def test_make_function(self):
result = make_data(os.path.join(os.path.dirname(__file__), "test_data",
"test_format_data.csv"),
target_col=["height"],
test_length=3,
relevant_cols=["temp", "precip"])
self.assertEqual(len(result.feats), 4)
self.assertEqual(len(result.targs), 4)
def train_function(model_type: str, params:Dict):
"""
Function to train a Model(TimeSeriesModel) or da_rnn. Will return the trained model
model_type str: Type of the model (for now) must be da_rnn or
:params dict: Dictionary containing all the parameters needed to run the model
"""
dataset_params = params["dataset_params"]
if model_type == "da_rnn":
from flood_forecast.da_rnn.train_da import da_rnn, train
from flood_forecast.preprocessing.preprocess_da_rnn import make_data
preprocessed_data = make_data(params["dataset_params"]["training_path"], params["dataset_params"]["target_col"], params["dataset_params"]["forecast_length"])
config, model = da_rnn(preprocessed_data, len(dataset_params["target_col"]))
# All train functions return trained_model
trained_model = train(model, preprocessed_data, config)
elif model_type == "PyTorch":
trained_model = PyTorchForecast(
params["model_name"],
dataset_params["training_path"],
dataset_params["validation_path"],
dataset_params["test_path"],
params)
train_transformer_style(trained_model, params["training_params"], params["forward_params"])
params["inference_params"]["dataset_params"]["scaling"] = scaler_dict[dataset_params["scaler"]]
test_acc = evaluate_model(
trained_model,
model_type,
params["dataset_params"]["target_col"],
params["metrics"],
params["inference_params"],
{})
wandb.run.summary["test_accuracy"] = test_acc[0]
df_train_and_test = test_acc[1]
forecast_start_idx = test_acc[2]
df_prediction_samples = test_acc[3]
inverse_mae = 1 / (
df_train_and_test.loc[forecast_start_idx:, "preds"] -
df_train_and_test.loc[forecast_start_idx:, params["dataset_params"]["target_col"][0]]).abs()
pred_std = df_prediction_samples.std(axis=1)
average_prediction_sharpe = (inverse_mae / pred_std).mean()
wandb.log({'average_prediction_sharpe': average_prediction_sharpe})
# Log plots
test_plot = plot_df_test_with_confidence_interval(
df_train_and_test,
df_prediction_samples,
forecast_start_idx,
params,
ci=95,
alpha=0.25)
wandb.log({"test_plot": test_plot})
test_plot_all = go.Figure()
for relevant_col in params["dataset_params"]["relevant_cols"]:
test_plot_all.add_trace(go.Scatter(x=df_train_and_test.index, y=df_train_and_test[relevant_col], name=relevant_col))
wandb.log({"test_plot_all": test_plot_all})
else:
raise Exception("Please supply valid model type for forecasting")
return trained_model
def setUp(self):
self.preprocessed_data = self.preprocessed_data = make_data(
os.path.join(os.path.dirname(__file__), "test_init",
"keag_small.csv"), ["cfs"], 72)
def test_make_function(self):
result = make_data(
os.path.join(os.path.dirname(__file__), "test_data",
"test_format_data.csv"), ["height"], 3)
self.assertEqual(len(result.feats), 1)
self.assertEqual(len(result.targs), 1)
def train_function(model_type: str, params: Dict):
"""Function to train a Model(TimeSeriesModel) or da_rnn. Will return the trained model
:param model_type: Type of the model. In almost all cases this will be 'PyTorch'
:type model_type: str
:param params: Dictionary containing all the parameters needed to run the model
:type Dict:
"""
dataset_params = params["dataset_params"]
if model_type == "da_rnn":
from flood_forecast.da_rnn.train_da import da_rnn, train
from flood_forecast.preprocessing.preprocess_da_rnn import make_data
preprocessed_data = make_data(
params["dataset_params"]["training_path"],
params["dataset_params"]["target_col"],
params["dataset_params"]["forecast_length"])
config, model = da_rnn(preprocessed_data,
len(dataset_params["target_col"]))
# All train functions return trained_model
trained_model = train(model, preprocessed_data, config)
elif model_type == "PyTorch":
trained_model = PyTorchForecast(params["model_name"],
dataset_params["training_path"],
dataset_params["validation_path"],
dataset_params["test_path"], params)
takes_target = False
if "takes_target" in trained_model.params:
takes_target = trained_model.params["takes_target"]
train_transformer_style(model=trained_model,
training_params=params["training_params"],
takes_target=takes_target,
forward_params=params["forward_params"])
# To do delete
if "scaler" in dataset_params:
if "scaler_params" in dataset_params:
params["inference_params"]["dataset_params"][
"scaling"] = scaling_function({},
dataset_params)["scaling"]
else:
params["inference_params"]["dataset_params"][
"scaling"] = scaling_function({},
dataset_params)["scaling"]
params["inference_params"]["dataset_params"].pop(
'scaler_params', None)
test_acc = evaluate_model(trained_model, model_type,
params["dataset_params"]["target_col"],
params["metrics"],
params["inference_params"], {})
wandb.run.summary["test_accuracy"] = test_acc[0]
df_train_and_test = test_acc[1]
forecast_start_idx = test_acc[2]
df_prediction_samples = test_acc[3]
mae = (df_train_and_test.loc[forecast_start_idx:, "preds"] -
df_train_and_test.loc[forecast_start_idx:,
params["dataset_params"]["target_col"][0]]
).abs()
inverse_mae = 1 / mae
i = 0
for df in df_prediction_samples:
pred_std = df.std(axis=1)
average_prediction_sharpe = (inverse_mae / pred_std).mean()
wandb.log({
'average_prediction_sharpe' + str(i):
average_prediction_sharpe
})
i += 1
df_train_and_test.to_csv("temp_preds.csv")
# Log plots now
if "probabilistic" in params["inference_params"]:
test_plot = plot_df_test_with_probabilistic_confidence_interval(
df_train_and_test,
forecast_start_idx,
params,
)
elif len(df_prediction_samples) > 0:
for thing in zip(df_prediction_samples,
params["dataset_params"]["target_col"]):
thing[0].to_csv(thing[1] + ".csv")
test_plot = plot_df_test_with_confidence_interval(
df_train_and_test,
thing[0],
forecast_start_idx,
params,
targ_col=thing[1],
ci=95,
alpha=0.25)
wandb.log({"test_plot_" + thing[1]: test_plot})
else:
pd.options.plotting.backend = "plotly"
t = params["dataset_params"]["target_col"][0]
test_plot = df_train_and_test[[t, "preds"]].plot()
wandb.log({"test_plot_" + t: test_plot})
print("Now plotting final plots")
test_plot_all = go.Figure()
for relevant_col in params["dataset_params"]["relevant_cols"]:
test_plot_all.add_trace(
go.Scatter(x=df_train_and_test.index,
y=df_train_and_test[relevant_col],
name=relevant_col))
wandb.log({"test_plot_all": test_plot_all})
else:
raise Exception("Please supply valid model type for forecasting")
return trained_model
def train_function(model_type: str, params: Dict) -> PyTorchForecast:
"""Function to train a Model(TimeSeriesModel) or da_rnn. Will return the trained model
:param model_type: Type of the model. In almost all cases this will be 'PyTorch'
:type model_type: str
:param params: Dictionary containing all the parameters needed to run the model
:type Dict:
:return: A trained model
.. code-block:: python
with open("model_config.json") as f:
params_dict = json.load(f)
train_function("PyTorch", params_dict)
...
For information on what this params_dict should include see `Confluence pages <https://flow-forecast.atlassian.net/wiki/spaces/FF/pages/92864513/Getting+Started>`_ on training models.
"""
dataset_params = params["dataset_params"]
if model_type == "da_rnn":
from flood_forecast.da_rnn.train_da import da_rnn, train
from flood_forecast.preprocessing.preprocess_da_rnn import make_data
preprocessed_data = make_data(
params["dataset_params"]["training_path"],
params["dataset_params"]["target_col"],
params["dataset_params"]["forecast_length"])
config, model = da_rnn(preprocessed_data,
len(dataset_params["target_col"]))
# All train functions return trained_model
trained_model = train(model, preprocessed_data, config)
elif model_type == "PyTorch":
dataset_params["batch_size"] = params["training_params"]["batch_size"]
trained_model = PyTorchForecast(params["model_name"],
dataset_params["training_path"],
dataset_params["validation_path"],
dataset_params["test_path"], params)
class2 = False if trained_model.params["dataset_params"][
"class"] != "GeneralClassificationLoader" else True
takes_target = False
if "takes_target" in trained_model.params:
takes_target = trained_model.params["takes_target"]
if "inference_params" in trained_model.params:
if "dataset_params" not in trained_model.params[
"inference_params"]:
print("Using generic dataset params")
trained_model.params["inference_params"][
"dataset_params"] = trained_model.params[
"dataset_params"].copy()
del trained_model.params["inference_params"]["dataset_params"][
"class"]
# noqa: F501
trained_model.params["inference_params"]["dataset_params"][
"interpolate_param"] = trained_model.params[
"inference_params"]["dataset_params"].pop(
"interpolate")
trained_model.params["inference_params"]["dataset_params"][
"scaling"] = trained_model.params["inference_params"][
"dataset_params"].pop("scaler")
if "feature_param" in trained_model.params["dataset_params"]:
trained_model.params["inference_params"]["dataset_params"][
"feature_params"] = trained_model.params[
"inference_params"]["dataset_params"].pop(
"feature_param")
delete_params = [
"num_workers", "pin_memory", "train_start", "train_end",
"valid_start", "valid_end", "test_start", "test_end",
"training_path", "validation_path", "test_path",
"batch_size"
]
for param in delete_params:
if param in trained_model.params["inference_params"][
"dataset_params"]:
del trained_model.params["inference_params"][
"dataset_params"][param]
train_transformer_style(model=trained_model,
training_params=params["training_params"],
takes_target=takes_target,
forward_params={},
class2=class2)
if "scaler" in dataset_params and "inference_params" in params:
if "scaler_params" in dataset_params:
params["inference_params"]["dataset_params"][
"scaling"] = scaling_function({},
dataset_params)["scaling"]
else:
params["inference_params"]["dataset_params"][
"scaling"] = scaling_function({},
dataset_params)["scaling"]
params["inference_params"]["dataset_params"].pop(
'scaler_params', None)
# TODO Move to other func
if params["dataset_params"]["class"] != "GeneralClassificationLoader":
handle_model_evaluation1(trained_model, params, model_type)
else:
raise Exception(
"Please supply valid model type for forecasting or classification")
return trained_model