Sebastian Raschka, 2015
Note that the optional watermark extension is a small IPython notebook plugin that I developed to make the code reproducible. You can just skip the following line(s).
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%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn
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# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
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import pandas as pd
df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wdbc.data', header=None)
df.head()
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df.shape
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from sklearn.preprocessing import LabelEncoder
X = df.loc[:, 2:].values
y = df.loc[:, 1].values
le = LabelEncoder()
y = le.fit_transform(y)
le.transform(['M', 'B'])
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In [15]:
from sklearn.cross_validation import train_test_split
X_train, X_test, y_train, y_test = \
train_test_split(X, y, test_size=0.20, random_state=1)
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from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import Pipeline
pipe_lr = Pipeline([('scl', StandardScaler()),
('pca', PCA(n_components=2)),
('clf', LogisticRegression(random_state=1))])
pipe_lr.fit(X_train, y_train)
print('Test Accuracy: %.3f' % pipe_lr.score(X_test, y_test))
y_pred = pipe_lr.predict(X_test)
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import numpy as np
from sklearn.cross_validation import StratifiedKFold
kfold = StratifiedKFold(y=y_train,
n_folds=10,
random_state=1)
scores = []
for k, (train, test) in enumerate(kfold):
pipe_lr.fit(X_train[train], y_train[train])
score = pipe_lr.score(X_train[test], y_train[test])
scores.append(score)
print('Fold: %s, Class dist.: %s, Acc: %.3f' % (k+1, np.bincount(y_train[train]), score))
print('\nCV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
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from sklearn.cross_validation import cross_val_score
scores = cross_val_score(estimator=pipe_lr,
X=X_train,
y=y_train,
cv=10,
n_jobs=1)
print('CV accuracy scores: %s' % scores)
print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
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%matplotlib inline
import matplotlib.pyplot as plt
from sklearn.learning_curve import learning_curve
pipe_lr = Pipeline([('scl', StandardScaler()),
('clf', LogisticRegression(penalty='l2', random_state=0))])
train_sizes, train_scores, test_scores =\
learning_curve(estimator=pipe_lr,
X=X_train,
y=y_train,
train_sizes=np.linspace(0.1, 1.0, 10),
cv=10,
n_jobs=1)
train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
plt.plot(train_sizes, train_mean,
color='blue', marker='o',
markersize=5, label='training accuracy')
plt.fill_between(train_sizes,
train_mean + train_std,
train_mean - train_std,
alpha=0.15, color='blue')
plt.plot(train_sizes, test_mean,
color='green', linestyle='--',
marker='s', markersize=5,
label='validation accuracy')
plt.fill_between(train_sizes,
test_mean + test_std,
test_mean - test_std,
alpha=0.15, color='green')
plt.grid()
plt.xlabel('Number of training samples')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')
plt.ylim([0.8, 1.0])
plt.tight_layout()
# plt.savefig('./figures/learning_curve.png', dpi=300)
plt.show()
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from sklearn.learning_curve import validation_curve
param_range = [0.001, 0.01, 0.1, 1.0, 10.0, 100.0]
train_scores, test_scores = validation_curve(
estimator=pipe_lr,
X=X_train,
y=y_train,
param_name='clf__C',
param_range=param_range,
cv=10)
train_mean = np.mean(train_scores, axis=1)
train_std = np.std(train_scores, axis=1)
test_mean = np.mean(test_scores, axis=1)
test_std = np.std(test_scores, axis=1)
plt.plot(param_range, train_mean,
color='blue', marker='o',
markersize=5, label='training accuracy')
plt.fill_between(param_range, train_mean + train_std,
train_mean - train_std, alpha=0.15,
color='blue')
plt.plot(param_range, test_mean,
color='green', linestyle='--',
marker='s', markersize=5,
label='validation accuracy')
plt.fill_between(param_range,
test_mean + test_std,
test_mean - test_std,
alpha=0.15, color='green')
plt.grid()
plt.xscale('log')
plt.legend(loc='lower right')
plt.xlabel('Parameter C')
plt.ylabel('Accuracy')
plt.ylim([0.8, 1.0])
plt.tight_layout()
# plt.savefig('./figures/validation_curve.png', dpi=300)
plt.show()
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from sklearn.grid_search import GridSearchCV
from sklearn.svm import SVC
pipe_svc = Pipeline([('scl', StandardScaler()),
('clf', SVC(random_state=1))])
param_range = [0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0]
param_grid = [{'clf__C': param_range,
'clf__kernel': ['linear']},
{'clf__C': param_range,
'clf__gamma': param_range,
'clf__kernel': ['rbf']}]
gs = GridSearchCV(estimator=pipe_svc,
param_grid=param_grid,
scoring='accuracy',
cv=10,
n_jobs=-1)
gs = gs.fit(X_train, y_train)
print(gs.best_score_)
print(gs.best_params_)
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clf = gs.best_estimator_
clf.fit(X_train, y_train)
print('Test accuracy: %.3f' % clf.score(X_test, y_test))
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gs = GridSearchCV(estimator=pipe_svc,
param_grid=param_grid,
scoring='accuracy',
cv=5)
scores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)
print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
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from sklearn.tree import DecisionTreeClassifier
gs = GridSearchCV(estimator=DecisionTreeClassifier(random_state=0),
param_grid=[{'max_depth': [1, 2, 3, 4, 5, 6, 7, None]}],
scoring='accuracy',
cv=5)
scores = cross_val_score(gs, X_train, y_train, scoring='accuracy', cv=5)
print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))
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from sklearn.metrics import confusion_matrix
pipe_svc.fit(X_train, y_train)
y_pred = pipe_svc.predict(X_test)
confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
print(confmat)
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fig, ax = plt.subplots(figsize=(2.5, 2.5))
ax.matshow(confmat, cmap=plt.cm.Blues, alpha=0.3)
for i in range(confmat.shape[0]):
for j in range(confmat.shape[1]):
ax.text(x=j, y=i, s=confmat[i, j], va='center', ha='center')
plt.xlabel('predicted label')
plt.ylabel('true label')
plt.tight_layout()
# plt.savefig('./figures/confusion_matrix.png', dpi=300)
plt.show()
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from sklearn.metrics import precision_score, recall_score, f1_score
print('Precision: %.3f' % precision_score(y_true=y_test, y_pred=y_pred))
print('Recall: %.3f' % recall_score(y_true=y_test, y_pred=y_pred))
print('F1: %.3f' % f1_score(y_true=y_test, y_pred=y_pred))
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from sklearn.metrics import make_scorer, f1_score
scorer = make_scorer(f1_score, pos_label=0)
c_gamma_range = [0.01, 0.1, 1.0, 10.0]
param_grid = [{'clf__C': c_gamma_range,
'clf__kernel': ['linear']},
{'clf__C': c_gamma_range,
'clf__gamma': c_gamma_range,
'clf__kernel': ['rbf'],}]
gs = GridSearchCV(estimator=pipe_svc,
param_grid=param_grid,
scoring=scorer,
cv=10,
n_jobs=-1)
gs = gs.fit(X_train, y_train)
print(gs.best_score_)
print(gs.best_params_)
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from sklearn.metrics import roc_curve, auc
from scipy import interp
X_train2 = X_train[:, [4, 14]]
cv = StratifiedKFold(y_train, n_folds=3, random_state=1)
fig = plt.figure(figsize=(7, 5))
mean_tpr = 0.0
mean_fpr = np.linspace(0, 1, 100)
all_tpr = []
for i, (train, test) in enumerate(cv):
probas = pipe_lr.fit(X_train2[train],
y_train[train]).predict_proba(X_train2[test])
fpr, tpr, thresholds = roc_curve(y_train[test],
probas[:, 1],
pos_label=1)
mean_tpr += interp(mean_fpr, fpr, tpr)
mean_tpr[0] = 0.0
roc_auc = auc(fpr, tpr)
plt.plot(fpr,
tpr,
lw=1,
label='ROC fold %d (area = %0.2f)'
% (i+1, roc_auc))
plt.plot([0, 1],
[0, 1],
linestyle='--',
color=(0.6, 0.6, 0.6),
label='random guessing')
mean_tpr /= len(cv)
mean_tpr[-1] = 1.0
mean_auc = auc(mean_fpr, mean_tpr)
plt.plot(mean_fpr, mean_tpr, 'k--',
label='mean ROC (area = %0.2f)' % mean_auc, lw=2)
plt.plot([0, 0, 1],
[0, 1, 1],
lw=2,
linestyle=':',
color='black',
label='perfect performance')
plt.xlim([-0.05, 1.05])
plt.ylim([-0.05, 1.05])
plt.xlabel('false positive rate')
plt.ylabel('true positive rate')
plt.title('Receiver Operator Characteristic')
plt.legend(loc="lower right")
plt.tight_layout()
# plt.savefig('./figures/roc.png', dpi=300)
plt.show()
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pipe_svc = pipe_svc.fit(X_train2, y_train)
y_pred2 = pipe_svc.predict(X_test[:, [4, 14]])
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from sklearn.metrics import roc_auc_score, accuracy_score
print('ROC AUC: %.3f' % roc_auc_score(y_true=y_test, y_score=y_pred2))
print('Accuracy: %.3f' % accuracy_score(y_true=y_test, y_pred=y_pred2))
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pre_scorer = make_scorer(score_func=precision_score,
pos_label=1,
greater_is_better=True,
average='micro')