Fixed diffusion confidence interval plot

src/policies/policy_executer.py

@@ -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"