import winfault

import warnings

import numpy as np

from sklearn.grid_search import GridSearchCV, RandomizedSearchCV

%matplotlib inline

Turbine = winfault.WT_data()

scada = Turbine.scada_data

# warnings suppressed because there's loads of UndefinedMetricWarnings

warnings.filterwarnings("ignore")

features = ['WEC_ava_windspeed',

'CS101__Transformer_temp']

# This gets all the data EXCEPT the faults listed. Labels as nf for "no-fault"

nf = Turbine.filter(scada, Turbine.status_data_wec, "Main_Status",

'fault_case_1', True, 600, 600, [62, 9, 80])

# feeding fault

ff = Turbine.filter(scada, Turbine.status_data_wec, "Main_Status",

'fault_case_1', False, 600, 600, 62)

# generator heating fault

gf = Turbine.filter(scada, Turbine.status_data_wec, "Main_Status",

'fault_case_1', False, 600, 600, 9)

# excitation fault

ef = Turbine.filter(scada, Turbine.status_data_wec, "Main_Status",

'fault_case_1', False, 600, 600, 80)

print("=============================================================")

print("----------Training for detection of specific faults----------")

print("=============================================================")

print("=============================================================", "\n")

# select the faults to include.

faults = [ff, ef, gf]

# label and split into train, test and balanced training data

xtrain, xtest, ytrain, ytest, xbaltrain, ybaltrain = \

Turbine.get_test_train_data(features, faults, nf)

# labels for confusion matrix

labels = ['no-fault', 'feeding fault', 'excitation fault', 'generator fault']

print("========================================================")

print("------Building models using balanced training data------")

print("========================================================")

# set the parameter space (class_weight is None for the balanced training data)

parameter_space_bal = {

'kernel': ['linear', 'rbf', 'poly'], 'gamma': ['auto', 1e-3, 1e-4],

'C': [0.01, .1, 1, 10, 100, 1000], 'class_weight': [None]}

# train and test svm

clf_bal, bgg_bal = winfault.svm_class_and_score(

xbaltrain, ybaltrain, xtest, ytest, labels,

parameter_space=parameter_space_bal, bagged=True, score='recall_weighted',

search_type=GridSearchCV)

print("==========================================================")

print("------Building models using imbalanced training data------")

print("==========================================================")

# set the parameter space (class_weight is None for the balanced training data)

parameter_space = {

{0: 0.01}, {1: 1}, {1: 2}, {1: 10}, {1: 50}, 'balanced']}

# train and test svm

clf, bgg = winfault.svm_class_and_score(

xtrain, ytrain, xtest, ytest, labels,

parameter_space=parameter_space, bagged=True, score='recall_weighted',

search_type=RandomizedSearchCV)

print("============================================================")

print("----------Training for detection of general faults----------")

print("============================================================")

print("============================================================", "\n\n")

# need to change this to the original way it was done!!!

# af = np.append(ff, ef)

# af = np.append(af, gf)

# xtrain, xtest, ytrain, ytest, xbaltrain, ybaltrain = \

# Turbine.get_test_train_data(features, [af], nf)

# # labels for confusion matrix

# labels = ['no-fault', 'fault']

# print("========================================================")

# print("------Building models using balanced training data------")

# print("========================================================")

# set the parameter space (class_weight is None for the balanced training data)

# parameter_space_bal = {

# 'kernel': ['linear', 'rbf', 'poly'], 'gamma': ['auto', 1e-3, 1e-4],

# 'C': [0.01, .1, 1, 10, 100, 1000], 'class_weight': [None]}

# train and test svm

# clf_bal, bgg_bal = winfault.svm_class_and_score(

# xbaltrain, ybaltrain, xtest, ytest, labels,

# parameter_space=parameter_space_bal, bagged=True, score='recall_weighted',

# search_type=GridSearchCV)

# print("==========================================================")

# print("------Building models using imbalanced training data------")

# print("==========================================================")

# set the parameter space (class_weight is None for the balanced training data)

# parameter_space = {

# 'kernel': ['linear', 'rbf', 'poly'], 'gamma': ['auto', 1e-3, 1e-4],

# 'C': [0.01, .1, 1, 10, 100, 1000],

# 'class_weight': [

# {0: 0.01}, {1: 1}, {1: 2}, {1: 10}, {1: 50}, 'balanced']}

# # train and test svm

# clf, bgg = winfault.svm_class_and_score(

# xtrain, ytrain, xtest, ytest, labels,

# parameter_space=parameter_space, bagged=True, score='recall_weighted',

# search_type=RandomizedSearchCV)