wide_preprocessor = WidePreprocessor(wide_cols=wide_cols, crossed_cols=crossed_cols) X_wide = wide_preprocessor.fit_transform(df) tab_preprocessor = TabPreprocessor( embed_cols=cat_embed_cols, # type: ignore[arg-type] continuous_cols=continuous_cols, already_standard=already_standard, ) X_tab = tab_preprocessor.fit_transform(df) text_processor = TextPreprocessor(word_vectors_path=word_vectors_path, text_col=text_col) X_text = text_processor.fit_transform(df) image_processor = ImagePreprocessor(img_col=img_col, img_path=img_path) X_images = image_processor.fit_transform(df) wide = Wide(wide_dim=np.unique(X_wide).shape[0], pred_dim=1) deepdense = TabMlp( mlp_hidden_dims=[64, 32], mlp_dropout=[0.2, 0.2], column_idx=tab_preprocessor.column_idx, embed_input=tab_preprocessor.embeddings_input, continuous_cols=continuous_cols, ) # # To use TabResnet as the deepdense component simply: # deepdense = TabResnet( # blocks_dims=[64, 32], # dropout=0.2, # column_idx=tab_preprocessor.column_idx,
import numpy as np import pandas as pd import pytest from pytorch_widedeep.preprocessing import ImagePreprocessor df = pd.DataFrame({'galaxies': ['galaxy1.png', 'galaxy2.png']}) img_col = 'galaxies' imd_dir = 'images' processor = ImagePreprocessor() X_imgs = processor.fit_transform(df, img_col, img_path=imd_dir) ############################################################################### # There is not much to test here, since I only resize. ############################################################################### def test_sizes(): img_width = X_imgs.shape[1] img_height = X_imgs.shape[2] assert np.all((img_width==processor.width, img_height==processor.height))
import numpy as np import pandas as pd import os from pytorch_widedeep.preprocessing import ImagePreprocessor full_path = os.path.realpath(__file__) path = os.path.split(full_path)[0] df = pd.DataFrame({"galaxies": ["galaxy1.png", "galaxy2.png"]}) img_col = "galaxies" imd_dir = os.path.join(path, "images") processor = ImagePreprocessor(img_col=img_col, img_path=imd_dir) X_imgs = processor.fit_transform(df) ############################################################################### # There is not much to test here, since I only resize. ############################################################################### def test_sizes(): img_width = X_imgs.shape[1] img_height = X_imgs.shape[2] assert np.all( (img_width == processor.width, img_height == processor.height))
def test_notfittederror(): processor = ImagePreprocessor(img_col=img_col, img_path=imd_dir) with pytest.raises(NotFittedError): processor.transform(df)