added missing plotting module

mhcflurry/plotting.py

+# Copyright (c) 2015-2016. Mount Sinai School of Medicine
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import seaborn
+from matplotlib import pyplot
+from matplotlib import rc
+import numpy as np
+
+def plot_nsamples_vs_metrics_with_imputation(
+        results_df,
+        base_filename,
+        metrics=["auc", "f1", "tau"],
+        titles={
+            "tau": "Kendall's $\\tau$",
+            "auc": "AUC",
+            "f1": "$F_1$ score",
+        },
+        figsize=(6.5, 3.5),
+        groupby_columns=["num_samples", "impute"],
+        dpi=None):
+
+    pyplot.figure(figsize=figsize)
+    seaborn.set_style("whitegrid")
+
+    for (j, score_name) in enumerate(metrics):
+        ax = pyplot.subplot2grid((1, 4), (0, j))
+        groups = results_df.groupby(groupby_columns)
+        groups_score = groups[score_name].mean().to_frame().reset_index()
+        groups_score["std_error"] = \
+            groups[score_name].std().to_frame().reset_index()[score_name]
+
+        for impute in [True, False]:
+            sub = groups_score[groups_score.impute == impute]
+            color = seaborn.get_color_cycle()[0] if impute else seaborn.get_color_cycle()[1]
+            pyplot.errorbar(
+                x=sub.num_samples.values,
+                y=sub[score_name].values,
+                yerr=sub.std_error.values,
+                label=("with" if impute else "without") + " imputation",
+                color=color)
+        if j == 1:
+            pyplot.xlabel("Training set size")
+        pyplot.xscale("log")
+        pyplot.title(titles[score_name])
+
+        if score_name == "auc":
+            pyplot.ylim(ymin=0.5, ymax=1.0)
+        if score_name == "f1":
+            pyplot.ylim(ymin=0, ymax=1)
+        if score_name == "tau":
+            pyplot.ylim(ymin=0, ymax=0.6)
+            pyplot.yticks(np.arange(0, 0.61, 0.15))
+
+    pyplot.legend(
+        bbox_to_anchor=(1.1, 1),
+        loc=2,
+        borderaxespad=0.,
+        fancybox=True,
+        frameon=True,
+        fontsize="small")
+
+    pyplot.tight_layout()
+
+    if dpi:
+        rc("savefig", dpi=dpi)
+
+    # Put the legend out of the figure
+    image_filename = base_filename + ".png"
+    print("Writing PNG to %s" % image_filename)
+    pyplot.savefig(image_filename)
+
+    pdf_filename = base_filename + ".pdf"
+    print("Writing PDF to %s" % pdf_filename)
+    pyplot.savefig(pdf_filename)