Jaime Durán pyjaime

## word_cloud_example.py
from wordcloud import WordCloud, ImageColorGenerator
from PIL import Image
from stop_words import get_stop_words

stopwords = get_stop_words('es')
mask = np.array(Image.open('img/logo/vox.png'))

wc = WordCloud(background_color="white", max_words=300, mask=mask,
               width=900, height=450, min_font_size=7, max_font_size=100,
               stopwords=stopwords, collocations=True, normalize_plurals=True,

## plotly_express_hbar_example.py
px.bar(
    cis_df, x="concern", y="problem", template="plotly_white",
    labels=dict(concern="% of respondents for whom the problem is Top 3", problem="Problem"),
    width=970, height=800, title="Main (3) concerns of spanish people [March 2019]",
    orientation='h'
)

## seaborn_table_heatmap_example.py
plt.figure(figsize=(8,11))
ax = sns.heatmap(df.T, annot=True, fmt='d', cmap='Blues', cbar=False, linecolor='white', linewidths=2)

ax.tick_params(labelbottom=True, labeltop=True, labelsize=13, labelcolor='black')
ax.set_title('\nOccurrences of topic-related key words on each electoral program\n\n',
             fontsize=16);

## seaborn_table_heatmap_example2.py
ax = sns.heatmap(df_rel.T.round(3), annot=True, fmt='g', cmap='Blues', linecolor='white', linewidths=1)

## pandas_barh_example.py
ax = df_rel.T.plot(kind='barh', figsize=(11,30), fontsize=13)

## image_data_bunch_from_df.py
data = ImageDataBunch.from_df(
  df=df_styles, path=pictures_dir, label_col='style', fn_col='new_filename',
  ds_tfms=get_transforms(), size=299, bs=48).normalize(imagenet_stats)

## cnn_fit_stage1.py
learner = cnn_learner(data, models.resnet50, metrics=[error_rate])
learner.fit_one_cycle(6)

## lr_finder.py
learner.lr_find()
learner.recorder.plot()

## cnn_fit_stage2.py
learner.unfreeze()
learner.fit_one_cycle(7, max_lr=slice(1e-6,3e-4))

## learner_predict.py
img = open_image('data/the_scream.jpg')
pred = learner.predict(img); pred
	from wordcloud import WordCloud, ImageColorGenerator
	from PIL import Image
	from stop_words import get_stop_words

	stopwords = get_stop_words('es')
	mask = np.array(Image.open('img/logo/vox.png'))

	wc = WordCloud(background_color="white", max_words=300, mask=mask,
	width=900, height=450, min_font_size=7, max_font_size=100,
	stopwords=stopwords, collocations=True, normalize_plurals=True,
	px.bar(
	cis_df, x="concern", y="problem", template="plotly_white",
	labels=dict(concern="% of respondents for whom the problem is Top 3", problem="Problem"),
	width=970, height=800, title="Main (3) concerns of spanish people [March 2019]",
	orientation='h'
	)
	plt.figure(figsize=(8,11))
	ax = sns.heatmap(df.T, annot=True, fmt='d', cmap='Blues', cbar=False, linecolor='white', linewidths=2)

	ax.tick_params(labelbottom=True, labeltop=True, labelsize=13, labelcolor='black')
	ax.set_title('\nOccurrences of topic-related key words on each electoral program\n\n',
	fontsize=16);
	data = ImageDataBunch.from_df(
	df=df_styles, path=pictures_dir, label_col='style', fn_col='new_filename',
	ds_tfms=get_transforms(), size=299, bs=48).normalize(imagenet_stats)
	learner = cnn_learner(data, models.resnet50, metrics=[error_rate])
	learner.fit_one_cycle(6)
	learner.unfreeze()
	learner.fit_one_cycle(7, max_lr=slice(1e-6,3e-4))
	img = open_image('data/the_scream.jpg')
	pred = learner.predict(img); pred