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Fixed diffusion confidence interval plot

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This commit is contained in:
Victor Mylle
2024-02-18 16:01:18 +01:00
parent 7bd0476085
commit bd250a664b
3 changed files with 87 additions and 67 deletions
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119
src/policies/policy_executer.py
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@@ -8,7 +8,8 @@ import pandas as pd
import datetime
from tqdm import tqdm
from src.utils.imbalance_price_calculator import ImbalancePriceCalculator
import time
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
### import functions ###
@@ -16,7 +17,7 @@ from src.trainers.quantile_trainer import auto_regressive as quantile_auto_regre
from src.trainers.diffusion_trainer import sample_diffusion
from src.utils.clearml import ClearMLHelper
# argparse to parse task id and model type
### Arguments ###
parser = argparse.ArgumentParser()
parser.add_argument('--task_id', type=str, default=None)
parser.add_argument('--model_type', type=str, default=None)
@@ -27,6 +28,7 @@ assert args.task_id is not None, "Please specify task id"
assert args.model_type is not None, "Please specify model type"
assert args.model_name is not None, "Please specify model name"
### Baseline Policy ###
battery = Battery(2, 1)
baseline_policy = BaselinePolicy(battery, data_path="")
@@ -163,20 +165,17 @@ def get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc,
return predicted_nrv_profits_cycles, baseline_profits_cycles, _charge_thresholds, _discharge_thresholds
def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: callable):
penalties = [0, 10, 50, 150, 300, 500, 600, 800, 1000, 1500, 2000, 2500]
penalties = [0, 50, 250, 500, 1000, 1500]
predicted_nrv_profits_cycles = {i: [0, 0] for i in penalties}
baseline_profits_cycles = {i: [0, 0] for i in penalties}
charge_thresholds = {}
discharge_thresholds = {}
# get all dates in test set
dates = baseline_policy.test_data["DateTime"].dt.date.unique()
# dates back to datetime
dates = pd.to_datetime(dates)
for date in tqdm(dates[:10]):
for date in tqdm(dates):
try:
new_predicted_nrv_profits_cycles, new_baseline_profits_cycles, new_charge_thresholds, new_discharge_thresholds = get_next_day_profits_for_date(model, data_processor, test_loader, date, ipc, predict_NRV, penalties)
@@ -191,8 +190,7 @@ def next_day_test_set(model, data_processor, test_loader, ipc, predict_NRV: call
baseline_profits_cycles[penalty][1] += new_baseline_profits_cycles[penalty][1]
except Exception as e:
# print(f"Error for date {date}")
raise e
print(f"Error for date {date}")
return predicted_nrv_profits_cycles, baseline_profits_cycles, charge_thresholds, discharge_thresholds
@@ -222,9 +220,6 @@ def main():
# the charge_thresholds is a dictionary with date as key. The values of the dictionary is another dictionary with keys as penalties and values as the charge thresholds
# create density plot that shows a density plot of the charge thresholds for each penalty (use seaborn displot) (One plot with a different color for each penalty)
import seaborn as sns
import matplotlib.pyplot as plt
charge_thresholds_for_penalty = {}
for d in charge_thresholds.values():
for penalty, thresholds in d.items():
@@ -239,47 +234,73 @@ def main():
discharge_thresholds_for_penalty[penalty] = []
discharge_thresholds_for_penalty[penalty].extend(thresholds)
def plot_threshold_distribution(thresholds: dict, title: str):
data_to_plot = []
for penalty, values in thresholds.items():
for value in values:
data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
df = pd.DataFrame(data_to_plot)
palette = sns.color_palette("bright", len(thresholds.keys()))
fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
plt.title('Density of Charge Thresholds by Penalty')
plt.xlabel('Charge Threshold')
plt.ylabel('Density')
plt.legend(title='Penalty')
task.get_logger().report_matplotlib_figure(
"Policy Results",
title,
iteration=0,
figure=fig
)
plt.close()
### Plot charge thresholds distribution ###
data_to_plot = []
for penalty, values in charge_thresholds_for_penalty.items():
for value in values:
data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
df = pd.DataFrame(data_to_plot)
print(df.head())
palette = sns.color_palette("bright", len(charge_thresholds.keys()))
fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
plt.title('Density of Charge Thresholds by Penalty')
plt.xlabel('Charge Threshold')
plt.ylabel('Density')
plt.legend(title='Penalty')
task.get_logger().report_matplotlib_figure(
"Policy Results",
"Charge Thresholds",
iteration=0,
figure=fig
)
plt.close()
plot_threshold_distribution(charge_thresholds_for_penalty, "Charge Thresholds")
### Plot discharge thresholds distribution ###
data_to_plot = []
for penalty, values in discharge_thresholds_for_penalty.items():
for value in values:
data_to_plot.append({'Penalty': penalty, 'Value': value.item()})
df = pd.DataFrame(data_to_plot)
palette = sns.color_palette("bright", len(discharge_thresholds.keys()))
fig = sns.displot(data=df, x="Value", hue="Penalty", kind="kde", palette=palette)
plt.title('Density of Charge Thresholds by Penalty')
plt.xlabel('Charge Threshold')
plt.ylabel('Density')
plt.legend(title='Penalty')
task.get_logger().report_matplotlib_figure(
"Policy Results",
"Discharge Thresholds",
iteration=0,
figure=fig
)
plt.close()
plot_threshold_distribution(discharge_thresholds_for_penalty, "Discharge Thresholds")
def plot_thresholds_per_day(thresholds: dict, title: str):
# plot mean charge threshold per day (per penalty (other color))
data_to_plot = []
for date, values in thresholds.items():
for penalty, value in values.items():
mean_val = value.mean().item()
std_val = value.std().item() # Calculate standard deviation
data_to_plot.append({'Date': date, 'Penalty': penalty, 'Mean': mean_val, 'StdDev': std_val})
print(f"Date: {date}, Penalty: {penalty}, Mean: {mean_val}, StdDev: {std_val}")
df = pd.DataFrame(data_to_plot)
df["Date"] = pd.to_datetime(df["Date"])
fig = px.line(
df,
x="Date",
y="Mean",
color="Penalty",
title=title,
labels={"Mean": "Threshold", "Date": "Date"},
markers=True, # Adds markers to the lines
hover_data=["Penalty"], # Adds additional hover information
)
fig.update_layout(
width=1000, # Set the width of the figure
height=600, # Set the height of the figure
title_x=0.5, # Center the title horizontally
)
task.get_logger().report_plotly(
"Thresholds per Day",
title,
iteration=0,
figure=fig
)
### Plot mean charge thresholds per day ###
plot_thresholds_per_day(charge_thresholds, "Mean Charge Thresholds per Day")
### Plot mean discharge thresholds per day ###
plot_thresholds_per_day(discharge_thresholds, "Mean Discharge Thresholds per Day")
# create dataframe with columns "name", "penalty", "profit", "cycles"

25
src/trainers/diffusion_trainer.py
View File

@@ -19,8 +19,6 @@ def sample_diffusion(model: DiffusionModel, n: int, inputs: torch.tensor, noise_
alpha = 1. - beta
alpha_hat = torch.cumprod(alpha, dim=0)
# inputs: (num_features) -> (batch_size, num_features)
# inputs: (time_steps, num_features) -> (batch_size, time_steps, num_features)
if len(inputs.shape) == 2:
inputs = inputs.repeat(n, 1)
elif len(inputs.shape) == 3:
@@ -42,17 +40,17 @@ def sample_diffusion(model: DiffusionModel, n: int, inputs: torch.tensor, noise_
noise = torch.zeros_like(x)
x = 1/torch.sqrt(_alpha) * (x-((1-_alpha) / (torch.sqrt(1 - _alpha_hat))) * predicted_noise) + torch.sqrt(_beta) * noise
x = torch.clamp(x, -1.0, 1.0)
return x
class DiffusionTrainer:
def __init__(self, model: nn.Module, data_processor: DataProcessor, device: torch.device):
self.model = model
self.device = device
self.noise_steps = 20
self.beta_start = 1e-4
self.noise_steps = 30
self.beta_start = 0.0001
self.beta_end = 0.02
self.ts_length = 96
@@ -183,17 +181,18 @@ class DiffusionTrainer:
with torch.no_grad():
samples = self.sample(self.model, 100, features).cpu().numpy()
ci_99_upper = np.quantile(samples, 0.99, axis=0)
ci_99_lower = np.quantile(samples, 0.01, axis=0)
ci_99_upper = np.quantile(samples, 0.995, axis=0)
ci_99_lower = np.quantile(samples, 0.005, axis=0)
ci_95_upper = np.quantile(samples, 0.95, axis=0)
ci_95_lower = np.quantile(samples, 0.05, axis=0)
ci_95_upper = np.quantile(samples, 0.975, axis=0)
ci_95_lower = np.quantile(samples, 0.025, axis=0)
ci_90_upper = np.quantile(samples, 0.9, axis=0)
ci_90_lower = np.quantile(samples, 0.1, axis=0)
ci_90_upper = np.quantile(samples, 0.95, axis=0)
ci_90_lower = np.quantile(samples, 0.05, axis=0)
ci_50_lower = np.quantile(samples, 0.25, axis=0)
ci_50_upper = np.quantile(samples, 0.75, axis=0)
ci_50_upper = np.quantile(samples, 0.5, axis=0)
ci_50_lower = np.quantile(samples, 0.5, axis=0)
sns.set_theme()
time_steps = np.arange(0, 96)

10
src/training_scripts/diffusion_training.py
View File

@@ -38,7 +38,7 @@ data_config.NOMINAL_NET_POSITION = True
data_config = task.connect(data_config, name="data_features")
data_processor = DataProcessor(data_config, path="", lstm=False)
data_processor.set_batch_size(128)
data_processor.set_batch_size(64)
data_processor.set_full_day_skip(True)
inputDim = data_processor.get_input_size()
@@ -47,15 +47,15 @@ print("Input dim: ", inputDim)
model_parameters = {
"epochs": 5000,
"learning_rate": 0.0001,
"hidden_sizes": [512, 512, 512],
"time_dim": 64,
"hidden_sizes": [128, 128],
"time_dim": 8,
}
model_parameters = task.connect(model_parameters, name="model_parameters")
#### Model ####
# model = SimpleDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[1], time_dim=model_parameters["time_dim"])
model = GRUDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[2], time_dim=model_parameters["time_dim"], gru_hidden_size=256)
model = SimpleDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[1], time_dim=model_parameters["time_dim"])
# model = GRUDiffusionModel(96, model_parameters["hidden_sizes"], other_inputs_dim=inputDim[2], time_dim=model_parameters["time_dim"], gru_hidden_size=128)
print("Starting training ...")