Comment out pf2 across ranks figure

pf2/figures/figure2.py

@@ -1,60 +1,60 @@
-"""Figure 2: R2X Curve"""
+# """Figure 2: R2X Curve"""
 
-import gc
+# import gc
 
-import numpy as np
-import pandas as pd
+# import numpy as np
+# import pandas as pd
 
-from pf2.data_import import convert_to_patients, import_data, import_meta
-from pf2.figures.common import getSetup
-from pf2.predict import predict_mortality
-from pf2.tensor import pf2
+# from pf2.data_import import convert_to_patients, import_data, import_meta
+# from pf2.figures.common import getSetup
+# from pf2.predict import predict_mortality
+# from pf2.tensor import pf2
 
 
-def makeFigure():
-    meta = import_meta()
-    data = import_data()
-    conversions = convert_to_patients(data)
+# def makeFigure():
+#     meta = import_meta()
+#     data = import_data()
+#     conversions = convert_to_patients(data)
 
-    axs, fig = getSetup((6, 6), (2, 1))
+#     axs, fig = getSetup((6, 6), (2, 1))
 
-    ranks = np.arange(5, 65, 5)
-    r2xs = pd.Series(0, dtype=float, index=ranks)
-    accuracies = pd.Series(0, dtype=float, index=ranks)
-    for rank in ranks:
-        fac, r2x = pf2(data, rank, do_embedding=False)
-        patient_factor = pd.DataFrame(
-            fac.uns["Pf2_A"],
-            index=conversions,
-            columns=np.arange(fac.uns["Pf2_A"].shape[1]) + 1,
-        )
-        if meta.shape[0] != patient_factor.shape[0]:
-            meta = meta.loc[patient_factor.index, :]
+#     ranks = np.arange(5, 65, 5)
+#     r2xs = pd.Series(0, dtype=float, index=ranks)
+#     accuracies = pd.Series(0, dtype=float, index=ranks)
+#     for rank in ranks:
+#         fac, r2x = pf2(data, rank, do_embedding=False)
+#         patient_factor = pd.DataFrame(
+#             fac.uns["Pf2_A"],
+#             index=conversions,
+#             columns=np.arange(fac.uns["Pf2_A"].shape[1]) + 1,
+#         )
+#         if meta.shape[0] != patient_factor.shape[0]:
+#             meta = meta.loc[patient_factor.index, :]
 
-        acc, _ = predict_mortality(patient_factor, meta)
-        r2xs.loc[rank] = r2x
-        accuracies.loc[rank] = acc
-        r2xs.to_csv("/home/jchin/BAL-Pf2/output/r2x_v_rank_no_ig.csv")
-        accuracies.to_csv("/home/jchin/BAL-Pf2/output/acc_v_rank_no_ig.csv")
+#         acc, _ = predict_mortality(patient_factor, meta)
+#         r2xs.loc[rank] = r2x
+#         accuracies.loc[rank] = acc
+#         r2xs.to_csv("/home/jchin/BAL-Pf2/output/r2x_v_rank_no_ig.csv")
+#         accuracies.to_csv("/home/jchin/BAL-Pf2/output/acc_v_rank_no_ig.csv")
 
-        gc.collect()
+#         gc.collect()
 
-    # R2X Plots
+#     # R2X Plots
 
-    axs[0].plot(r2xs.index.astype(int), r2xs)
-    axs[0].set_xticks(r2xs.index.astype(int))
-    axs[0].grid(True)
+#     axs[0].plot(r2xs.index.astype(int), r2xs)
+#     axs[0].set_xticks(r2xs.index.astype(int))
+#     axs[0].grid(True)
 
-    axs[0].set_ylabel("R2X")
-    axs[0].set_xlabel("Rank")
+#     axs[0].set_ylabel("R2X")
+#     axs[0].set_xlabel("Rank")
 
-    # Accuracy Plots
+#     # Accuracy Plots
 
-    axs[1].plot(accuracies.index, accuracies)
-    axs[1].set_xticks(accuracies.index.astype(int))
-    axs[1].grid(True)
+#     axs[1].plot(accuracies.index, accuracies)
+#     axs[1].set_xticks(accuracies.index.astype(int))
+#     axs[1].grid(True)
 
-    axs[1].set_ylabel("Accuracy")
-    axs[1].set_xlabel("Rank")
+#     axs[1].set_ylabel("Accuracy")
+#     axs[1].set_xlabel("Rank")
 
-    return fig
+#     return fig