Mercurial > repos > siwaa > wine_quality_train_eval

comparison toto_wine_quality_train_eval.py @ 10:b432386c0f1c draft

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planemo upload for repository https://forgemia.inra.fr/nathalie.rousse/use/-/tree/dnn/DNN/galaxy-tools/wine_quality_train_eval commit e7fd13c34ec074a7ebc246301b5a80069dcbcc3a-dirty
author siwaa
date Thu, 05 Dec 2024 15:47:56 +0000
parents 31d737992c63
children b5f69f836e03
comparison
equal deleted inserted replaced
9:31d737992c63 10:b432386c0f1c
96 96
97 # Import some packages 97 # Import some packages
98 import os 98 import os
99 import lightning.pytorch as pl 99 import lightning.pytorch as pl
100 import torchvision.transforms as T 100 import torchvision.transforms as T
101 from IPython.display import display, HTML 101 from IPython.display import display, HTML ##toto## HTML
102 from torch.utils.data import DataLoader, random_split 102 from torch.utils.data import DataLoader, random_split
103 from model_wine_lightning.modules.progressbar import CustomTrainProgressBar 103 from model_wine_lightning.modules.progressbar import CustomTrainProgressBar
104 from model_wine_lightning.modules.data_load import WineQualityDataset 104 from model_wine_lightning.modules.data_load import WineQualityDataset
105 from model_wine_lightning.modules.data_load import Normalize, ToTensor 105 from model_wine_lightning.modules.data_load import Normalize, ToTensor
106 from model_wine_lightning.modules.model import LitRegression 106 from model_wine_lightning.modules.model import LitRegression
184 error_msg += message + " " 184 error_msg += message + " "
185 185
186 datasets = WineQualityDataset(dataset_filepath) 186 datasets = WineQualityDataset(dataset_filepath)
187 print("datasets:") 187 print("datasets:")
188 #display(datasets.data.head(5).style.format("{0:.2f}")) 188 #display(datasets.data.head(5).style.format("{0:.2f}"))
189 display(datasets.data.head(5)) 189 ##toto##display(datasets.data.head(5))
190 print('Missing Data : ',datasets.data.isna().sum().sum(), 190 print('Missing Data : ',datasets.data.isna().sum().sum(),
191 ' Shape is : ', datasets.data.shape) 191 ' Shape is : ', datasets.data.shape)
192 192
193 # ## Step 3 - Preparing the data 193 # ## Step 3 - Preparing the data
194 print("\n"+HEAD,"# ## Step 3 - Preparing the data\n") 194 print("\n"+HEAD,"# ## Step 3 - Preparing the data\n")
207 "min_json":N.min_json, "max_json":N.max_json} 207 "min_json":N.min_json, "max_json":N.max_json}
208 transforms = T.Compose([N, ToTensor()]) 208 transforms = T.Compose([N, ToTensor()])
209 dataset = WineQualityDataset(dataset_filepath, transform=transforms) 209 dataset = WineQualityDataset(dataset_filepath, transform=transforms)
210 210
211 print("Before normalization :") 211 print("Before normalization :")
212 display(datasets[:]["features"]) 212 ##toto##display(datasets[:]["features"])
213 print("After normalization :") 213 print("After normalization :")
214 display(dataset[:]["features"]) 214 ##toto##display(dataset[:]["features"])
215 215
216 # ### 3.2 - Split data 216 # ### 3.2 - Split data
217 print("\n"+HEAD,"# ### 3.2 - Split data\n") 217 print("\n"+HEAD,"# ### 3.2 - Split data\n")
218 # We will use 80% of the data for training and 20% for validation. 218 # We will use 80% of the data for training and 20% for validation.
219 # x will be the features data of the analysis and y the target (quality) 219 # x will be the features data of the analysis and y the target (quality)