def load_data(dataset):
if dataset == 'test':
X, y = load_test()
sz = 230
elif dataset == 'uc1':
X, y = split_df(pd.read_pickle('..\\data\\df_uc1.pkl'),
index_column='run_id',
feature_columns=['fldPosition', 'fldCurrent'],
target_name='target')
# Length of timeseries for resampler and cnn
sz = 38
elif dataset == 'uc2':
X, y = split_df(pd.read_pickle('..\\data\\df_uc2.pkl'),
index_column='run_id',
feature_columns=['position', 'force'],
target_name='label')
# Length of timeseries for resampler and cnn
sz = 200
resampler = TimeSeriesResampler(sz=sz)
X = resampler.fit_transform(X, y)
y = np.array(y)
return X, y
from alpaca import Alpaca
from utils import to_time_series_dataset, to_dataset, split_df, TimeSeriesResampler
import time
import numpy as np
import pandas as pd
from sklearn.pipeline import Pipeline
max_sample = 20
for dataset in ['uc2']:
if dataset == 'uc1':
X, y = split_df(pd.read_pickle('..\\data\\df_uc1.pkl'),
index_column='run_id',
feature_columns=['fldPosition', 'fldCurrent'],
target_name='target')
y = np.array(y)
# Length of timeseries for resampler and cnn
sz = 38
# Number of channels for cnn
num_channels = len(X[0][0])
# Number of classes for cnn
num_classes = np.unique(y).shape[0]
if dataset == 'uc2':
X, y = split_df(pd.read_pickle('..\\data\\df_uc2.pkl'),
index_column='run_id',
feature_columns=['position', 'force'],
target_name='label')
y = np.array(y)
# Length of timeseries for resampler and cnn
sz = 200
# Number of channels for cnn
import utils
df_pickled_path = "./data.pkl"
data = utils.load_df_pickled(df_pickled_path)
train_data, valid_data, test_data = utils.split_df(data,
train_set_ratio=0.8,
test_set_ratio=0.1,
valid_set_ratio=0.1)
if __name__ == "__main__":
pass
print(f'The list different size image: {heigh_width_unique}')
# Train file
annot_df = pd.DataFrame.from_records(train_json['annotations'])
annot_df['weight'] = heigh_width_unique[0][1]
annot_df['height'] = heigh_width_unique[0][0]
annot_df['x'] = annot_df['bbox'].apply(lambda x: x[0])
annot_df['y'] = annot_df['bbox'].apply(lambda x: x[1])
annot_df['w'] = annot_df['bbox'].apply(lambda x: x[2])
annot_df['h'] = annot_df['bbox'].apply(lambda x: x[3])
display(annot_df.head())
# List label
categorie_df = pd.DataFrame.from_records(train_json['categories'])
display((categorie_df))
# List image in folder
train_image_path = glob(f'{args.train_image}/*.*')
test_image_path = glob(f'{args.test_image}/*.*')
print(
f'Number of train image: {len(train_image_path)}, test image: {len(test_image_path)}'
)
# Create fold
annot_pivot = split_df(annot_df)
display(annot_pivot.head())
#Train process
train_pr = Train_process()
train_pr.fit(annot_df, annot_pivot)