Mercurial > repos > bgruening > sklearn_nn_classifier

--- a/keras_train_and_eval.py	Wed Aug 09 11:32:07 2023 +0000
+++ b/keras_train_and_eval.py	Mon Oct 02 08:24:41 2023 +0000
@@ -188,6 +188,7 @@
     infile1,
     infile2,
     outfile_result,
+    outfile_history=None,
     outfile_object=None,
     outfile_y_true=None,
     outfile_y_preds=None,
@@ -215,6 +216,9 @@
     outfile_result : str
         File path to save the results, either cv_results or test result.

+    outfile_history : str, optional
+        File path to save the training history.
+
     outfile_object : str, optional
         File path to save searchCV object.

@@ -253,9 +257,7 @@
     swapping = params["experiment_schemes"]["hyperparams_swapping"]
     swap_params = _eval_swap_params(swapping)
     estimator.set_params(**swap_params)
-
     estimator_params = estimator.get_params()
-
     # store read dataframe object
     loaded_df = {}

@@ -448,12 +450,20 @@
     # train and eval
     if hasattr(estimator, "config") and hasattr(estimator, "model_type"):
         if exp_scheme == "train_val_test":
-            estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
+            history = estimator.fit(X_train, y_train, validation_data=(X_val, y_val))
         else:
-            estimator.fit(X_train, y_train, validation_data=(X_test, y_test))
+            history = estimator.fit(X_train, y_train, validation_data=(X_test, y_test))
     else:
-        estimator.fit(X_train, y_train)
-
+        history = estimator.fit(X_train, y_train)
+    if "callbacks" in estimator_params:
+        for cb in estimator_params["callbacks"]:
+            if cb["callback_selection"]["callback_type"] == "CSVLogger":
+                hist_df = pd.DataFrame(history.history)
+                hist_df["epoch"] = np.arange(1, estimator_params["epochs"] + 1)
+                epo_col = hist_df.pop('epoch')
+                hist_df.insert(0, 'epoch', epo_col)
+                hist_df.to_csv(path_or_buf=outfile_history, sep="\t", header=True, index=False)
+                break
     if isinstance(estimator, KerasGBatchClassifier):
         scores = {}
         steps = estimator.prediction_steps
@@ -526,6 +536,7 @@
     aparser.add_argument("-X", "--infile1", dest="infile1")
     aparser.add_argument("-y", "--infile2", dest="infile2")
     aparser.add_argument("-O", "--outfile_result", dest="outfile_result")
+    aparser.add_argument("-hi", "--outfile_history", dest="outfile_history")
     aparser.add_argument("-o", "--outfile_object", dest="outfile_object")
     aparser.add_argument("-l", "--outfile_y_true", dest="outfile_y_true")
     aparser.add_argument("-p", "--outfile_y_preds", dest="outfile_y_preds")
@@ -542,6 +553,7 @@
         args.infile1,
         args.infile2,
         args.outfile_result,
+        outfile_history=args.outfile_history,
         outfile_object=args.outfile_object,
         outfile_y_true=args.outfile_y_true,
         outfile_y_preds=args.outfile_y_preds,
--- a/ml_visualization_ex.py	Wed Aug 09 11:32:07 2023 +0000
+++ b/ml_visualization_ex.py	Mon Oct 02 08:24:41 2023 +0000
@@ -15,6 +15,7 @@
 from sklearn.metrics import (
     auc,
     average_precision_score,
+    confusion_matrix,
     precision_recall_curve,
     roc_curve,
 )
@@ -258,6 +259,30 @@
     os.rename(os.path.join(folder, "output.svg"), os.path.join(folder, "output"))


+def get_dataframe(file_path, plot_selection, header_name, column_name):
+    header = "infer" if plot_selection[header_name] else None
+    column_option = plot_selection[column_name]["selected_column_selector_option"]
+    if column_option in [
+        "by_index_number",
+        "all_but_by_index_number",
+        "by_header_name",
+        "all_but_by_header_name",
+    ]:
+        col = plot_selection[column_name]["col1"]
+    else:
+        col = None
+    _, input_df = read_columns(
+        file_path,
+        c=col,
+        c_option=column_option,
+        return_df=True,
+        sep="\t",
+        header=header,
+        parse_dates=True,
+    )
+    return input_df
+
+
 def main(
     inputs,
     infile_estimator=None,
@@ -271,6 +296,10 @@
     targets=None,
     fasta_path=None,
     model_config=None,
+    true_labels=None,
+    predicted_labels=None,
+    plot_color=None,
+    title=None,
 ):
     """
     Parameter
@@ -311,6 +340,18 @@

     model_config : str, default is None
         File path to dataset containing JSON config for neural networks
+
+    true_labels : str, default is None
+        File path to dataset containing true labels
+
+    predicted_labels : str, default is None
+        File path to dataset containing true predicted labels
+
+    plot_color : str, default is None
+        Color of the confusion matrix heatmap
+
+    title : str, default is None
+        Title of the confusion matrix heatmap
     """
     warnings.simplefilter("ignore")

@@ -534,6 +575,36 @@

         return 0

+    elif plot_type == "classification_confusion_matrix":
+        plot_selection = params["plotting_selection"]
+        input_true = get_dataframe(
+            true_labels, plot_selection, "header_true", "column_selector_options_true"
+        )
+        header_predicted = "infer" if plot_selection["header_predicted"] else None
+        input_predicted = pd.read_csv(
+            predicted_labels, sep="\t", parse_dates=True, header=header_predicted
+        )
+        true_classes = input_true.iloc[:, -1].copy()
+        predicted_classes = input_predicted.iloc[:, -1].copy()
+        axis_labels = list(set(true_classes))
+        c_matrix = confusion_matrix(true_classes, predicted_classes)
+        fig, ax = plt.subplots(figsize=(7, 7))
+        im = plt.imshow(c_matrix, cmap=plot_color)
+        for i in range(len(c_matrix)):
+            for j in range(len(c_matrix)):
+                ax.text(j, i, c_matrix[i, j], ha="center", va="center", color="k")
+        ax.set_ylabel("True class labels")
+        ax.set_xlabel("Predicted class labels")
+        ax.set_title(title)
+        ax.set_xticks(axis_labels)
+        ax.set_yticks(axis_labels)
+        fig.colorbar(im, ax=ax)
+        fig.tight_layout()
+        plt.savefig("output.png", dpi=125)
+        os.rename("output.png", "output")
+
+        return 0
+
     # save pdf file to disk
     # fig.write_image("image.pdf", format='pdf')
     # fig.write_image("image.pdf", format='pdf', width=340*2, height=226*2)
@@ -553,6 +624,10 @@
     aparser.add_argument("-t", "--targets", dest="targets")
     aparser.add_argument("-f", "--fasta_path", dest="fasta_path")
     aparser.add_argument("-c", "--model_config", dest="model_config")
+    aparser.add_argument("-tl", "--true_labels", dest="true_labels")
+    aparser.add_argument("-pl", "--predicted_labels", dest="predicted_labels")
+    aparser.add_argument("-pc", "--plot_color", dest="plot_color")
+    aparser.add_argument("-pt", "--title", dest="title")
     args = aparser.parse_args()

     main(
@@ -568,4 +643,8 @@
         targets=args.targets,
         fasta_path=args.fasta_path,
         model_config=args.model_config,
+        true_labels=args.true_labels,
+        predicted_labels=args.predicted_labels,
+        plot_color=args.plot_color,
+        title=args.title,
     )
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