ch06.py

# coding: utf-8


import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
import numpy as np
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
from sklearn.model_selection import validation_curve
from sklearn.model_selection import GridSearchCV
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import precision_score, recall_score, f1_score
from sklearn.metrics import make_scorer
from sklearn.metrics import roc_curve, auc
from scipy import interp
from sklearn.utils import resample

# *Python Machine Learning 2nd Edition* by [Sebastian Raschka](https://sebastianraschka.com), Packt Publishing Ltd. 2017
# 
# Code Repository: https://github.com/rasbt/python-machine-learning-book-2nd-edition
# 
# Code License: [MIT License](https://github.com/rasbt/python-machine-learning-book-2nd-edition/blob/master/LICENSE.txt)

# # Python Machine Learning - Code Examples

# # Chapter 6 - Learning Best Practices for Model Evaluation and Hyperparameter Tuning

# 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).





# *The use of `watermark` is optional. You can install this IPython extension via "`pip install watermark`". For more information, please see: https://github.com/rasbt/watermark.*


# ### Overview

# - [Streamlining workflows with pipelines](#Streamlining-workflows-with-pipelines)
#   - [Loading the Breast Cancer Wisconsin dataset](#Loading-the-Breast-Cancer-Wisconsin-dataset)
#   - [Combining transformers and estimators in a pipeline](#Combining-transformers-and-estimators-in-a-pipeline)
# - [Using k-fold cross-validation to assess model performance](#Using-k-fold-cross-validation-to-assess-model-performance)
#   - [The holdout method](#The-holdout-method)
#   - [K-fold cross-validation](#K-fold-cross-validation)
# - [Debugging algorithms with learning and validation curves](#Debugging-algorithms-with-learning-and-validation-curves)
#   - [Diagnosing bias and variance problems with learning curves](#Diagnosing-bias-and-variance-problems-with-learning-curves)
#   - [Addressing overfitting and underfitting with validation curves](#Addressing-overfitting-and-underfitting-with-validation-curves)
# - [Fine-tuning machine learning models via grid search](#Fine-tuning-machine-learning-models-via-grid-search)
#   - [Tuning hyperparameters via grid search](#Tuning-hyperparameters-via-grid-search)
#   - [Algorithm selection with nested cross-validation](#Algorithm-selection-with-nested-cross-validation)
# - [Looking at different performance evaluation metrics](#Looking-at-different-performance-evaluation-metrics)
#   - [Reading a confusion matrix](#Reading-a-confusion-matrix)
#   - [Optimizing the precision and recall of a classification model](#Optimizing-the-precision-and-recall-of-a-classification-model)
#   - [Plotting a receiver operating characteristic](#Plotting-a-receiver-operating-characteristic)
#   - [The scoring metrics for multiclass classification](#The-scoring-metrics-for-multiclass-classification)
# - [Dealing with class imbalance](#Dealing-with-class-imbalance)
# - [Summary](#Summary)






# # Streamlining workflows with pipelines

# ...

# ## Loading the Breast Cancer Wisconsin dataset




df = pd.read_csv('https://archive.ics.uci.edu/ml/'
                 'machine-learning-databases'
                 '/breast-cancer-wisconsin/wdbc.data', header=None)

# if the Breast Cancer dataset is temporarily unavailable from the
# UCI machine learning repository, un-comment the following line
# of code to load the dataset from a local path:

# df = pd.read_csv('wdbc.data', header=None)

df.head()




df.shape






X = df.loc[:, 2:].values
y = df.loc[:, 1].values
le = LabelEncoder()
y = le.fit_transform(y)
le.classes_




le.transform(['M', 'B'])





X_train, X_test, y_train, y_test =     train_test_split(X, y, 
                     test_size=0.20,
                     stratify=y,
                     random_state=1)



# ## Combining transformers and estimators in a pipeline




pipe_lr = make_pipeline(StandardScaler(),
                        PCA(n_components=2),
                        LogisticRegression(random_state=1))

pipe_lr.fit(X_train, y_train)
y_pred = pipe_lr.predict(X_test)
print('Test Accuracy: %.3f' % pipe_lr.score(X_test, y_test))







# # Using k-fold cross validation to assess model performance

# ...

# ## The holdout method






# ## K-fold cross-validation







    

kfold = StratifiedKFold(n_splits=10,
                        random_state=1).split(X_train, y_train)

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: %2d, Class dist.: %s, Acc: %.3f' % (k+1,
          np.bincount(y_train[train]), score))
    
print('\nCV accuracy: %.3f +/- %.3f' % (np.mean(scores), np.std(scores)))





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)))



# # Debugging algorithms with learning curves


# ## Diagnosing bias and variance problems with learning curves









pipe_lr = make_pipeline(StandardScaler(),
                        LogisticRegression(penalty='l2', random_state=1))

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, 1.03])
plt.tight_layout()
#plt.savefig('images/06_05.png', dpi=300)
plt.show()



# ## Addressing over- and underfitting with validation curves





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='logisticregression__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('images/06_06.png', dpi=300)
plt.show()



# # Fine-tuning machine learning models via grid search


# ## Tuning hyperparameters via grid search 




pipe_svc = make_pipeline(StandardScaler(),
                         SVC(random_state=1))

param_range = [0.0001, 0.001, 0.01, 0.1, 1.0, 10.0, 100.0, 1000.0]

param_grid = [{'svc__C': param_range, 
               'svc__kernel': ['linear']},
              {'svc__C': param_range, 
               'svc__gamma': param_range, 
               'svc__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_)




clf = gs.best_estimator_
clf.fit(X_train, y_train)
print('Test accuracy: %.3f' % clf.score(X_test, y_test))



# ## Algorithm selection with nested cross-validation







gs = GridSearchCV(estimator=pipe_svc,
                  param_grid=param_grid,
                  scoring='accuracy',
                  cv=2)

scores = cross_val_score(gs, X_train, y_train, 
                         scoring='accuracy', cv=5)
print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores),
                                      np.std(scores)))





gs = GridSearchCV(estimator=DecisionTreeClassifier(random_state=0),
                  param_grid=[{'max_depth': [1, 2, 3, 4, 5, 6, 7, None]}],
                  scoring='accuracy',
                  cv=2)

scores = cross_val_score(gs, X_train, y_train, 
                         scoring='accuracy', cv=5)
print('CV accuracy: %.3f +/- %.3f' % (np.mean(scores), 
                                      np.std(scores)))



# # Looking at different performance evaluation metrics

# ...

# ## Reading a 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)




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('images/06_09.png', dpi=300)
plt.show()


# ### Additional Note

# Remember that we previously encoded the class labels so that *malignant* samples are the "postive" class (1), and *benign* samples are the "negative" class (0):



le.transform(['M', 'B'])




confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
print(confmat)


# Next, we printed the confusion matrix like so:



confmat = confusion_matrix(y_true=y_test, y_pred=y_pred)
print(confmat)


# Note that the (true) class 0 samples that are correctly predicted as class 0 (true negatives) are now in the upper left corner of the matrix (index 0, 0). In order to change the ordering so that the true negatives are in the lower right corner (index 1,1) and the true positves are in the upper left, we can use the `labels` argument like shown below:



confmat = confusion_matrix(y_true=y_test, y_pred=y_pred, labels=[1, 0])
print(confmat)


# We conclude:
# 
# Assuming that class 1 (malignant) is the positive class in this example, our model correctly classified 71 of the samples that belong to class 0 (true negatives) and 40 samples that belong to class 1 (true positives), respectively. However, our model also incorrectly misclassified 1 sample from class 0 as class 1 (false positive), and it predicted that 2 samples are benign although it is a malignant tumor (false negatives).


# ## Optimizing the precision and recall of a classification model




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))





scorer = make_scorer(f1_score, pos_label=0)

c_gamma_range = [0.01, 0.1, 1.0, 10.0]

param_grid = [{'svc__C': c_gamma_range,
               'svc__kernel': ['linear']},
              {'svc__C': c_gamma_range,
               'svc__gamma': c_gamma_range,
               'svc__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_)



# ## Plotting a receiver operating characteristic




pipe_lr = make_pipeline(StandardScaler(),
                        PCA(n_components=2),
                        LogisticRegression(penalty='l2', 
                                           random_state=1, 
                                           C=100.0))

X_train2 = X_train[:, [4, 14]]
    

cv = list(StratifiedKFold(n_splits=3, 
                          random_state=1).split(X_train, y_train))

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,
             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],
         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.legend(loc="lower right")

plt.tight_layout()
# plt.savefig('images/06_10.png', dpi=300)
plt.show()



# ## The scoring metrics for multiclass classification



pre_scorer = make_scorer(score_func=precision_score, 
                         pos_label=1, 
                         greater_is_better=True, 
                         average='micro')


# ## Dealing with class imbalance



X_imb = np.vstack((X[y == 0], X[y == 1][:40]))
y_imb = np.hstack((y[y == 0], y[y == 1][:40]))




y_pred = np.zeros(y_imb.shape[0])
np.mean(y_pred == y_imb) * 100





print('Number of class 1 samples before:', X_imb[y_imb == 1].shape[0])

X_upsampled, y_upsampled = resample(X_imb[y_imb == 1],
                                    y_imb[y_imb == 1],
                                    replace=True,
                                    n_samples=X_imb[y_imb == 0].shape[0],
                                    random_state=123)

print('Number of class 1 samples after:', X_upsampled.shape[0])




X_bal = np.vstack((X[y == 0], X_upsampled))
y_bal = np.hstack((y[y == 0], y_upsampled))




y_pred = np.zeros(y_bal.shape[0])
np.mean(y_pred == y_bal) * 100



# # Summary

# ...

# ---
# 
# Readers may ignore the next cell.