Barplot #2 works now

nmrcraft/analysis/plotting.py

@@ -275,52 +275,82 @@ def convert_to_labels(target_list):
     plt.close()
 
 
-def plot_metric_1(
+def plot_bar(
     data,
     title="Accuracy",
     filename="plots/accuracy.png",
     metric="accuracy",
     iterative_column="model",
     xdata="dataset_fraction",
 ):
-    if iterative_column == "target":
+    _, colors, _ = style_setup()
 
-        def convert_to_labels(target_list):
-            label_dict = {"metal": "M", "E_ligand": "E", "X3_ligand": "X3"}
-            return ", ".join([label_dict[i] for i in target_list])
+    def convert_to_labels(target_list):
+        label_dict = {"metal": "M", "E_ligand": "E", "X3_ligand": "X3"}
+        return ", ".join([label_dict[i] for i in target_list])
 
-        # Convert string representations of lists to actual lists
-        data["model_targets"] = data["model_targets"].apply(
-            lambda x: ast.literal_eval(x) if isinstance(x, str) else x
-        )
+    # Convert string representations of lists to actual lists
+    data["model_targets"] = data["model_targets"].apply(
+        lambda x: ast.literal_eval(x) if isinstance(x, str) else x
+    )
 
-        data["xlabel"] = data["model_targets"].apply(convert_to_labels)
-        print(data)
+    data["xlabel"] = data["model_targets"].apply(convert_to_labels)
 
-    for iterator in data[iterative_column].unique():
-        model_data = data[data[iterative_column] == iterator]
-        errors = [
-            model_data[metric + "_mean"].values
-            - model_data[metric + "_lb"].values,
-            model_data[metric + "_hb"].values
-            - model_data[metric + "_mean"].values,
-        ]
-        plt.errorbar(
-            model_data[xdata],
-            model_data[metric + "_mean"],
-            yerr=errors,
-            fmt="o",
-            label=model_data["target"],
+    # Aggregate the data to handle duplicates
+    aggregated_data = (
+        data.groupby(["xlabel", "target"])
+        .agg({metric + "_mean": "mean"})
+        .reset_index()
+    )
+    aggregated_data_lb = (
+        data.groupby(["xlabel", "target"])
+        .agg({metric + "_lb": "mean"})
+        .reset_index()
+    )
+    aggregated_data_hb = (
+        data.groupby(["xlabel", "target"])
+        .agg({metric + "_hb": "mean"})
+        .reset_index()
+    )
+
+    # Pivot the aggregated data
+    new_df = aggregated_data.pivot(
+        index="xlabel", columns="target", values=metric + "_mean"
+    )
+    new_lb = aggregated_data_lb.pivot(
+        index="xlabel", columns="target", values=metric + "_lb"
+    )
+    new_hb = aggregated_data_hb.pivot(
+        index="xlabel", columns="target", values=metric + "_hb"
+    )
+
+    fig, ax = plt.subplots()
+    width = 0.2  # width of the bar
+    x = np.arange(len(new_df.index))
+
+    # Plotting each column (target) as a separate group
+    for i, column in enumerate(new_df.columns):
+        ax.bar(
+            x + i * width,
+            new_df[column],
+            width,
+            color=colors[i],
+            label=column,
+            yerr=[
+                new_df[column] - new_lb[column],
+                new_hb[column] - new_df[column],
+            ],
             capsize=5,
         )
-    plt.legend()
-    plt.title(title)
-    plt.grid(True)
-    if iterative_column == "model":
-        plt.xlim(0, 1)
-        plt.ylim(0, 1.2)
-    plt.xlabel("Dataset Size")
-    plt.ylabel(metric)
+
+    ax.set_ylabel(f"{metric}")
+    ax.set_ylim(0, 1.2)
+    ax.set_xticks(x + width * (len(new_df.columns) - 1) / 2)
+    ax.set_xticklabels(new_df.index, rotation=45, ha="right")
+    ax.set_title(title)
+    ax.legend()
+    plt.grid(True, "major", "y", ls="--", lw=0.5, c="k", alpha=0.3)
+    fig.tight_layout()
     plt.savefig(filename)
     plt.close()
 

scripts/analysis/visulize_results.py

@@ -1,6 +1,6 @@
 import pandas as pd
 
-from nmrcraft.analysis.plotting import plot_metric, plot_metric_1
+from nmrcraft.analysis.plotting import plot_bar, plot_metric
 
 import_filename_base = "metrics/results_baselines.csv"
 import_filename_one = "metrics/results_one_target.csv"
@@ -39,34 +39,24 @@
 df = pd.concat([df_one, df_multi])
 full_df = df[df["dataset_fraction"] == 1]
 
-
-# models = full_df['model'].unique()
-# for model in models:
-#     sub_df = full_df[full_df["model"] == model]
-#     print(sub_df)
-#     plot_metric_1(sub_df, title=f"Accuracy for {model} Predictions", filename=f'plots/02_accuracy_{model}.png', metric="accuracy", iterative_column='target', xdata='xlabel')
-#     plot_metric_1(sub_df, title=f"F1-Score for {model} Predictions", filename=f'plots/02_f1-score_{model}.png', metric="f1", iterative_column='target', xdata='xlabel')
-
-nmr_only = full_df["nmr_only"].unique()
-for mode in nmr_only:
-    mode_df = full_df[full_df["nmr_only"] == mode]
-    models = mode_df["model"].unique()
-    for model in models:
-        sub_df = full_df[mode_df["model"] == model]
-        print(sub_df)
-        plot_metric_1(
-            sub_df,
-            title=f"Accuracy for {model} Predictions with onlyNMR = {mode}",
-            filename=f"plots/03_accuracy_{model}_NMR_{mode}.png",
-            metric="accuracy",
-            iterative_column="target",
-            xdata="xlabel",
-        )
-        plot_metric_1(
-            sub_df,
-            title=f"F1-Score for {model} Predictions with onlyNMR = {mode}",
-            filename=f"plots/03_f1-score_{model}_NMR_{mode}.png",
-            metric="f1",
-            iterative_column="target",
-            xdata="xlabel",
-        )
+true_df = full_df[full_df["nmr_only"]]
+models = true_df["model"].unique()
+for model in models:
+    sub_df = true_df[true_df["model"] == model]
+    print(sub_df)
+    plot_bar(
+        sub_df,
+        title=f"Accuracy for {model} Predictions",
+        filename=f"plots/02_accuracy_{model}.png",
+        metric="accuracy",
+        iterative_column="target",
+        xdata="xlabel",
+    )
+    plot_bar(
+        sub_df,
+        title=f"F1-Score for {model} Predictions",
+        filename=f"plots/02_f1-score_{model}.png",
+        metric="f1",
+        iterative_column="target",
+        xdata="xlabel",
+    )