def main():
parser = argparse.ArgumentParser()
parser.add_argument("training_data", help="Training data (CSV)")
parser.add_argument("testing_data", help="Testing data (CSV)")
parser.add_argument("learning_curve_file", help="Learning curve (PNG)")
parser.add_argument("output", help="Output predictions (CSV)")
args = parser.parse_args()
training_data = pandas.read_csv(args.training_data, header=0)
test_data = pandas.read_csv(args.testing_data, header=0)
all_data = pandas.concat([training_data, test_data])
clean_data(all_data)
training_data = all_data[all_data.Survived.notnull()]
test_data = all_data[all_data.Survived.isnull()]
training_x, training_y, columns = select_features(training_data)
# cross-validate the classifier
split_iterator = sklearn.cross_validation.StratifiedShuffleSplit(training_y, n_iter=10, random_state=4)
# learning curve with default settings
learning_curve(training_x, training_y, args.learning_curve_file)
print "Hyperparameter tuning"
base_classifier = sklearn.ensemble.RandomForestClassifier(100, oob_score=True, random_state=13)
parameter_space = {
"max_features": [None, "sqrt", 0.5, training_x.shape[1] - 1, training_x.shape[1] - 2, training_x.shape[1] - 3,
training_x.shape[1] - 4, training_x.shape[1] - 5],
"min_samples_split": [3, 5, 10, 20],
"min_samples_leaf": [1, 2, 3]
}
parameter_space["max_features"] = [n for n in parameter_space["max_features"] if n is None or n > 0]
tuned_classifier = sklearn.grid_search.GridSearchCV(base_classifier, parameter_space, n_jobs=-1, cv=split_iterator,refit=True)
tuned_classifier.fit(training_x, training_y)
print_tuning_scores(tuned_classifier)
print_feature_importances(columns, tuned_classifier)
training_predictions = tuned_classifier.predict(training_x)
diffs = training_predictions - training_y
print "Training accuracy: {:.3f}".format(1. - numpy.abs(diffs).mean())
ids = test_data.PassengerId.values
test_x, _, _ = select_features(test_data)
test_predictions = tuned_classifier.predict(test_x)
with io.open(args.output, "wb") as csv_out:
csv_writer = csv.writer(csv_out)
csv_writer.writerow(["PassengerId", "Survived"])
csv_writer.writerows(zip(ids, test_predictions.astype(int)))
def main():
args = parse_args()
logging.basicConfig(level=logging.INFO)
original_data = pandas.read_csv(args.input, header=0)
data = preprocess_features(original_data)
if args.save_matrix:
data.to_csv(args.save_matrix)
global N_JOBS
N_JOBS = args.n_jobs
X, y = dataframe_to_ndarrays(data)
check_data(X, y)
cross_val_splits = sklearn.cross_validation.StratifiedShuffleSplit(y, n_iter=5, random_state=4)
if args.decision_tree:
decision_tree(X, y, data, cross_val_splits)
if args.logistic:
logistic_regression_cv(X, y, data, cross_val_splits)
if args.learning_curve:
learning_curve(X, y, args.learning_curve, sklearn.ensemble.RandomForestClassifier(100), "Random Forest Classifier")
if args.forest:
random_forest(X, y, data, cross_val_splits)
if args.predictability:
predictability_tests(X, y, original_data)
if args.elastic:
elastic_net(X, y, cross_val_splits)
if args.xg:
gradient_boosting_exp(X, y, data, cross_val_splits)
if args.xg_hybrid:
gradient_boosting_exp(X, y, data, cross_val_splits, base_classifier=classifiers.LogisticRegressionCVWrapper(5, solver="lbfgs"))
if args.neural_network:
neural_network(X, y, data, cross_val_splits)
if args.ensemble:
ensemble(X, y, cross_val_splits)
if args.logistic_ensemble:
logistic_ensemble(X, y, cross_val_splits)
def learning_curve_analysis(estimator=None, issues_train=None, priority_train=None):
"""
Generates the learning curve for a specific estimator.
:param estimator: Estimator.
:param issues_train: Standarized train set.
:param priority_train: Priorities for train set.
:return: None.
"""
train_sizes, train_scores, test_scores = learning_curve(estimator=estimator, X=issues_train,
y=priority_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.subplots(figsize=(2.5, 2.5))
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.show()
def plot_learning_curve(X, y, model_type, algorithm, metric, transforms):
"""
Plots a learning curve showing model performance against both training and
validation data sets as a function of the number of training samples.
"""
model = define_model(model_type, algorithm)
X = apply_transforms(X, transforms)
t0 = time.time()
train_sizes, train_scores, test_scores = learning_curve(model, X, y, scoring=metric, cv=3, n_jobs=-1)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
fig, ax = plt.subplots(figsize=(16, 10))
ax.set_title('Learning Curve')
ax.set_xlabel('Training Examples')
ax.set_ylabel('Score')
ax.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std,
alpha=0.1, color='r')
ax.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std,
alpha=0.1, color='r')
ax.plot(train_sizes, train_scores_mean, 'o-', color='r', label='Training score')
ax.plot(train_sizes, test_scores_mean, 'o-', color='r', label='Cross-validation score')
ax.legend(loc='best')
fig.tight_layout()
t1 = time.time()
print('Learning curve generated in {0:3f} s.'.format(t1 - t0))
def plot_learning_curve(X, y, algorithm, scaler, pca, selector, metric):
"""
Plots a learning curve showing model performance against both training and
validation data sets as a function of the number of training samples.
"""
model = define_model(algorithm)
X = apply_transforms(X, scaler, pca, selector)
t0 = time.time()
train_sizes, train_scores, test_scores = learning_curve(model, X, y, scoring=metric, cv=3, n_jobs=-1)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
fig, ax = plt.subplots(figsize=(16, 10))
ax.set_title('Learning Curve')
ax.set_xlabel('Training Examples')
ax.set_ylabel('Score')
ax.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std,
alpha=0.1, color='r')
ax.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std,
alpha=0.1, color='r')
ax.plot(train_sizes, train_scores_mean, 'o-', color='r', label='Training score')
ax.plot(train_sizes, test_scores_mean, 'o-', color='r', label='Cross-validation score')
ax.legend(loc='best')
fig.tight_layout()
t1 = time.time()
print('Learning curve generated in {0:3f} s.'.format(t1 - t0))
def learnCurve(self, modelEstimator, title, data, label, cv=None, train_sizes=np.linspace(0.1, 1.0, 5)):
'''
:param estimator: the model/algorithem you choose
:param title: plot title
:param x: train data numpy array style
:param y: target data vector
:param xlim: axes x lim
:param ylim: axes y lim
:param cv:
:return: the figure
'''
train_sizes, train_scores, test_scores = \
learning_curve(modelEstimator, data, label, cv=cv, train_sizes=train_sizes)
'''this is the key score function'''
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.figure()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1, color='b')
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color='g')
plt.plot(train_sizes, train_scores_mean, 'o-', color='b', label='training score')
plt.plot(train_sizes, test_scores_mean, 'o-', color='g', label='cross valid score')
plt.xlabel('training examples')
plt.ylabel('score')
plt.legend(loc='best')
plt.grid('on')
plt.title(title)
plt.show()
def learning_curve_analysis(estimator=None,
issues_train=None,
priority_train=None):
"""
Generates the learning curve for a specific estimator.
:param estimator: Estimator.
:param issues_train: Standarized train set.
:param priority_train: Priorities for train set.
:return: None.
"""
train_sizes, train_scores, test_scores = learning_curve(
estimator=estimator,
X=issues_train,
y=priority_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.subplots(figsize=(2.5, 2.5))
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.show()
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None, n_jobs=1,
train_sizes=np.linspace(.05, 1., 20), verbose=0, plot=True):
"""
画出data在某模型上的learning curve.
参数解释
----------
estimator : 你用的分类器。
title : 表格的标题。
X : 输入的feature，numpy类型
y : 输入的target vector
ylim : tuple格式的(ymin, ymax), 设定图像中纵坐标的最低点和最高点
cv : 做cross-validation的时候，数据分成的份数，其中一份作为cv集，其余n-1份作为training(默认为3份)
n_jobs : 并行的的任务数(默认1)
"""
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes, verbose=verbose)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
if plot:
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel(u"训练样本数")
plt.ylabel(u"得分")
plt.gca().invert_yaxis()
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std, train_scores_mean + train_scores_std,
alpha=0.1, color="b")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std, test_scores_mean + test_scores_std,
alpha=0.1, color="r")
plt.plot(train_sizes, train_scores_mean, 'o-', color="b", label=u"训练集上得分")
plt.plot(train_sizes, test_scores_mean, 'o-', color="r", label=u"交叉验证集上得分")
plt.legend(loc="best")
plt.draw()
plt.show()
plt.gca().invert_yaxis()
midpoint = ((train_scores_mean[-1] + train_scores_std[-1]) + (test_scores_mean[-1] - test_scores_std[-1])) / 2
diff = (train_scores_mean[-1] + train_scores_std[-1]) - (test_scores_mean[-1] - test_scores_std[-1])
return midpoint, diff
def plot_learning_curve(estimator,
title,
X,
y,
ylim=None,
cv=None,
n_jobs=1,
train_sizes=np.linspace(.1, 1.0, 5)):
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes,
train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std,
alpha=0.1,
color="r")
plt.fill_between(train_sizes,
test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std,
alpha=0.1,
color="g")
plt.plot(train_sizes,
train_scores_mean,
'o-',
color="r",
label="Training score")
plt.plot(train_sizes,
test_scores_mean,
'o-',
color="g",
label="Cross-validation score")
plt.legend(loc="best")
plt.show()
get_ipython().magic(u'pinfo sklearn.svm.SVC') help(sklearn.svm.SVC) get_ipython().set_next_input(u'help(sklearn.svm.SVC');get_ipython().magic(u'pinfo sklearn.svm.SVC') get_ipython().magic(u'pinfo sklearn.svm.SVC') help(sklearn.svm.SVC) get_ipython().magic(u'pinfo sklearn.svm.SVC') X_test.max X_test.max() X_test.min() from sklearn.learning_curve import learning_curve learning_curve> get_ipython().magic(u'pinfo learning_curve') get_ipython().magic(u'pinfo learning_curve') get_ipython().magic(u'pinfo learning_curve') estimator = GaussianNB() learning_curve(estimator,data.data,data,target) learning_curve(estimator,data.data,data.target) estimator.fit(X_train,y_train) estimator.fit(X_train,y_train) estimator.fit(X_train,y_train) estimator.fit(X_train,y_train) estimator.fit(X_train,y_train) estimator.fit(X_train,y_train) estimator.fit(X_train,y_train) estimator.fit(X_train,y_train) learning_curve(estimator,data.data,data.target) get_ipython().magic(u'pinfo _') get_ipython().magic(u'pinfo learning_curve') estimator.name estimator.__name__ print estimator
