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objective.py
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from mlmicrophysics.models import DenseNeuralNetwork
from mlmicrophysics.data import subset_data_files_by_date, assemble_data_files
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler, MinMaxScaler, MaxAbsScaler, RobustScaler, OneHotEncoder
from sklearn.metrics import confusion_matrix, accuracy_score, mean_absolute_error, mean_squared_error
from mlmicrophysics.metrics import heidke_skill_score, peirce_skill_score, hellinger_distance, root_mean_squared_error, r2_corr
import argparse
import yaml
from os.path import join, exists
import os
from datetime import datetime
import logging
import optuna
from aimlutils.hyper_opt.utils import trial_suggest_loader
from aimlutils.hyper_opt.base_objective import *
from aimlutils.hyper_opt.utils import KerasPruningCallback
logger = logging.getLogger(__name__)
scalers = {"MinMaxScaler": MinMaxScaler,
"MaxAbsScaler": MaxAbsScaler,
"StandardScaler": StandardScaler,
"RobustScaler": RobustScaler}
class_metrics = {"accuracy": accuracy_score,
"heidke": heidke_skill_score,
"peirce": peirce_skill_score}
reg_metrics = {"rmse": root_mean_squared_error,
"mae": mean_absolute_error,
"r2": r2_corr,
"hellinger": hellinger_distance,
"mse": mean_squared_error}
def leaky(x):
return tf.nn.leaky_relu(x, alpha=0.01)
def ranked_probability_score(y_true_discrete, y_pred_discrete):
y_pred_cumulative = np.cumsum(y_pred_discrete)
y_true_cumulative = np.cumsum(y_true_discrete)
return np.mean((y_pred_cumulative - y_true_cumulative) ** 2) / float(y_pred_discrete.shape[1] - 1)
def objective(trial, config):
# Get list of hyperparameters from the config
hyperparameters = config["optuna"]["parameters"]
# Now update some hyperparameters via custom rules
class_activation = trial_suggest_loader(trial, hyperparameters["class_activation"])
class_hidden_layers = trial_suggest_loader(trial, hyperparameters["class_hidden_layers"])
class_hidden_neurons = trial_suggest_loader(trial, hyperparameters["class_hidden_neurons"])
class_lr = trial_suggest_loader(trial, hyperparameters["class_lr"])
class_l2_weight = trial_suggest_loader(trial, hyperparameters["class_l2_weight"])
reg_activation = trial_suggest_loader(trial, hyperparameters["reg_activation"])
reg_hidden_layers = trial_suggest_loader(trial, hyperparameters["reg_hidden_layers"])
reg_hidden_neurons = trial_suggest_loader(trial, hyperparameters["reg_hidden_neurons"])
reg_lr = trial_suggest_loader(trial, hyperparameters["reg_lr"])
reg_l2_weight = trial_suggest_loader(trial, hyperparameters["reg_l2_weight"])
data_path = config["data_path"]
out_path = config["out_path"]
input_cols = config["input_cols"]
output_cols = config["output_cols"]
input_transforms = config["input_transforms"]
output_transforms = config["output_transforms"]
np.random.seed(config["random_seed"])
input_scaler = scalers[config["input_scaler"]]()
subsample = config["subsample"]
if not exists(out_path):
os.makedirs(out_path)
train_files, val_files, test_files = subset_data_files_by_date(data_path, **config["subset_data"])
print("Loading training data")
scaled_input_train, \
labels_train, \
transformed_out_train, \
scaled_out_train, \
output_scalers, \
meta_train = assemble_data_files(train_files, input_cols, output_cols, input_transforms,
output_transforms, input_scaler, subsample=subsample)
print("Loading testing data")
scaled_input_test, \
labels_test, \
transformed_out_test, \
scaled_out_test, \
output_scalers_test, \
meta_test = assemble_data_files(test_files, input_cols, output_cols, input_transforms,
output_transforms, input_scaler, output_scalers=output_scalers,
train=False, subsample=subsample)
input_scaler_df = pd.DataFrame({"mean": input_scaler.mean_, "scale": input_scaler.scale_},
index=input_cols)
out_scales_list = []
for var in output_scalers.keys():
for out_class in output_scalers[var].keys():
print(var, out_class)
if output_scalers[var][out_class] is not None:
out_scales_list.append(pd.DataFrame({"mean": output_scalers[var][out_class].mean_,
"scale": output_scalers[var][out_class].scale_},
index=[var + "_" + str(out_class)]))
out_scales_df = pd.concat(out_scales_list)
out_scales_df.to_csv(join(out_path, "output_scale_values.csv"),
index_label="output")
beginning = datetime.now()
print(f"BEGINNING: {beginning}")
classifiers = dict()
regressors = dict()
reg_index = []
for output_col in output_cols:
for label in list(output_transforms[output_col].keys()):
if label != 0:
reg_index.append(output_col + f"_{label:d}")
test_prediction_values = np.zeros((scaled_out_test.shape[0], len(reg_index)))
test_prediction_labels = np.zeros(scaled_out_test.shape)
l = 0
score = 0
for o, output_col in enumerate(output_cols):
print("Train Classifer ", output_col)
classifiers[output_col] = DenseNeuralNetwork(hidden_layers=class_hidden_layers,
hidden_neurons=class_hidden_neurons,
lr=class_lr,
l2_weight=class_l2_weight,
activation=class_activation,
**config["classifier_networks"])
hist = classifiers[output_col].fit(scaled_input_train,
labels_train[output_col],
scaled_input_test,
labels_test[output_col],
callbacks=[KerasPruningCallback(trial, "val_loss")])
logger.info(f"finished with classifier {output_col}")
regressors[output_col] = dict()
print("Evaluate Classifier", output_col)
test_prediction_labels[:, o] = classifiers[output_col].predict(scaled_input_test)
true = OneHotEncoder(sparse=False).fit_transform(labels_test[output_col].to_numpy().reshape(-1, 1))
pred = OneHotEncoder(sparse=False).fit_transform(pd.DataFrame(test_prediction_labels[:, o]))
score += ranked_probability_score(true, pred)
for label in list(output_transforms[output_col].keys()):
if label != 0:
print("Train Regressor ", output_col, label)
regressors[output_col][label] = DenseNeuralNetwork(hidden_layers=reg_hidden_layers,
hidden_neurons=reg_hidden_neurons,
lr=reg_lr,
l2_weight=reg_l2_weight,
activation = reg_activation,
**config["regressor_networks"])
hist = regressors[output_col][label].fit(scaled_input_train.loc[labels_train[output_col] == label],
scaled_out_train.loc[labels_train[output_col] == label, output_col],
scaled_input_test.loc[labels_test[output_col] == label],
scaled_out_test.loc[labels_test[output_col] == label, output_col],
callbacks=[KerasPruningCallback(trial, "val_loss")])
logger.info(f"finished with regressor {output_col}")
if label > 0:
out_label = "pos"
else:
out_label = "neg"
print("Test Regressor", output_col, label)
test_prediction_values[:, l] = output_scalers[output_col][label].inverse_transform(regressors[output_col][label].predict(scaled_input_test))
score += mean_squared_error(transformed_out_test.loc[labels_test[output_col] == label, output_col],
test_prediction_values[labels_test[output_col] == label, l])
l += 1
print(f"Running the model took: {datetime.now() - beginning}")
return score
class Objective(BaseObjective):
def __init__(self, study, config, metric = "val_loss", device = "cpu"):
# Initialize the base class
BaseObjective.__init__(self, study, config, metric, device)
def train(self, trial, conf):
result = objective(trial, conf)
results_dictionary = {
"val_loss": result
}
return results_dictionary
if __name__ == "__main__":
main()
print("Starting script...")
# parse arguments from config/yaml file
parser = argparse.ArgumentParser()
parser.add_argument("config", help="Path to config file")
args = parser.parse_args()
with open(args.config) as config_file:
config = yaml.load(config_file, Loader=yaml.FullLoader)
study = optuna.create_study(direction=config["direction"],
pruner=optuna.pruners.MedianPruner())
study.optimize(objective, config, n_trials=config["n_trials"])
pruned_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.PRUNED]
complete_trials = [t for t in study.trials if t.state == optuna.trial.TrialState.COMPLETE]
print("Study statistics: ")
print(" Number of finished trials: ", len(study.trials))
print(" Number of pruned trials: ", len(pruned_trials))
print(" Number of complete trials: ", len(complete_trials))
print("Best trial:")
trial = study.best_trial
print(" Value: ", trial.value)
print(" Params: ")
for key, value in trial.params.items():
print(" {}: {}".format(key, value))