class VisualizePredictions(Task):
dir_path = luigi.Parameter(default="data")
prediction_visualization_path = luigi.Parameter(default="data/VisualizePredictions/predictions.png")
requires = Requires()
source_data_testset = Requirement(MakeTestSet)
source_predictions = Requirement(EvaluateModel)
def output(self):
return LocalTarget(self.prediction_visualization_path)
def run(self):
test_ddf = self.input()["source_data_testset"].read_dask()
y_predicted = np.load(self.input()["source_predictions"].path, allow_pickle=True)
fig = visualizepredictions(y_predicted, test_ddf)
self.output().makedirs()
# https://mattiacinelli.com/tutorial-on-luigi-part-3-pipeline-input-and-output/
fig.savefig(self.output().path)
class EvaluateModel(Task):
dir_path = luigi.Parameter(default="data")
predicted_values_path = luigi.Parameter(default="data/EvaluateModel/predicted.npy")
requires = Requires()
source_data_testset = Requirement(MakeTestSet)
source_model = Requirement(TrainModel)
def output(self):
return LocalTarget(self.predicted_values_path)
def run(self):
test_ddf = self.input()["source_data_testset"].read_dask()
with open(self.input()["source_model"].fn, "rb") as file:
model = pickle.load(file)
y_predicted = evaluate_model(model, test_ddf)
self.output().makedirs()
np.save(self.output().path, y_predicted)
class MakeTestSet(MakeDatasets):
source_data = Requirement(TransformData)
def run(self):
ddf = self.input()["source_data"].read_dask()
_, test = ddf.random_split([
1 - self.TEST_AS_PERCENT_OF_DATASET,
self.TEST_AS_PERCENT_OF_DATASET],
random_state=123
)
self.output().write_dask(test, compression='gzip')
class VisualizeFeatureImportance(Task):
dir_path = luigi.Parameter(default="data")
importance_path = luigi.Parameter(default="data/VisualizeFeatureSignificance/featureimportance.png")
requires = Requires()
source_data_testset = Requirement(MakeTestSet)
source_model = Requirement(TrainModel)
def output(self):
return LocalTarget(self.importance_path)
def run(self):
test_ddf = self.input()["source_data_testset"].read_dask()
with open(self.input()["source_model"].fn, "rb") as file:
model = pickle.load(file)
fig = visualizefeaturesignificance(model, test_ddf)
self.output().makedirs()
# https://mattiacinelli.com/tutorial-on-luigi-part-3-pipeline-input-and-output/
fig.savefig(self.output().path)
class TransformData(Task):
dir_path = luigi.Parameter(default="data")
requires = Requires()
source_data = Requirement(ExtractFeatures)
output = TargetOutput(
file_pattern="{task.dir_path}/{task.__class__.__name__}/",
target_class=ParquetTarget,
glob="*.parquet",
)
def run(self):
ddf = self.input()["source_data"].read_dask()
ddf = transform_dataframe(ddf)
self.output().write_dask(ddf, compression='gzip')
class CleanData(Task):
dir_path = luigi.Parameter(default="data")
requires = Requires()
source_data = Requirement(DownloadData)
output = TargetOutput(
file_pattern="{task.dir_path}/{task.__class__.__name__}/",
target_class=ParquetTarget,
glob="*.parquet",
)
def run(self):
ddf = self.input()["source_data"].read_dask()
ddf = clean_datasets(ddf)
self.output().write_dask(ddf, compression='gzip')
class TrainModel(Task):
dir_path = luigi.Parameter(default="data")
model_path = luigi.Parameter(default="data/Model/model.pckl")
requires = Requires()
source_data = Requirement(MakeTrainingSet)
def output(self):
return LocalTarget(self.model_path)
def run(self):
train_ddf = self.input()["source_data"].read_dask()
model = train_model(train_ddf)
self.output().makedirs()
with self.output().temporary_path() as temp_output_path:
print(temp_output_path)
pickle.dump(model, open(temp_output_path, 'wb'))