def run(input_path, output_path, remvoe_columns):
"""
This functions removes specified column from input
"""
meta = {"Remove Columns": remvoe_columns}
proccesor = Process(meta)
df = ioutil.read_parquet(input_path)
result = proccesor.run(df)
ioutil.save_parquet(result, output_path)
def prepare_input():
df = ioutil.read_parquet("../dstest/outputs/stargan/")
results = []
for index in range(len(df)):
results.append([[0, 0, 0, 1, 0]])
df.insert(len(df.columns), "c", results, True)
print(df.columns)
ioutil.save_parquet(df, "outputs/stargan/model_input", True)
return df
def run(input_path, output_path, tensor_column):
"""
This functions removes specified column from input
"""
meta = {"Tensor Column": tensor_column}
proccesor = Process(meta)
df = ioutil.read_parquet(input_path)
result = proccesor.run(df)
#result = result[['image','Result']]
# ioutil.save_parquet(result, output_path, True)
ioutil.save_parquet1(result, output_path, True)
def run(input_path, meta_path, output_path, file_name, prob_col):
"""
read
"""
meta = {
"Category File Name": file_name,
"Probability Column Name": prob_col
}
proccesor = Process(meta_path, meta)
df = ioutil.read_parquet(input_path)
result = proccesor.run(df)
ioutil.save_parquet(result, output_path, True)
def run(input_path, output_path, image_column, target_column,
target_datauri_column, target_image_size):
"""
This functions read base64 encoded images from df. Transform to format required by model input.
"""
meta = {
"Image Column": image_column,
"Target Column": target_column,
"Target DataURI Column": target_datauri_column,
"Target Image Size": target_image_size
}
proccesor = PreProcess(meta)
df = ioutil.read_parquet(input_path)
result = proccesor.run(df)
ioutil.save_parquet(result, output_path)
def run(input_path, meta_path, output_path, file_name, prob_col,
append_category_column_to_output):
"""
read
"""
meta = {
CATEGORY_FILE_NAME_KEY: file_name,
PROBABILITY_COLUMN_NAME_KEY: prob_col,
APPEND_CATEGORY_COLUMN_TO_OUTPUT_KEY: append_category_column_to_output
}
proccesor = Process(meta_path, meta)
df = ioutil.read_parquet(input_path)
result = proccesor.run(df)
print(result)
ioutil.save_parquet(result, output_path, True)
#df = pd.DataFrame()
#df.insert(len(df.columns), 'x', batch_xs.tolist(), True)
columns = [f"x.{i}" for i in range(784)]
#columns = ['x']*784
df = pd.DataFrame(data=batch_xs, columns=columns, dtype=np.float64)
names = [
"fixed acidity", "volatile acidity", "citric acid", "residual sugar",
"chlorides", "free sulfur dioxide", "total sulfur dioxide", "density",
"pH", "sulphates", "alcohol"
]
data = [[7, 0.27, 0.36, 20.7, 0.045, 45, 170, 1.001, 3, 0.45, 8.8]]
df1 = pd.DataFrame(data=data, columns=names)
df = pd.concat([df, df1], axis=1)
#df.to_parquet("test.parquet")
#df.to_csv("test.csv")
return df
# python -m dstest.tensorflow.mnist_test
if __name__ == '__main__':
# df = prepare_input()
df = ioutil.read_parquet("../dstest/outputs/mnist/")
print(df.columns)
test_tensor(df)
out = test_builtin(df)
print(out.columns)
print(out)
def prepare_input():
df = ioutil.read_parquet("../dstest/outputs/mnist/")
print(df.columns)
return df
return result
def prepare_input():
df = ioutil.read_parquet("../dstest/outputs/stargan/")
results = []
for index in range(len(df)):
results.append([[0, 0, 0, 1, 0]])
df.insert(len(df.columns), "c", results, True)
print(df.columns)
ioutil.save_parquet(df, "outputs/stargan/model_input", True)
return df
def test(model_path):
df = prepare_input()
out = test_builtin(model_path, df)
ioutil.save_parquet(out, "outputs/stargan/model_output", True)
print(out.columns)
print(out)
print(out["0"].shape)
# python -m dstest.pytorch.stargan
if __name__ == '__main__':
# model_path = "model/stargan/"
# test(model_path)
out = ioutil.read_parquet("outputs/stargan/model_output")
print(out.columns)
#print(out["0"][0])