def __eval_data(self):
if self.type == 'train':
sampler = acs.AccousticSampler(
'D:/PYTHON_WORKSPACES/Kaggles/EarthquakePrediction/LANL_Earthquake/data/train_data_new'
)
# sampler = acs.AccousticSampler('D:/PYTHON_WORKSPACES/Kaggles/EarthquakePrediction/LANL_Earthquake/data/test_data', data_type='test')
sampler.fit()
self.data_df = sampler.get()
self.y_train = self.data_df['time_to_failure']
elif self.type == 'test':
sampler = acs.AccousticSampler(
'D:/PYTHON_WORKSPACES/Kaggles/EarthquakePrediction/LANL_Earthquake/data/test_data',
data_type='test')
sampler.fit()
self.data_df = sampler.get()
self.y_seg = self.data_df['segment_id']
# Run Multi Layer Perceptron
from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
import accoustic_sampler as acs
import data_formatter as dtFrm
from model_holder import ModelHolder, load_model
import numpy as np
from sklearn.neural_network.multilayer_perceptron import MLPRegressor
model_name = 'mlp_regression.model';
sampler = acs.AccousticSampler('D:/PYTHON_WORKSPACES/Kaggles/EarthquakePrediction/LANL_Earthquake/data/train_data_new')
sampler.fit()
data_df = sampler.get()
formatter = dtFrm.LANLDataFormatter(data_df=data_df, data_type='train', doTransform=True, doScale=True, cols_to_keep=50)
data_df = formatter.transform()
most_dependent_columns = formatter.getMostImpCols()
# data_df = data_df.drop(['acc_max','acc_min','chg_acc_max','chg_acc_min'],axis=1)
# Splitting data into test_random_forest and train
# train_set, test_set = train_test_split(data_df, test_size=0.2, random_state=np.random.randint(1, 1000))
# Separate output from inputs
y_train = data_df['time_to_failure']
x_train_seg = data_df['segment_id']
x_train = data_df.drop(['time_to_failure', 'segment_id'], axis=1)
y_train = np.around(y_train.values, decimals=2)