Merge branch 'main' into docs/54-documentation

.gitignore

@@ -1,8 +1,8 @@
 mlruns/
 scratch/
 dataset/
-data/
 plots/
+data/
 
 docs/source
 

Dockerfile

@@ -3,17 +3,31 @@ FROM archlinux
 # Install python, poetry and looks stuff from arch system repos
 RUN pacman-key --init
 RUN pacman-key --populate
-RUN pacman -Syu python python-poetry ranger neovim eza git tree zsh openssh which neofetch github-cli make --noconfirm
+RUN pacman -Syu python python-poetry ranger neovim eza git tree zsh openssh which neofetch github-cli make binutils gcc pkg-config fakeroot debugedit --noconfirm
 
 # Set working directory and copy over config files and install python packages
 RUN mkdir -p /home/steve
+
+# Get Arial font
+RUN cd /home/steve
+WORKDIR /home/steve
+RUN git clone https://aur.archlinux.org/ttf-ms-fonts.git
+RUN mv ttf-ms-fonts/* .
+RUN chmod 777 .
+RUN runuser -unobody makepkg
+RUN pacman -U ttf-ms-fonts*.pkg.tar.zst --noconfirm
+RUN rm -r ./*
+
+# Get Project
 ADD https://api.github.com/repos/mlederbauer/NMRcraft/git/refs/heads/main version.json
 RUN git clone https://github.com/mlederbauer/NMRcraft.git /home/steve/NMRcraft
 WORKDIR /home/steve/NMRcraft
 RUN echo "🚀 Creating virtual environment using pyenv and poetry"
 RUN poetry install
 RUN poetry run pre-commit install
 
+
+
 # Quality of Life stuff
 ADD https://api.github.com/repos/tiaguinho-code/Archpy_dots/git/refs/heads/main version.json
 RUN git clone https://github.com/tiaguinho-code/Archpy_dots /home/steve/Archpy_dots

nmrcraft/analysis/plotting.py

@@ -1,3 +1,8 @@
+"""Functions to plot."""
+
+import os
+
+import matplotlib.patches as mpatches
 import matplotlib.pyplot as plt
 import numpy as np
 from cycler import cycler
@@ -15,11 +20,13 @@ def style_setup():
     plt.rcParams["text.latex.preamble"] = r"\usepackage{sansmathfonts}"
     plt.rcParams["axes.prop_cycle"] = cycler(color=colors)
 
-    # Use the first color from the custom color cycle
-    first_color = plt.rcParams["axes.prop_cycle"].by_key()["color"][0]
+    all_colors = [
+        plt.rcParams["axes.prop_cycle"].by_key()["color"][i]
+        for i in range(len(colors))
+    ]
     plt.rcParams["text.usetex"] = False
 
-    return cmap, colors, first_color
+    return cmap, colors, all_colors
 
 
 def plot_predicted_vs_ground_truth(
@@ -33,7 +40,8 @@ def plot_predicted_vs_ground_truth(
     Returns:
     None
     """
-    _, _, first_color = style_setup()
+    _, _, colors = style_setup()
+    first_color = colors[0]
     # Creating the plot
     plt.figure(figsize=(10, 8))
     plt.scatter(y_test, y_pred, color=first_color, edgecolor="k", alpha=0.6)
@@ -85,7 +93,7 @@ def plot_predicted_vs_ground_truth_density(
 
 
 def plot_confusion_matrix(
-    cm, classes, title, path, full=True, columns_set=False
+    cm_list, y_labels, model_name, dataset_size, folder_path: str = "plots/"
 ):
     """
     Plots the confusion matrix.
@@ -98,45 +106,27 @@ def plot_confusion_matrix(
     Returns:
     None
     """
-    _, _, _ = style_setup()
-    if full:  # Plot one big cm
+    if not os.path.exists(folder_path):
+        os.makedirs(folder_path)
+    # _, _, _ = style_setup()
+    for target in y_labels:
+        file_path = os.path.join(
+            folder_path,
+            f"ConfusionMatrix_{model_name}_{dataset_size}_{target}.png",
+        )
+        cm = cm_list[target]
+        classes = y_labels[target]
         plt.figure(figsize=(10, 8))
         plt.imshow(cm, interpolation="nearest", cmap=plt.cm.Blues)
-        plt.title(title)
+        plt.title(f"{target} Confusion Matrix")
         plt.colorbar()
         tick_marks = np.arange(len(classes))
-        plt.xticks(tick_marks, classes, rotation=45)
+        plt.xticks(tick_marks, classes, rotation=90)
         plt.yticks(tick_marks, classes)
         plt.tight_layout()
         plt.ylabel("True label")
         plt.xlabel("Predicted label")
-        plt.savefig(path)
-        plt.close()
-
-    elif not full:  # Plot many small cms of each target
-        cms = []
-        for columns in columns_set:  # Make list of confusion matrices
-            cms.append(
-                cm[
-                    slice(columns[0], columns[-1] + 1),
-                    slice(columns[0], columns[-1] + 1),
-                ]
-            )
-        fig, axs = plt.subplots(nrows=len(cms), figsize=(10, 8 * len(cms)))
-        for i, sub_cm in enumerate(cms):
-            sub_classes = classes[
-                slice(columns_set[i][0], columns_set[i][-1] + 1)
-            ]
-            axs[i].imshow(sub_cm, interpolation="nearest", cmap=plt.cm.Blues)
-            axs[i].set_title(f"Confusion Matrix {i+1}")
-            tick_marks = np.arange(len(sub_classes))
-            axs[i].set_xticks(tick_marks)
-            axs[i].set_xticklabels(sub_classes, rotation=45)
-            axs[i].set_yticks(tick_marks)
-            axs[i].set_yticklabels(sub_classes)
-            plt.tight_layout()
-        print(cm)
-        plt.savefig(path)
+        plt.savefig(file_path)
         plt.close()
 
 
@@ -167,3 +157,75 @@ def plot_roc_curve(fpr, tpr, roc_auc, title, path):
     plt.legend(loc="lower right")
     plt.savefig(path)
     plt.close()
+
+
+def plot_with_without_ligands_bar(df):
+    categories = df["target"].unique()
+    _, _, colors = style_setup()
+    first_color = colors[0]
+    second_color = colors[1]
+
+    # Extract data
+
+    x_pos = np.arange(len(categories))
+    bar_width = 0.35
+
+    # Initialize plot
+    fig, ax = plt.subplots()
+
+    # Loop through each category and plot bars
+    for i, category in enumerate(categories):
+        subset = df[df["target"] == category]
+
+        # Means and error bars
+        means = subset["accuracy_mean"].values
+        errors = [
+            subset["accuracy_mean"].values
+            - subset["accuracy_lower_bd"].values,
+            subset["accuracy_upper_bd"].values
+            - subset["accuracy_mean"].values,
+        ]
+
+        # Bar locations for the group
+        bar_positions = x_pos[i] + np.array([-bar_width / 2, bar_width / 2])
+
+        # Determine bar colors based on 'nmr_tensor_input_only' field
+        bar_colors = [
+            first_color if x else second_color
+            for x in subset["nmr_tensor_input_only"]
+        ]
+
+        # Plotting the bars
+        ax.bar(
+            bar_positions,
+            means,
+            yerr=np.array(errors),
+            color=bar_colors,
+            align="center",
+            ecolor="black",
+            capsize=5,
+            width=bar_width,
+        )
+
+    # Labeling and aesthetics
+    ax.set_ylabel("Accuracy / %")
+    ax.set_xlabel("Target(s)")
+    ax.set_xticks(x_pos)
+    ax.set_xticklabels(categories)
+    ax.set_title("Accuracy Measurements with Error Bars")
+
+    handles = [
+        mpatches.Patch(color=first_color, label="With Ligand Info"),
+        mpatches.Patch(color=second_color, label="Without Ligand Info"),
+    ]
+    ax.legend(handles=handles, loc="best", fontsize=20)
+    plt.tight_layout()
+    plt.savefig("plots/exp3_incorporate_ligand_info.png")
+    print("Saved to plots/exp3_incorporate_ligand_info.png")
+
+
+if __name__ == "main":
+    import pandas as pd
+
+    df = pd.read_csv("dataset/path_to_results.csv")
+    plot_with_without_ligands_bar(df)

nmrcraft/data/data_utils.py

@@ -0,0 +1,76 @@
+"""Load and preprocess data."""
+
+import os
+
+import pandas as pd
+from datasets import load_dataset
+
+
+class DatasetLoadError(FileNotFoundError):
+    """Exeption raised when the Dataloader could not find data/dataset.csv,
+    even after trying to generate it from huggingface"""
+
+    def __init__(self, t):
+        super().__init__(f"Could not load raw Dataset '{t}'")
+
+
+class InvalidTargetError(ValueError):
+    """Exception raised when the specified model name is not found."""
+
+    def __init__(self, t):
+        super().__init__(f"Invalid target '{t}'")
+
+
+def filename_to_ligands(dataset: pd.DataFrame):
+    """
+    Extract ligands from the filename and add as columns to the dataset.
+    Assumes that filenames are structured in a specific way that can be parsed into ligands.
+    """
+    filename_parts = dataset["file_name"].str.split("_", expand=True)
+    dataset["metal"] = filename_parts.get(0)
+    dataset["geometry"] = filename_parts.get(1)
+    dataset["E_ligand"] = filename_parts.get(2)
+    dataset["X1_ligand"] = filename_parts.get(3)
+    dataset["X2_ligand"] = filename_parts.get(4)
+    dataset["X3_ligand"] = filename_parts.get(5)
+    dataset["X4_ligand"] = filename_parts.get(6)
+    dataset["L_ligand"] = filename_parts.get(7).fillna(
+        "none"
+    )  # Fill missing L_ligand with 'none'
+    return dataset
+
+
+def load_dummy_dataset_locally(datset_path: str = "tests/data.csv"):
+    dataset = pd.read_csv(datset_path)
+    return dataset
+
+
+def load_dataset_from_hf(
+    dataset_name: str = "NMRcraft/nmrcraft", data_files: str = "all_no_nan.csv"
+):
+    """Load the dataset.
+
+    This function loads the dataset using the specified dataset name and data files.
+    It assumes that you have logged into the Hugging Face CLI prior to calling this function.
+
+    Args:
+        dataset_name (str, optional): The name of the dataset. Defaults to "NMRcraft/nmrcraft".
+        data_files (str, optional): The name of the data file. Defaults to 'all_no_nan.csv'.
+
+    Returns:
+        pandas.DataFrame: The loaded dataset as a pandas DataFrame.
+    """
+    # Create data dir if needed
+    if not os.path.isdir("dataset"):
+        os.mkdir("dataset")
+    # Check if hf dataset is already downloaded, else download it and then load it
+    if not os.path.isfile("dataset/dataset.csv"):
+        dataset = load_dataset(dataset_name, data_files=data_files)[
+            "train"
+        ].to_pandas()
+        dataset.to_csv("dataset/dataset.csv")
+    if os.path.isfile("dataset/dataset.csv"):
+        dataset = pd.read_csv("dataset/dataset.csv")
+    elif not os.path.isfile("dataset/dataset.csv"):
+        raise DatasetLoadError(FileNotFoundError)
+    return dataset