flowa: (V1.2.3)
Python Machine Learning, Image Generation, Decision Trees, Label Encoders, and more!
# Linux/macOS
python3 pip install -U flowa
# Windows
py -3 -m pip install -U flowa
from flowa import (
Encoder,
Tree,
Dataset,
read_csv,
convert
)
classifier: Tree = Tree()
encoder: Encoder = Encoder()
dataset: str = convert(Dataset.get_music_data())
csv: object = read_csv(dataset)
dataframe: object = encoder.df(csv, 'gender')
X_matrix: object = dataframe.drop('genre', axis=1).values
y_column: object = encoder(dataframe['genre'].values)
classifier.fit(X_matrix, y_column)
age, gender = encoder.new(30, 'female')
fix: list = encoder.fix(age, gender)
prediction: list[int] = classifier.predict(fix)
print(encoder.inverse(prediction))
#>>> ['Pop']
Image generation:
model: ImageModel[object] = ImageModel()
image: ImageModel[str] = model.generate(
prompt="a cat", model="pixart", width=512, height=512, logo=False
).save("some-file.png")
#>>> flowa.types.Image
String Dataset to dataframe conversion:
from flowa import (
Dataset,
read_csv,
convert
)
dataset: Dataset = Dataset.get_play_tennis()
converted_dataset: str = convert(dataset)
csv: Dataset = read_csv(converted_dataset)
print(csv)
#>>> Outlook Temperature Humidity Wind Play Tennis
#>>> 0 Overcast Mild Normal Weak Yes
#>>> 1 Sunny Mild Normal Weak Yes
#>>> ... [2 rows not shown]