def run_ann_with_only_dimensionality_reduction(pca_x_train, ica_x_train,
rp_x_train, variance_x_train):
classifier = MLPClassifier(hidden_layer_sizes=(25),
activation='logistic',
max_iter=5000,
solver='adam')
cv = ShuffleSplit(n_splits=20, test_size=0.2, random_state=0)
ann_data = standard_scaler.fit_transform(data[features])
plot_learning_curve("{}- DR - ANN Learning Curve".format(plot_name),
classifier,
"{}- DR - ANN Learning Curve Simple".format(plot_name),
ann_data,
data[target],
ylim=(0, 1),
cv=cv,
n_jobs=4)
plot_learning_curve("{}-PCA DR - ANN Learning Curve".format(plot_name),
classifier,
"{}- PCA DR - ANN Learning Curve".format(plot_name),
standard_scaler.fit_transform(pca_x_train),
data[target],
ylim=(0, 1),
cv=cv,
n_jobs=4)
plot_learning_curve("{}-ICA DR - ANN Learning Curve".format(plot_name),
classifier,
"{}- ICA DR - ANN Learning Curve".format(plot_name),
standard_scaler.fit_transform(ica_x_train),
data[target],
ylim=(0, 1),
cv=cv,
n_jobs=4)
plot_learning_curve("{}-RP DR - ANN Learning Curve".format(plot_name),
classifier,
"{}- RP DR - ANN Learning Curve".format(plot_name),
standard_scaler.fit_transform(rp_x_train),
data[target],
ylim=(0, 1),
cv=cv,
n_jobs=4)
plot_learning_curve(
"{}-Variance filter DR - ANN Learning Curve".format(plot_name),
classifier,
"{}- Variance filter DR - ANN Learning Curve".format(plot_name),
standard_scaler.fit_transform(variance_x_train),
data[target],
ylim=(0, 1),
cv=cv,
n_jobs=4)
def clustering_after_reduction(pca_x_train, ica_x_train, rp_x_train,
variance_x_train):
ica_kmeans_k = 6
ica_em_k = 5
pca_em_k = 5
pca_kmeans_k = 7
rp_em_k = 6
rp_kmeans_k = 7
variance_filter_em_k = 13
variance_filter_kmeans_k = 6
cluster_params1 = simple_clustering(
"{}- ICA".format(plot_name), ica_x_train, ica_kmeans_k, ica_em_k,
base_experiment.identify_top_2_features(ica_x_train))
cluster_params2 = simple_clustering(
"{}- PCA".format(plot_name), pca_x_train, pca_kmeans_k, pca_em_k,
base_experiment.identify_top_2_features(pca_x_train))
cluster_params3 = simple_clustering(
"{}- Random Projection".format(plot_name), rp_x_train, rp_kmeans_k,
rp_em_k, base_experiment.identify_top_2_features(rp_x_train))
cluster_params4 = simple_clustering(
"{}- Variance Filtering".format(plot_name), variance_x_train,
variance_filter_kmeans_k, variance_filter_em_k,
base_experiment.identify_top_2_features(variance_x_train))
final_cluster_array = np.concatenate(
(cluster_params1, cluster_params2, cluster_params3, cluster_params4),
axis=1)
cluster_features = pd.DataFrame(data=final_cluster_array)
classifier = MLPClassifier(hidden_layer_sizes=(25),
activation='logistic',
max_iter=5000,
solver='adam')
cv = ShuffleSplit(n_splits=20, test_size=0.2, random_state=0)
plot_learning_curve(
"{}-Cluster Features - ANN Learning Curve".format(plot_name),
classifier,
"{}- Cluster Features - ANN Learning Curve".format(plot_name),
standard_scaler.fit_transform(cluster_features),
data[target],
ylim=(0, 1),
cv=cv,
n_jobs=4)
def train(X,
y,
config,
max_epochs,
batch_iterator='BatchIterator',
pretrained_model=None,
name=None,
debug=True):
# print globals()['net_name']
global net_name
sample = 500 if debug else X.shape[0]
X_t, y_t = X[:sample], y[:sample, :]
param_dump_folder = './model_%s' % (m_time.strftime("%m_%d_%H_%M_%S")
if name is None else name)
print 'Model name: %s' % param_dump_folder
print 'Debug mode:', debug
# Load the net and add a function to save the params after every epoch
nnet = globals()[config](batch_iterator, max_epochs)
func_save_model = lambda n, h: save_model_params(n, h, param_dump_folder,
debug)
nnet.on_epoch_finished.append(func_save_model)
func_learning_curve = lambda n, h: plot_learning_curve(
n, h, param_dump_folder, debug)
nnet.on_epoch_finished.append(func_learning_curve)
# func_viz_weights = lambda n, h: plot_weight_matrix_grid(
# n, h, param_dump_folder, debug)
# nnet.on_epoch_finished.append(func_viz_weights)
print 'Config: %s' % config
print 'Max num epochs: %d' % nnet.max_epochs
print "Dataset loaded, shape:", X_t.shape, y_t.shape
print "Loading pretrained model %s ..." % pretrained_model
if pretrained_model is not None:
pretrained_weights = pickle.load(open(pretrained_model, 'rb'))
nnet.load_params_from(pretrained_weights)
print "Finished loading"
# Train
if not debug:
if not os.path.exists(param_dump_folder):
os.mkdir(param_dump_folder)
try:
nnet.fit(X_t, y_t)
except KeyboardInterrupt:
pass
if not debug:
nnet.save_params_to(os.path.join(param_dump_folder, 'model_final.pkl'))
def train(X, y, config, max_epochs, batch_iterator='BatchIterator',
pretrained_model=None, name=None, debug=True):
# print globals()['net_name']
global net_name
sample = 500 if debug else X.shape[0]
X_t, y_t = X[:sample], y[:sample, :]
param_dump_folder = './model_%s' % (m_time.strftime(
"%m_%d_%H_%M_%S") if name is None else name)
print 'Model name: %s' % param_dump_folder
print 'Debug mode:', debug
# Load the net and add a function to save the params after every epoch
nnet = globals()[config](batch_iterator, max_epochs)
func_save_model = lambda n, h: save_model_params(
n, h, param_dump_folder, debug)
nnet.on_epoch_finished.append(func_save_model)
func_learning_curve = lambda n, h: plot_learning_curve(
n, h, param_dump_folder, debug)
nnet.on_epoch_finished.append(func_learning_curve)
# func_viz_weights = lambda n, h: plot_weight_matrix_grid(
# n, h, param_dump_folder, debug)
# nnet.on_epoch_finished.append(func_viz_weights)
print 'Config: %s' % config
print 'Max num epochs: %d' % nnet.max_epochs
print "Dataset loaded, shape:", X_t.shape, y_t.shape
print "Loading pretrained model %s ..." % pretrained_model
if pretrained_model is not None:
pretrained_weights = pickle.load(open(pretrained_model, 'rb'))
nnet.load_params_from(pretrained_weights)
print "Finished loading"
# Train
if not debug:
if not os.path.exists(param_dump_folder):
os.mkdir(param_dump_folder)
try:
nnet.fit(X_t, y_t)
except KeyboardInterrupt:
pass
if not debug:
nnet.save_params_to(os.path.join(
param_dump_folder, 'model_final.pkl'))
'criterion': 'gini',
'max_depth': 3,
'min_samples_split': 2
})
}
# Running code with default values
plt = print_results(result_dict)
#plt.show()
plt.savefig(fig_path + 'results.png')
title = "Learning Curves for Decision Tree"
plt = plot_learning_curve(DecisionTreeClassifier(criterion='gini',
max_depth=3,
min_samples_split=2),
'Survived',
FEATURES,
titanic_df,
title,
ylim=(0.4, 1.01))
#plt.show()
plt.savefig(fig_path + 'learning_curve_dt.png')
title = "Learning Curves for Neural Networks"
plt = plot_learning_curve(MLPClassifier(activation='identity',
learning_rate='constant',
solver='lbfgs'),
'Survived',
FEATURES,
titanic_df,
title,
ylim=(0.4, 1.01))
title='Wine Quality Neural Network Confusion Matrix')
plt.show()
#DecisionTreeClassifier
singleDT = DecisionTreeClassifier(max_depth=43, max_features=5)
singleDT.fit(X_train, y_train)
DT_predictions = singleDT.predict(X_test)
DT_accuracy = accuracy_score(y_test, DT_predictions)
print 'Wine Quality Decision Tree Accuracy:', DT_accuracy
print '-----------------'
cv = ShuffleSplit(n_splits=10, test_size=0.3, random_state=0)
plot_learning_curve(singleDT,
'Wine Quality Decision Tree Learning Curve',
X,
y,
ylim=(0.5, 1.01),
cv=cv,
n_jobs=4)
plt.show()
#Boosting
AdaBoost = AdaBoostClassifier(DecisionTreeClassifier(max_depth=10),
n_estimators=40)
AdaBoost.fit(X_train, y_train)
boosted_accuracy = AdaBoost.score(X_test, y_test)
print 'Wine Quality AdaBoost Accuracy:', boosted_accuracy
print '-----------------'
cv = ShuffleSplit(n_splits=10, test_size=0.3, random_state=0)
plot_learning_curve(AdaBoost,
'income - Linear SVM': build_model(linear_svm_fn, 'income', FEATURES, adult_df, options={'C': 0.1, 'loss': 'hinge'}),
'income - SVM Linear': build_model(svm_linear_fn, 'income', FEATURES, adult_df, options={'C': 1, 'gamma': 0.1}),
'income - SVM RBF': build_model(svm_rbf_fn, 'income', FEATURES, adult_df, options={'C': 1, 'gamma': 0.1}),
'income - Ada Boosting': build_model(ada_boosting_fn, 'income', FEATURES, adult_df, options={'algorithm': 'SAMME.R', 'learning_rate': 1, 'n_estimators': 500}),
'income - Gradient Boosting': build_model(gradient_boosting_fn, 'income', FEATURES, adult_df, options={'criterion': 'friedman_mse', 'learning_rate': 0.1, 'loss': 'exponential', 'n_estimators': 100}),
'income - Neural networks': build_model(neural_network_fn, 'income', FEATURES, adult_df, options={'activation':'tanh', 'learning_rate':'invscaling', 'solver': 'adam'}),
'income - Decision_tree': build_model(decision_tree_fn, 'income', FEATURES, adult_df, options={'criterion': 'gini', 'max_depth': 3, 'min_samples_split': 2})
}
# Running code with default values
plt = print_results(result_dict)
#plt.show()
plt.savefig(fig_path + 'results.png')
title = "Learning Curves for Decision Tree"
plt = plot_learning_curve(DecisionTreeClassifier(criterion='gini', max_depth= 8, min_samples_split=12), 'income', FEATURES, adult_df, title, ylim=(0.4, 1.01))
#plt.show()
plt.savefig(fig_path + 'learning_curve_dt.png')
title = "Learning Curves for Neural Networks"
plt = plot_learning_curve(MLPClassifier(activation='tanh', learning_rate='invscaling', solver='adam'), 'income', FEATURES, adult_df, title, ylim=(0.4, 1.01))
#plt.show()
plt.savefig(fig_path + 'learning_curve_neural.png')
title = "Learning Curves for AdaBoost"
plt = plot_learning_curve(AdaBoostClassifier(algorithm='SAMME.R', learning_rate=1, n_estimators= 500), 'income', FEATURES, adult_df, title, ylim=(0.4, 1.01))
#plt.show()
plt.savefig(fig_path + 'learning_curve_adaboost.png')
title = "Learning Curves for GradientBoost"
plt = plot_learning_curve(GradientBoostingClassifier(criterion='friedman_mse', learning_rate=0.1, loss='deviance', n_estimators=100), 'income', FEATURES, adult_df, title, ylim=(0.4, 1.01))
C=1e10, # Large C for no regularization
random_state=33,
penalty='l1')
pipe_lrn = create_pipeline(lrn_basic)
results_mod = cv_evaluation(pipe_lrn, train_x.question_text, train_y)
print('The mean train {} and test CV {}.'.format(
round(results_mod['train_score'], 2), round(results_mod['test_score'], 2)))
pipe_lrn.fit(train_x.question_text, train_y)
predictions_valid = pipe_lrn.predict(valid_x.question_text)
val_score = metrics.f1_score(valid_y, predictions_valid)
print('The validation F1_score was of {}'.format(val_score))
h.plot_learning_curve(pipe_lrn,
text,
train_y,
cv=3,
n_jobs=3,
title='Learning Curves (Logistic Classifer)')
# Validating preprocessing with the same base learner
results_mod = cv_evaluation(pipe_lrn, train_x.qt_clean_stop, train_y)
print('The mean train {} and test CV {}.'.format(
round(results_mod['train_score'], 2), round(results_mod['test_score'], 2)))
# 5. Feature Engineering ------------------------------------------------------
# 5.1 Feature Engineering: Ngram and noise reduction
# One problem is the vast amount of text features generated in CountVectorizer
# much of them are pure noise
count_vect = CountVectorizer(binary=True)
count_vect.fit(train_x.qt_clean_stop)
xtrain_count = count_vect.transform(train_x.qt_clean_stop)
def main():
# ['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked']
train_df_ori = pd.read_csv('data/train.csv')
test_df_ori = pd.read_csv('data/test.csv')
train_df, test_df = fea_eng(train_df_ori, test_df_ori)
# colormap = plt.cm.RdBu
# plt.figure(figsize=(14, 12))
# sns.heatmap(train_df.astype(float).corr(), linewidths=0.1, vmax=1.0, square=True, cmap=colormap, linecolor='white',
# annot=True)
# plt.title('Pearson Correlation of Features', y=1.05, size=15)
# plt.show()
# Some useful parameters which will come in handy later on
rf = helper.SklearnHelper(clf=RandomForestClassifier, seed=SEED, params=config.rf_params)
et = helper.SklearnHelper(clf=ExtraTreesClassifier, seed=SEED, params=config.et_params)
lr = helper.SklearnHelper(clf=LogisticRegression, seed=SEED, params=config.lr_params)
gb = helper.SklearnHelper(clf=GradientBoostingClassifier, seed=SEED, params=config.gb_params)
# svc = helper.SklearnHelper(clf=SVC, seed=SEED, params=config.svc_params)
y_train = train_df['Survived'].ravel()
train_df = train_df.drop(['Survived'], axis=1)
x_train = train_df.values
x_test = test_df.values
# # 对XGBoost进行网格搜索
# x_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size=0.2, random_state=SEED)
#
# xgb_grid = GridSearchCV(estimator=xgb.XGBClassifier(n_estimators=102,
# learning_rate=0.01,
# objective='binary:logistic',
# max_depth=2,
# min_child_weight=1,
# gamma=0,
# subsample=0.8,
# colsample_bytree=0.9,
# seed=SEED),
# param_grid=config.xgb_grid_params,
# cv=5)
# xgb_grid.fit(X=x_train, y=y_train)
# y_scores = xgb_grid.predict(x_test)
# # Print model report:
# print("Model Report")
# print('best parameter:' + str(xgb_grid.best_params_))
# print("Accuracy : {}".format(accuracy_score(y_true=y_test, y_pred=y_scores)))
# print("AUC Score (Test): {}".format(roc_auc_score(y_true=y_test, y_score=y_scores)))
#
# feat_imp = pd.Series(xgb_grid.best_estimator_.feature_importances_, index=train_df.columns.values).sort_values(ascending=False)
# feat_imp.plot(kind='bar', title='Feature Importances')
# plt.ylabel('Feature Importance Score')
# plt.show()
# # 网格搜索，寻找最优参数
# helper.GridSearchProcessing(helper.process_build(clf=RandomForestClassifier(),
# params=config.rf_grid_params,
# X=x_train,
# y=y_train))
# helper.GridSearchProcessing(helper.process_build(clf=ExtraTreesClassifier(),
# params=config.et_grid_params,
# X=x_train,
# y=y_train))
# helper.GridSearchProcessing(helper.process_build(clf=LogisticRegression(),
# params=config.lr_grid_params,
# X=x_train,
# y=y_train))
# process = helper.GridSearchProcessing(helper.process_build(clf=GradientBoostingClassifier(),
# params=config.gb_grid_params,
# X=x_train,
# y=y_train))
# # 对Pool对象调用join()方法会等待所有子进程执行完毕，调用join()之前必须先调用close()，调用close()之后就不能继续添加新的Process了
# process.get_pool().close()
# process.get_pool().join()
# Create our OOF train and test predictions. These base results will be used as new features
et_oof_train, et_oof_test = helper.get_oof(et, x_train, y_train, x_test) # Extra Trees
rf_oof_train, rf_oof_test = helper.get_oof(rf, x_train, y_train, x_test) # Random Forest
lr_oof_train, lr_oof_test = helper.get_oof(lr, x_train, y_train, x_test) # lr
gb_oof_train, gb_oof_test = helper.get_oof(gb, x_train, y_train, x_test) # Gradient Boost
# svc_oof_train, svc_oof_test = helper.get_oof(svc, x_train, y_train, x_test) # Support Vector Classifier
plt.figure(figsize=(10,8))
plt.subplot(2,2,1)
rf_feature_importances = pd.Series(rf.clf.feature_importances_, index=train_df.columns.values).sort_values(ascending=False)
rf_feature_importances.plot(kind='bar', title='Feature Importances')
plt.ylabel('rf Feature Importance Score')
plt.subplot(2,2,2)
et_feature_importances = pd.Series(et.clf.feature_importances_, index=train_df.columns.values).sort_values(ascending=False)
et_feature_importances.plot(kind='bar', title='Feature Importances')
plt.ylabel('et Feature Importance Score')
plt.subplot(2,2,3)
gb_feature_importances = pd.Series(gb.clf.feature_importances_, index=train_df.columns.values).sort_values(ascending=False)
gb_feature_importances.plot(kind='bar', title='Feature Importances')
plt.ylabel('gb Feature Importance Score')
plt.show()
print("Training is complete")
base_predictions_train = pd.DataFrame({'RandomForest': rf_oof_train.ravel(),
'ExtraTrees': et_oof_train.ravel(),
# 'AdaBoost': ada_oof_train.ravel(),
'GradientBoost': gb_oof_train.ravel()
})
x_train = np.concatenate((et_oof_train, rf_oof_train, lr_oof_train, gb_oof_train), axis=1)
x_test = np.concatenate((et_oof_test, rf_oof_test, lr_oof_test, gb_oof_test), axis=1)
xgb_helper = helper.SklearnHelper(clf=xgb.XGBClassifier, seed=SEED, params=config.xgb_params)
xgb_helper.train(x_train, y_train)
predictions = xgb_helper.predict(x_test)
helper.create_feature_map(train_df.columns)
xgb.plot_tree(xgb_helper.clf, fmap='xgb.fmap', num_trees=0)
# 交叉验证，可以快速得到模型的预测正确率
scores = cross_val_score(rf.clf, x_train, y_train, cv=5)
print("rf Accuracy: {}".format(scores))
scores = cross_val_score(et.clf, x_train, y_train, cv=5)
print("et Accuracy: {}".format(scores))
scores = cross_val_score(lr.clf, x_train, y_train, cv=5)
print("lr Accuracy: {}".format(scores))
scores = cross_val_score(gb.clf, x_train, y_train, cv=5)
print("gb Accuracy: {}".format(scores))
# scores = cross_val_score(svc.clf, x_train, y_train, cv=5)
# print("svc Accuracy: {}".format(scores))
scores = cross_val_score(xgb_helper.clf, x_train, y_train, cv=5)
print("stacking xgb Accuracy: {}".format(scores))
helper.plot_learning_curve(xgb_helper.clf, 'xgb_learn curve', x_train, y_train)
helper.plot_learning_curve(rf.clf, 'rf_learn curve', x_train, y_train)
result = pd.DataFrame({"PassengerId": test_df_ori['PassengerId'],
"Survived": predictions
})
result.to_csv("data/submission.csv", index=False)
title='Pen Digits Neural Network Confusion Matrix')
plt.show()
#DecisionTreeClassifier
singleDT = DecisionTreeClassifier(max_depth=22, max_features=5)
singleDT.fit(X_train, y_train)
DT_predictions = singleDT.predict(X_test)
DT_accuracy = accuracy_score(y_test, DT_predictions)
print 'Pen Digit Decision Tree Accuracy:', DT_accuracy
print '-----------------'
cv = ShuffleSplit(n_splits=10, test_size=0.3, random_state=0)
plot_learning_curve(singleDT,
'Pen Digit Decision Tree Learning Curve',
X,
y,
ylim=(0.5, 1.01),
cv=cv,
n_jobs=4)
plt.show()
#Boosting
AdaBoost = AdaBoostClassifier(DecisionTreeClassifier(max_depth=10),
n_estimators=22)
AdaBoost.fit(X_train, y_train)
boosted_accuracy = AdaBoost.score(X_test, y_test)
print 'Pen Digit AdaBoost Accuracy:', boosted_accuracy
print '-----------------'
cv = ShuffleSplit(n_splits=10, test_size=0.3, random_state=0)
plot_learning_curve(AdaBoost,