapsed: 640.1min finished
Out[90]:
GridSearchCV(cv=5, error_score='raise-deprecating',
estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=5, p=2,
weights='uniform'),
fit_params=None, iid='warn', n_jobs=-1,
param_grid=[{'weights': ['uniform', 'distance'], 'n_neighbors': [3, 4, 5]}],
pre_dispatch='2*n_jobs', refit=True, return_train_score='warn',
scoring=None, verbose=3)
In [91]:
grid_search.best_params_
Out[91]:
{'n_neighbors': 4, 'weights': 'distance'}
In [92]:
grid_search.best_score_
Out[92]:
0.97325
In [93]:
from sklearn.metrics import accuracy_score
y_pred = grid_search.predict(X_test)
accuracy_score(y_test, y_pred)
Out[93]:
0.9714
2. Data Augmentation¶
In [94]:
from scipy.ndimage.interpolation import shift
In [95]:
def shift_image(image, dx, dy):
image = image.reshape((28, 28))
shifted_image = shift(image, [dy, dx], cval=0, mode="constant")
return shifted_image.reshape([-1])
In [96]:
image = X_train[1000]
shifted_image_down = shift_image(image, 0, 5)
shifted_image_left = shift_image(image, -5, 0)
plt.figure(figsize=(12,3))
plt.subplot(131)
plt.title("Original", fontsize=14)
plt.imshow(image.reshape(28, 28), interpolation="nearest", cmap="Greys")
plt.subplot(132)
plt.title("Shifted down", fontsize=14)
plt.imshow(shifted_image_down.reshape(28, 28), interpolation="nearest", cmap="Greys")
plt.subplot(133)
plt.title("Shifted left", fontsize=14)
plt.imshow(shifted_image_left.reshape(28, 28), interpolation="nearest", cmap="Greys")
plt.show()
In [97]:
X_train_augmented = [image for image in X_train]
y_train_augmented = [label for label in y_train]
for dx, dy in ((1, 0), (-1, 0), (0, 1), (0, -1)):
for image, label in zip(X_train, y_train):
X_train_augmented.append(shift_image(image, dx, dy))
y_train_augmented.append(label)
X_train_augmented = np.array(X_train_augmented)
y_train_augmented = np.array(y_train_augmented)
In [98]:
shuffle_idx = np.random.permutation(len(X_train_augmented))
X_train_augmented = X_train_augmented[shuffle_idx]
y_train_augmented = y_train_augmented[shuffle_idx]
In [99]:
knn_clf = KNeighborsClassifier(**grid_search.best_params_)
In [100]:
knn_clf.fit(X_train_augmented, y_train_augmented)
Out[100]:
KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
metric_params=None, n_jobs=None, n_neighbors=4, p=2,
weights='distance')
In [101]:
y_pred = knn_clf.predict(X_test)
accuracy_score(y_test, y_pred)
Out[101]:
0.9763
By simply augmenting the data, we got a 0.5% accuracy boost. :)
3. Tackle the Titanic dataset¶
The goal is to predict whether or not a passenger survived based on attributes such as their age, sex, passenger class, where they embarked and so on.
First, login to Kaggle and go to the Titanic challenge to download train.csv and test.csv. Save them to the datasets/titanic directory.
Next, let's load the data:
In [102]:
import os
TITANIC_PATH = os.path.join("datasets", "titanic")
In [103]:
import pandas as pd
def load_titanic_data(filename, titanic_path=TITANIC_PATH):
csv_path = os.path.join(titanic_path, filename)
return pd.read_csv(csv_path)
In [104]:
train_data = load_titanic_data("train.csv")
test_data = load_titanic_data("test.csv")
The data is already split into a training set and a test set. However, the test data does not contain the labels: your goal is to train the best model you can using the training data, then make your predictions on the test data and upload them to Kaggle to see your final score.
Let's take a peek at the top few rows of the training set:
In [105]:
train_data.head()
Out[105]:
| PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1 | 0 | 3 | Braund, Mr. Owen Harris | male | 22.0 | 1 | 0 | A/5 21171 | 7.2500 | NaN | S |
| 1 | 2 | 1 | 1 | Cumings, Mrs. John Bradley (Florence Briggs Th... | female | 38.0 | 1 | 0 | PC 17599 | 71.2833 | C85 | C |
| 2 | 3 | 1 | 3 | Heikkinen, Miss. Laina | female | 26.0 | 0 | 0 | STON/O2. 3101282 | 7.9250 | NaN | S |
| 3 | 4 | 1 | 1 | Futrelle, Mrs. Jacques Heath (Lily May Peel) | female | 35.0 | 1 | 0 | 113803 | 53.1000 | C123 | S |
| 4 | 5 | 0 | 3 | Allen, Mr. William Henry | male | 35.0 | 0 | 0 | 373450 | 8.0500 | NaN | S |
The attributes have the following meaning:
- Survived: that's the target, 0 means the passenger did not survive, while 1 means he/she survived.
- Pclass: passenger class.
- Name, Sex, Age: self-explanatory
- SibSp: how many siblings & spouses of the passenger aboard the Titanic.
- Parch: how many children & parents of the passenger aboard the Titanic.
- Ticket: ticket id
- Fare: price paid (in pounds)
- Cabin: passenger's cabin number
- Embarked: where the passenger embarked the Titanic
Let's get more info to see how much data is missing:
In [106]:
train_data.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 891 entries, 0 to 890 Data columns (total 12 columns): PassengerId 891 non-null int64 Survived 891 non-null int64 Pclass 891 non-null int64 Name 891 non-null object Sex 891 non-null object Age 714 non-null float64 SibSp 891 non-null int64 Parch 891 non-null int64 Ticket 891 non-null object Fare 891 non-null float64 Cabin 204 non-null object Embarked 889 non-null object dtypes: float64(2), int64(5), object(5) memory usage: 83.6+ KB
Okay, the Age, Cabin and Embarked attributes are sometimes null (less than 891 non-null), especially the Cabin (77% are null). We will ignore the Cabin for now and focus on the rest. The Age attribute has about 19% null values, so we will need to decide what to do with them. Replacing null values with the median age seems reasonable.
The Name and Ticket attributes may have some value, but they will be a bit tricky to convert into useful numbers that a model can consume. So for now, we will ignore them.
Let's take a look at the numerical attributes:
In [107]:
train_data.describe()
Out[107]:
| PassengerId | Survived | Pclass | Age | SibSp | Parch | Fare | |
|---|---|---|---|---|---|---|---|
| count | 891.000000 | 891.000000 | 891.000000 | 714.000000 | 891.000000 | 891.000000 | 891.000000 |
| mean | 446.000000 | 0.383838 | 2.308642 | 29.699118 | 0.523008 | 0.381594 | 32.204208 |
| std | 257.353842 | 0.486592 | 0.836071 | 14.526497 | 1.102743 | 0.806057 | 49.693429 |
| min | 1.000000 | 0.000000 | 1.000000 | 0.420000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 223.500000 | 0.000000 | 2.000000 | 20.125000 | 0.000000 | 0.000000 | 7.910400 |
| 50% | 446.000000 | 0.000000 | 3.000000 | 28.000000 | 0.000000 | 0.000000 | 14.454200 |
| 75% | 668.500000 | 1.000000 | 3.000000 | 38.000000 | 1.000000 | 0.000000 | 31.000000 |
| max | 891.000000 | 1.000000 | 3.000000 | 80.000000 | 8.000000 | 6.000000 | 512.329200 |
- Yikes, only 38% Survived. :( That's close enough to 40%, so accuracy will be a reasonable metric to evaluate our model.
- The mean Fare was £32.20, which does not seem so expensive (but it was probably a lot of money back then).
- The mean Age was less than 30 years old.
Let's check that the target is indeed 0 or 1:
In [108]:
train_data["Survived"].value_counts()
Out[108]:
0 549 1 342 Name: Survived, dtype: int64
Now let's take a quick look at all the categorical attributes:
In [109]:
train_data["Pclass"].value_counts()
Out[109]:
3 491 1 216 2 184 Name: Pclass, dtype: int64
In [110]:
train_data["Sex"].value_counts()
Out[110]:
male 577 female 314 Name: Sex, dtype: int64
In [111]:
train_data["Embarked"].value_counts()
Out[111]:
S 644 C 168 Q 77 Name: Embarked, dtype: int64
The Embarked attribute tells us where the passenger embarked: C=Cherbourg, Q=Queenstown, S=Southampton.
Now let's build our preprocessing pipelines. We will reuse the DataframeSelector we built in the previous chapter to select specific attributes from the DataFrame:
In [112]:
from sklearn.base import BaseEstimator, TransformerMixin
# A class to select numerical or categorical columns
# since Scikit-Learn doesn't handle DataFrames yet
class DataFrameSelector(BaseEstimator, TransformerMixin):
def __init__(self, attribute_names):
self.attribute_names = attribute_names
def fit(self, X, y=None):
return self
def transform(self, X):
return X[self.attribute_names]
Let's build the pipeline for the numerical attributes:
Warning: Since Scikit-Learn 0.20, the sklearn.preprocessing.Imputer class was replaced by the sklearn.impute.SimpleImputer class.
In [113]:
from sklearn.pipeline import Pipeline
try:
from sklearn.impute import SimpleImputer # Scikit-Learn 0.20+
except ImportError:
from sklearn.preprocessing import Imputer as SimpleImputer
num_pipeline = Pipeline([
("select_numeric", DataFrameSelector(["Age", "SibSp", "Parch", "Fare"])),
("imputer", SimpleImputer(strategy="median")),
])
In [114]:
num_pipeline.fit_transform(train_data)
Out[114]:
array([[22. , 1. , 0. , 7.25 ],
[38. , 1. , 0. , 71.2833],
[26. , 0. , 0. , 7.925 ],
...,
[28. , 1. , 2. , 23.45 ],
[26. , 0. , 0. , 30. ],
[32. , 0. , 0. , 7.75 ]])
We will also need an imputer for the string categorical columns (the regular SimpleImputer does not work on those):
In [115]:
# Inspired from stackoverflow.com/questions/25239958
class MostFrequentImputer(BaseEstimator, TransformerMixin):
def fit(self, X, y=None):
self.most_frequent_ = pd.Series([X[c].value_counts().index[0] for c in X],
index=X.columns)
return self
def transform(self, X, y=None):
return X.fillna(self.most_frequent_)
Warning: earlier versions of the book used the LabelBinarizer or CategoricalEncoder classes to convert each categorical value to a one-hot vector. It is now preferable to use the OneHotEncoder class. Since Scikit-Learn 0.20 it can handle string categorical inputs (see PR #10521), not just integer categorical inputs. If you are using an older version of Scikit-Learn, you can import the new version from future_encoders.py:
In [116]:
try:
from sklearn.preprocessing import OrdinalEncoder # just to raise an ImportError if Scikit-Learn < 0.20
from sklearn.preprocessing import OneHotEncoder
except ImportError:
from future_encoders import OneHotEncoder # Scikit-Learn < 0.20
Now we can build the pipeline for the categorical attributes:
In [117]:
cat_pipeline = Pipeline([
("select_cat", DataFrameSelector(["Pclass", "Sex", "Embarked"])),
("imputer", MostFrequentImputer()),
("cat_encoder", OneHotEncoder(sparse=False)),
])
In [118]:
cat_pipeline.fit_transform(train_data)
Out[118]:
array([[0., 0., 1., ..., 0., 0., 1.],
[1., 0., 0., ..., 1., 0., 0.],
[0., 0., 1., ..., 0., 0., 1.],
...,
[0., 0., 1., ..., 0., 0., 1.],
[1., 0., 0., ..., 1., 0., 0.],
[0., 0., 1., ..., 0., 1., 0.]])
Finally, let's join the numerical and categorical pipelines:
In [119]:
from sklearn.pipeline import FeatureUnion
preprocess_pipeline = FeatureUnion(transformer_list=[
("num_pipeline", num_pipeline),
("cat_pipeline", cat_pipeline),
])
Cool! Now we have a nice preprocessing pipeline that takes the raw data and outputs numerical input features that we can feed to any Machine Learning model we want.
In [120]:
X_train = preprocess_pipeline.fit_transform(train_data)
X_train
Out[120]:
array([[22., 1., 0., ..., 0., 0., 1.],
[38., 1., 0., ..., 1., 0., 0.],
[26., 0., 0., ..., 0., 0., 1.],
...,
[28., 1., 2., ..., 0., 0., 1.],
[26., 0., 0., ..., 1., 0., 0.],
[32., 0., 0., ..., 0., 1., 0.]])
Let's not forget to get the labels:
In [121]:
y_train = train_data["Survived"]
We are now ready to train a classifier. Let's start with an SVC:
In [122]:
from sklearn.svm import SVC
svm_clf = SVC(gamma="auto")
svm_clf.fit(X_train, y_train)
Out[122]:
SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False)
Great, our model is trained, let's use it to make predictions on the test set:
In [123]:
X_test = preprocess_pipeline.transform(test_data)
y_pred = svm_clf.predict(X_test)
And now we could just build a CSV file with these predictions (respecting the format excepted by Kaggle), then upload it and hope for the best. But wait! We can do better than hope. Why don't we use cross-validation to have an idea of how good our model is?
In [124]:
from sklearn.model_selection import cross_val_score
svm_scores = cross_val_score(svm_clf, X_train, y_train, cv=10)
svm_scores.mean()
Out[124]:
0.7365250822835092
Okay, over 73% accuracy, clearly better than random chance, but it's not a great score. Looking at the leaderboard for the Titanic competition on Kaggle, you can see that you need to reach above 80% accuracy to be within the top 10% Kagglers. Some reached 100%, but since you can easily find the list of victims of the Titanic, it seems likely that there was little Machine Learning involved in their performance! ;-) So let's try to build a model that reaches 80% accuracy.
Let's try a RandomForestClassifier:
In [125]:
from sklearn.ensemble import RandomForestClassifier
forest_clf = RandomForestClassifier(n_estimators=100, random_state=42)
forest_scores = cross_val_score(forest_clf, X_train, y_train, cv=10)
forest_scores.mean()
Out[125]:
0.8149526160481217
That's much better!
Instead of just looking at the mean accuracy across the 10 cross-validation folds, let's plot all 10 scores for each model, along with a box plot highlighting the lower and upper quartiles, and "whiskers" showing the extent of the scores (thanks to Nevin Yilmaz for suggesting this visualization). Note that the boxplot() function detects outliers (called "fliers") and does not include them within the whiskers. Specifically, if the lower quartile is $Q_1$ and the upper quartile is $Q_3$, then the interquartile range $IQR = Q_3 - Q_1$ (this is the box's height), and any score lower than $Q_1 - 1.5 \times IQR$ is a flier, and so is any score greater than $Q3 + 1.5 \times IQR$.
In [126]:
plt.figure(figsize=(8, 4))
plt.plot([1]*10, svm_scores, ".")
plt.plot([2]*10, forest_scores, ".")
plt.boxplot([svm_scores, forest_scores], labels=("SVM","Random Forest"))
plt.ylabel("Accuracy", fontsize=14)
plt.show()
To improve this result further, you could:
- Compare many more models and tune hyperparameters using cross validation and grid search,
- Do more feature engineering, for example:
- replace SibSp and Parch with their sum,
- try to identify parts of names that correlate well with the Survived attribute (e.g. if the name contains "Countess", then survival seems more likely),
- try to convert numerical attributes to categorical attributes: for example, different age groups had very different survival rates (see below), so it may help to create an age bucket category and use it instead of the age. Similarly, it may be useful to have a special category for people traveling alone since only 30% of them survived (see below).
In [127]:
train_data["AgeBucket"] = train_data["Age"] // 15 * 15
train_data[["AgeBucket", "Survived"]].groupby(['AgeBucket']).mean()
Out[127]:
| Survived | |
|---|---|
| AgeBucket | |
| 0.0 | 0.576923 |
| 15.0 | 0.362745 |
| 30.0 | 0.423256 |
| 45.0 | 0.404494 |
| 60.0 | 0.240000 |
| 75.0 | 1.000000 |
In [128]:
train_data["RelativesOnboard"] = train_data["SibSp"] + train_data["Parch"]
train_data[["RelativesOnboard", "Survived"]].groupby(['RelativesOnboard']).mean()
Out[128]:
| Survived | |
|---|---|
| RelativesOnboard | |
| 0 | 0.303538 |
| 1 | 0.552795 |
| 2 | 0.578431 |
| 3 | 0.724138 |
| 4 | 0.200000 |
| 5 | 0.136364 |
| 6 | 0.333333 |
| 7 | 0.000000 |
| 10 | 0.000000 |
4. Spam classifier¶
First, let's fetch the data:
In [129]:
import os
import tarfile
import urllib.request
DOWNLOAD_ROOT = "http://spamassassin.apache.org/old/publiccorpus/"
HAM_URL = DOWNLOAD_ROOT + "20030228_easy_ham.tar.bz2"
SPAM_URL = DOWNLOAD_ROOT + "20030228_spam.tar.bz2"
SPAM_PATH = os.path.join("datasets", "spam")
def fetch_spam_data(spam_url=SPAM_URL, spam_path=SPAM_PATH):
if not os.path.isdir(spam_path):
os.makedirs(spam_path)
for filename, url in (("ham.tar.bz2", HAM_URL), ("spam.tar.bz2", SPAM_URL)):
path = os.path.join(spam_path, filename)
if not os.path.isfile(path):
urllib.request.urlretrieve(url, path)
tar_bz2_file = tarfile.open(path)
tar_bz2_file.extractall(path=SPAM_PATH)
tar_bz2_file.close()
In [130]:
fetch_spam_data()
Next, let's load all the emails:
In [131]:
HAM_DIR = os.path.join(SPAM_PATH, "easy_ham")
SPAM_DIR = os.path.join(SPAM_PATH, "spam")
ham_filenames = [name for name in sorted(os.listdir(HAM_DIR)) if len(name) > 20]
spam_filenames = [name for name in sorted(os.listdir(SPAM_DIR)) if len(name) > 20]
In [132]:
len(ham_filenames)
Out[132]:
2500
In [133]:
len(spam_filenames)
Out[133]:
500
We can use Python's email module to parse these emails (this handles headers, encoding, and so on):
In [134]:
import email
import email.policy
def load_email(is_spam, filename, spam_path=SPAM_PATH):
directory = "spam" if is_spam else "easy_ham"
with open(os.path.join(spam_path, directory, filename), "rb") as f:
return email.parser.BytesParser(policy=email.policy.default).parse(f)
In [135]:
ham_emails = [load_email(is_spam=False, filename=name) for name in ham_filenames]
spam_emails = [load_email(is_spam=True, filename=name) for name in spam_filenames]
Let's look at one example of ham and one example of spam, to get a feel of what the data looks like:
In [136]:
print(ham_emails[1].get_content().strip())
Martin A posted: Tassos Papadopoulos, the Greek sculptor behind the plan, judged that the limestone of Mount Kerdylio, 70 miles east of Salonika and not far from the Mount Athos monastic community, was ideal for the patriotic sculpture. As well as Alexander's granite features, 240 ft high and 170 ft wide, a museum, a restored amphitheatre and car park for admiring crowds are planned --------------------- So is this mountain limestone or granite? If it's limestone, it'll weather pretty fast. ------------------------ Yahoo! Groups Sponsor ---------------------~--> 4 DVDs Free +s&p Join Now http://us.click.yahoo.com/pt6YBB/NXiEAA/mG3HAA/7gSolB/TM ---------------------------------------------------------------------~-> To unsubscribe from this group, send an email to: forteana-unsubscribe@egroups.com Your use of Yahoo! Groups is subject to http://docs.yahoo.com/info/terms/
In [137]:
print(spam_emails[6].get_content().strip())
Help wanted. We are a 14 year old fortune 500 company, that is growing at a tremendous rate. We are looking for individuals who want to work from home. This is an opportunity to make an excellent income. No experience is required. We will train you. So if you are looking to be employed from home with a career that has vast opportunities, then go: http://www.basetel.com/wealthnow We are looking for energetic and self motivated people. If that is you than click on the link and fill out the form, and one of our employement specialist will contact you. To be removed from our link simple go to: http://www.basetel.com/remove.html 4139vOLW7-758DoDY1425FRhM1-764SMFc8513fCsLl40
Some emails are actually multipart, with images and attachments (which can have their own attachments). Let's look at the various types of structures we have:
In [138]:
def get_email_structure(email):
if isinstance(email, str):
return email
payload = email.get_payload()
if isinstance(payload, list):
return "multipart({})".format(", ".join([
get_email_structure(sub_email)
for sub_email in payload
]))
else:
return email.get_content_type()
In [139]:
from collections import Counter
def structures_counter(emails):
structures = Counter()
for email in emails:
structure = get_email_structure(email)
structures[structure] += 1
return structures
In [140]:
structures_counter(ham_emails).most_common()
Out[140]:
[('text/plain', 2408),
('multipart(text/plain, application/pgp-signature)', 66),
('multipart(text/plain, text/html)', 8),
('multipart(text/plain, text/plain)', 4),
('multipart(text/plain)', 3),
('multipart(text/plain, application/octet-stream)', 2),
('multipart(text/plain, text/enriched)', 1),
('multipart(text/plain, application/ms-tnef, text/plain)', 1),
('multipart(multipart(text/plain, text/plain, text/plain), application/pgp-signature)',
1),
('multipart(text/plain, video/mng)', 1),
('multipart(text/plain, multipart(text/plain))', 1),
('multipart(text/plain, application/x-pkcs7-signature)', 1),
('multipart(text/plain, multipart(text/plain, text/plain), text/rfc822-headers)',
1),
('multipart(text/plain, multipart(text/plain, text/plain), multipart(multipart(text/plain, application/x-pkcs7-signature)))',
1),
('multipart(text/plain, application/x-java-applet)', 1)]
In [141]:
structures_counter(spam_emails).most_common()
Out[141]:
[('text/plain', 218),
('text/html', 183),
('multipart(text/plain, text/html)', 45),
('multipart(text/html)', 20),
('multipart(text/plain)', 19),
('multipart(multipart(text/html))', 5),
('multipart(text/plain, image/jpeg)', 3),
('multipart(text/html, application/octet-stream)', 2),
('multipart(text/plain, application/octet-stream)', 1),
('multipart(text/html, text/plain)', 1),
('multipart(multipart(text/html), application/octet-stream, image/jpeg)', 1),
('multipart(multipart(text/plain, text/html), image/gif)', 1),
('multipart/alternative', 1)]
It seems that the ham emails are more often plain text, while spam has quite a lot of HTML. Moreover, quite a few ham emails are signed using PGP, while no spam is. In short, it seems that the email structure is useful information to have.
Now let's take a look at the email headers:
In [142]:
for header, value in spam_emails[0].items():
print(header,":",value)
Return-Path : <12a1mailbot1@web.de> Delivered-To : zzzz@localhost.spamassassin.taint.org Received : from localhost (localhost [127.0.0.1]) by phobos.labs.spamassassin.taint.org (Postfix) with ESMTP id 136B943C32 for <zzzz@localhost>; Thu, 22 Aug 2002 08:17:21 -0400 (EDT) Received : from mail.webnote.net [193.120.211.219] by localhost with POP3 (fetchmail-5.9.0) for zzzz@localhost (single-drop); Thu, 22 Aug 2002 13:17:21 +0100 (IST) Received : from dd_it7 ([210.97.77.167]) by webnote.net (8.9.3/8.9.3) with ESMTP id NAA04623 for <zzzz@spamassassin.taint.org>; Thu, 22 Aug 2002 13:09:41 +0100 From : 12a1mailbot1@web.de Received : from r-smtp.korea.com - 203.122.2.197 by dd_it7 with Microsoft SMTPSVC(5.5.1775.675.6); Sat, 24 Aug 2002 09:42:10 +0900 To : dcek1a1@netsgo.com Subject : Life Insurance - Why Pay More? Date : Wed, 21 Aug 2002 20:31:57 -1600 MIME-Version : 1.0 Message-ID : <0103c1042001882DD_IT7@dd_it7> Content-Type : text/html; charset="iso-8859-1" Content-Transfer-Encoding : quoted-printable
There's probably a lot of useful information in there, such as the sender's email address (12a1mailbot1@web.de looks fishy), but we will just focus on the Subject header:
In [143]:
spam_emails[0]["Subject"]
Out[143]:
'Life Insurance - Why Pay More?'
Okay, before we learn too much about the data, let's not forget to split it into a training set and a test set:
In [144]:
import numpy as np
from sklearn.model_selection import train_test_split
X = np.array(ham_emails + spam_emails)
y = np.array([0] * len(ham_emails) + [1] * len(spam_emails))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Okay, let's start writing the preprocessing functions. First, we will need a function to convert HTML to plain text. Arguably the best way to do this would be to use the great BeautifulSoup library, but I would like to avoid adding another dependency to this project, so let's hack a quick & dirty solution using regular expressions (at the risk of un̨ho͞ly radiańcé destro҉ying all enli̍̈́̂̈́ghtenment). The following function first drops the <head> section, then converts all <a> tags to the word HYPERLINK, then it gets rid of all HTML tags, leaving only the plain text. For readability, it also replaces multiple newlines with single newlines, and finally it unescapes html entities (such as > or ):
In [145]:
import re
from html import unescape
def html_to_plain_text(html):
text = re.sub('<head.*?>.*?</head>', '', html, flags=re.M | re.S | re.I)
text = re.sub('<a\s.*?>', ' HYPERLINK ', text, flags=re.M | re.S | re.I)
text = re.sub('<.*?>', '', text, flags=re.M | re.S)
text = re.sub(r'(\s*\n)+', '\n', text, flags=re.M | re.S)
return unescape(text)
Let's see if it works. This is HTML spam:
In [146]:
html_spam_emails = [email for email in X_train[y_train==1]
if get_email_structure(email) == "text/html"]
sample_html_spam = html_spam_emails[7]
print(sample_html_spam.get_content().strip()[:1000], "...")
<HTML><HEAD><TITLE></TITLE><META http-equiv="Content-Type" content="text/html; charset=windows-1252"><STYLE>A:link {TEX-DECORATION: none}A:active {TEXT-DECORATION: none}A:visited {TEXT-DECORATION: none}A:hover {COLOR: #0033ff; TEXT-DECORATION: underline}</STYLE><META content="MSHTML 6.00.2713.1100" name="GENERATOR"></HEAD>
<BODY text="#000000" vLink="#0033ff" link="#0033ff" bgColor="#CCCC99"><TABLE borderColor="#660000" cellSpacing="0" cellPadding="0" border="0" width="100%"><TR><TD bgColor="#CCCC99" valign="top" colspan="2" height="27">
<font size="6" face="Arial, Helvetica, sans-serif" color="#660000">
<b>OTC</b></font></TD></TR><TR><TD height="2" bgcolor="#6a694f">
<font size="5" face="Times New Roman, Times, serif" color="#FFFFFF">
<b> Newsletter</b></font></TD><TD height="2" bgcolor="#6a694f"><div align="right"><font color="#FFFFFF">
<b>Discover Tomorrow's Winners </b></font></div></TD></TR><TR><TD height="25" colspan="2" bgcolor="#CCCC99"><table width="100%" border="0" ...
And this is the resulting plain text:
In [147]:
print(html_to_plain_text(sample_html_spam.get_content())[:1000], "...")
OTC Newsletter Discover Tomorrow's Winners For Immediate Release Cal-Bay (Stock Symbol: CBYI) Watch for analyst "Strong Buy Recommendations" and several advisory newsletters picking CBYI. CBYI has filed to be traded on the OTCBB, share prices historically INCREASE when companies get listed on this larger trading exchange. CBYI is trading around 25 cents and should skyrocket to $2.66 - $3.25 a share in the near future. Put CBYI on your watch list, acquire a position TODAY. REASONS TO INVEST IN CBYI A profitable company and is on track to beat ALL earnings estimates! One of the FASTEST growing distributors in environmental & safety equipment instruments. Excellent management team, several EXCLUSIVE contracts. IMPRESSIVE client list including the U.S. Air Force, Anheuser-Busch, Chevron Refining and Mitsubishi Heavy Industries, GE-Energy & Environmental Research. RAPIDLY GROWING INDUSTRY Industry revenues exceed $900 million, estimates indicate that there could be as much as $25 billi ...
Great! Now let's write a function that takes an email as input and returns its content as plain text, whatever its format is:
In [148]:
def email_to_text(email):
html = None
for part in email.walk():
ctype = part.get_content_type()
if not ctype in ("text/plain", "text/html"):
continue
try:
content = part.get_content()
except: # in case of encoding issues
content = str(part.get_payload())
if ctype == "text/plain":
return content
else:
html = content
if html:
return html_to_plain_text(html)
In [149]:
print(email_to_text(sample_html_spam)[:100], "...")
OTC Newsletter Discover Tomorrow's Winners For Immediate Release Cal-Bay (Stock Symbol: CBYI) Wat ...
Let's throw in some stemming! For this to work, you need to install the Natural Language Toolkit (NLTK). It's as simple as running the following command (don't forget to activate your virtualenv first; if you don't have one, you will likely need administrator rights, or use the --user option):
$ pip3 install nltk
In [150]:
try:
import nltk
stemmer = nltk.PorterStemmer()
for word in ("Computations", "Computation", "Computing", "Computed", "Compute", "Compulsive"):
print(word, "=>", stemmer.stem(word))
except ImportError:
print("Error: stemming requires the NLTK module.")
stemmer = None
Computations => comput Computation => comput Computing => comput Computed => comput Compute => comput Compulsive => compuls
We will also need a way to replace URLs with the word "URL". For this, we could use hard core regular expressions but we will just use the urlextract library. You can install it with the following command (don't forget to activate your virtualenv first; if you don't have one, you will likely need administrator rights, or use the --user option):
$ pip3 install urlextract
In [151]:
try:
import urlextract # may require an Internet connection to download root domain names
url_extractor = urlextract.URLExtract()
print(url_extractor.find_urls("Will it detect github.com and https://youtu.be/7Pq-S557XQU?t=3m32s"))
except ImportError:
print("Error: replacing URLs requires the urlextract module.")
url_extractor = None
['github.com', 'https://youtu.be/7Pq-S557XQU?t=3m32s']
We are ready to put all this together into a transformer that we will use to convert emails to word counters. Note that we split sentences into words using Python's split() method, which uses whitespaces for word boundaries. This works for many written languages, but not all. For example, Chinese and Japanese scripts generally don't use spaces between words, and Vietnamese often uses spaces even between syllables. It's okay in this exercise, because the dataset is (mostly) in English.
In [152]:
from sklearn.base import BaseEstimator, TransformerMixin
class EmailToWordCounterTransformer(BaseEstimator, TransformerMixin):
def __init__(self, strip_headers=True, lower_case=True, remove_punctuation=True,
replace_urls=True, replace_numbers=True, stemming=True):
self.strip_headers = strip_headers
self.lower_case = lower_case
self.remove_punctuation = remove_punctuation
self.replace_urls = replace_urls
self.replace_numbers = replace_numbers
self.stemming = stemming
def fit(self, X, y=None):
return self
def transform(self, X, y=None):
X_transformed = []
for email in X:
text = email_to_text(email) or ""
if self.lower_case:
text = text.lower()
if self.replace_urls and url_extractor is not None:
urls = list(set(url_extractor.find_urls(text)))
urls.sort(key=lambda url: len(url), reverse=True)
for url in urls:
text = text.replace(url, " URL ")
if self.replace_numbers:
text = re.sub(r'\d+(?:\.\d*(?:[eE]\d+))?', 'NUMBER', text)
if self.remove_punctuation:
text = re.sub(r'\W+', ' ', text, flags=re.M)
word_counts = Counter(text.split())
if self.stemming and stemmer is not None:
stemmed_word_counts = Counter()
for word, count in word_counts.items():
stemmed_word = stemmer.stem(word)
stemmed_word_counts[stemmed_word] += count
word_counts = stemmed_word_counts
X_transformed.append(word_counts)
return np.array(X_transformed)
Let's try this transformer on a few emails:
In [153]:
X_few = X_train[:3]
X_few_wordcounts = EmailToWordCounterTransformer().fit_transform(X_few)
X_few_wordcounts
Out[153]:
array([Counter({'chuck': 1, 'murcko': 1, 'wrote': 1, 'stuff': 1, 'yawn': 1, 'r': 1}),
Counter({'the': 11, 'of': 9, 'and': 8, 'all': 3, 'christian': 3, 'to': 3, 'by': 3, 'jefferson': 2, 'i': 2, 'have': 2, 'superstit': 2, 'one': 2, 'on': 2, 'been': 2, 'ha': 2, 'half': 2, 'rogueri': 2, 'teach': 2, 'jesu': 2, 'some': 1, 'interest': 1, 'quot': 1, 'url': 1, 'thoma': 1, 'examin': 1, 'known': 1, 'word': 1, 'do': 1, 'not': 1, 'find': 1, 'in': 1, 'our': 1, 'particular': 1, 'redeem': 1, 'featur': 1, 'they': 1, 'are': 1, 'alik': 1, 'found': 1, 'fabl': 1, 'mytholog': 1, 'million': 1, 'innoc': 1, 'men': 1, 'women': 1, 'children': 1, 'sinc': 1, 'introduct': 1, 'burnt': 1, 'tortur': 1, 'fine': 1, 'imprison': 1, 'what': 1, 'effect': 1, 'thi': 1, 'coercion': 1, 'make': 1, 'world': 1, 'fool': 1, 'other': 1, 'hypocrit': 1, 'support': 1, 'error': 1, 'over': 1, 'earth': 1, 'six': 1, 'histor': 1, 'american': 1, 'john': 1, 'e': 1, 'remsburg': 1, 'letter': 1, 'william': 1, 'short': 1, 'again': 1, 'becom': 1, 'most': 1, 'pervert': 1, 'system': 1, 'that': 1, 'ever': 1, 'shone': 1, 'man': 1, 'absurd': 1, 'untruth': 1, 'were': 1, 'perpetr': 1, 'upon': 1, 'a': 1, 'larg': 1, 'band': 1, 'dupe': 1, 'import': 1, 'led': 1, 'paul': 1, 'first': 1, 'great': 1, 'corrupt': 1}),
Counter({'url': 5, 's': 3, 'group': 3, 'to': 3, 'in': 2, 'forteana': 2, 'martin': 2, 'an': 2, 'and': 2, 'we': 2, 'is': 2, 'yahoo': 2, 'unsubscrib': 2, 'y': 1, 'adamson': 1, 'wrote': 1, 'for': 1, 'altern': 1, 'rather': 1, 'more': 1, 'factual': 1, 'base': 1, 'rundown': 1, 'on': 1, 'hamza': 1, 'career': 1, 'includ': 1, 'hi': 1, 'belief': 1, 'that': 1, 'all': 1, 'non': 1, 'muslim': 1, 'yemen': 1, 'should': 1, 'be': 1, 'murder': 1, 'outright': 1, 'know': 1, 'how': 1, 'unbias': 1, 'memri': 1, 'don': 1, 't': 1, 'html': 1, 'rob': 1, 'sponsor': 1, 'number': 1, 'dvd': 1, 'free': 1, 'p': 1, 'join': 1, 'now': 1, 'from': 1, 'thi': 1, 'send': 1, 'email': 1, 'your': 1, 'use': 1, 'of': 1, 'subject': 1})],
dtype=object)
This looks about right!
Now we have the word counts, and we need to convert them to vectors. For this, we will build another transformer whose fit() method will build the vocabulary (an ordered list of the most common words) and whose transform() method will use the vocabulary to convert word counts to vectors. The output is a sparse matrix.
In [154]:
from scipy.sparse import csr_matrix
class WordCounterToVectorTransformer(BaseEstimator, TransformerMixin):
def __init__(self, vocabulary_size=1000):
self.vocabulary_size = vocabulary_size
def fit(self, X, y=None):
total_count = Counter()
for word_count in X:
for word, count in word_count.items():
total_count[word] += min(count, 10)
most_common = total_count.most_common()[:self.vocabulary_size]
self.most_common_ = most_common
self.vocabulary_ = {word: index + 1 for index, (word, count) in enumerate(most_common)}
return self
def transform(self, X, y=None):
rows = []
cols = []
data = []
for row, word_count in enumerate(X):
for word, count in word_count.items():
rows.append(row)
cols.append(self.vocabulary_.get(word, 0))
data.append(count)
return csr_matrix((data, (rows, cols)), shape=(len(X), self.vocabulary_size + 1))
In [155]:
vocab_transformer = WordCounterToVectorTransformer(vocabulary_size=10)
X_few_vectors = vocab_transformer.fit_transform(X_few_wordcounts)
X_few_vectors
Out[155]:
<3x11 sparse matrix of type '<class 'numpy.int64'>' with 20 stored elements in Compressed Sparse Row format>
In [156]:
X_few_vectors.toarray()
Out[156]:
array([[ 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
[99, 11, 9, 8, 1, 3, 3, 1, 3, 2, 3],
[65, 0, 1, 2, 5, 3, 1, 2, 0, 1, 0]], dtype=int64)
What does this matrix mean? Well, the 64 in the third row, first column, means that the third email contains 64 words that are not part of the vocabulary. The 1 next to it means that the first word in the vocabulary is present once in this email. The 2 next to it means that the second word is present twice, and so on. You can look at the vocabulary to know which words we are talking about. The first word is "of", the second word is "and", etc.
In [157]:
vocab_transformer.vocabulary_
Out[157]:
{'the': 1,
'of': 2,
'and': 3,
'url': 4,
'to': 5,
'all': 6,
'in': 7,
'christian': 8,
'on': 9,
'by': 10}
We are now ready to train our first spam classifier! Let's transform the whole dataset:
In [158]:
from sklearn.pipeline import Pipeline
preprocess_pipeline = Pipeline([
("email_to_wordcount", EmailToWordCounterTransformer()),
("wordcount_to_vector", WordCounterToVectorTransformer()),
])
X_train_transformed = preprocess_pipeline.fit_transform(X_train)
In [159]:
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import cross_val_score
log_clf = LogisticRegression(solver="liblinear", random_state=42)
score = cross_val_score(log_clf, X_train_transformed, y_train, cv=3, verbose=3)
score.mean()
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers. [Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 0.0s remaining: 0.0s [Parallel(n_jobs=1)]: Done 2 out of 2 | elapsed: 0.1s remaining: 0.0s
[CV] ................................................................ [CV] .................................. , score=0.98375, total= 0.0s [CV] ................................................................ [CV] .................................... , score=0.985, total= 0.1s [CV] ................................................................ [CV] ................................... , score=0.9925, total= 0.1s
[Parallel(n_jobs=1)]: Done 3 out of 3 | elapsed: 0.2s finished
Out[159]:
0.9870833333333334
Over 98.7%, not bad for a first try! :) However, remember that we are using the "easy" dataset. You can try with the harder datasets, the results won't be so amazing. You would have to try multiple models, select the best ones and fine-tune them using cross-validation, and so on.
But you get the picture, so let's stop now, and just print out the precision/recall we get on the test set:
In [160]:
from sklearn.metrics import precision_score, recall_score
X_test_transformed = preprocess_pipeline.transform(X_test)
log_clf = LogisticRegression(solver="liblinear", random_state=42)
log_clf.fit(X_train_transformed, y_train)
y_pred = log_clf.predict(X_test_transformed)
print("Precision: {:.2f}%".format(100 * precision_score(y_test, y_pred)))
print("Recall: {:.2f}%".format(100 * recall_score(y_test, y_pred)))
Precision: 94.90% Recall: 97.89%
In [ ]: