Added outputs to regression example

FunctionLab · Dec 9, 2018 · 4eabae5 · 4eabae5
1 parent 838391c
commit 4eabae5
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Showing 2 changed files with 20 additions and 7 deletions.
diff --git a/tutorials/regression_mpra_example/download_data.py b/tutorials/regression_mpra_example/download_data.py
@@ -1,9 +1,10 @@
 import io
 import gzip
 import os
-import urllib
+import urllib.request
 import tarfile
 
+import numpy
 import pandas
 import scipy.io
 import selene_sdk.sequences
@@ -14,7 +15,7 @@ def run():
     local_file = "sample_et_al.tar"
 
     # Download the data.
-    urllib.retrieve("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE114002&format=file", local_file)
+    urllib.request.urlretrieve("https://www.ncbi.nlm.nih.gov/geo/download/?acc=GSE114002&format=file", local_file)
     with tarfile.open(local_file, "r") as archive:
         contents = archive.extractfile("GSM3130435_egfp_unmod_1.csv.gz").read()
         contents = gzip.decompress(contents).decode("utf-8")

diff --git a/tutorials/regression_mpra_example/regression_mpra_example.ipynb b/tutorials/regression_mpra_example/regression_mpra_example.ipynb
@@ -9,7 +9,7 @@
     "Selene is a flexible framework, and can be used for tasks beyond simple classification.\n",
     "This tutorial serves as an introduction to training regression models with Selene.\n",
     "For this tutorial, we will predict mean ribosomal load (MRL) from 50 base pair 5' UTR sequences using models and data from [*Human 5′ UTR design and variant effect prediction from a massively parallel translation assay*](https://doi.org/10.1101/310375) by Sample et al.\n",
-    "This data was generated from a massively parallel reporter assay (MPRA), which you can read more about in the preprint [on *bioRxiv*](https://doi.org/10.1101/310375).\n",
+    "This data was generated from a massively parallel reporter assay (MPRA), which you can read more about it in the preprint [on *bioRxiv*](https://doi.org/10.1101/310375).\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -62,7 +62,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,9 +71,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Outputs and logs saved to ./2018-12-08-22-08-14\n",
+      "[VALIDATE] average r2: 0.8641705948994154\n",
+      "[VALIDATE] average r2: 0.8767916124114791\n",
+      "[VALIDATE] average r2: 0.8817297326343803\n",
+      "[TEST] average r2: 0.9232683662644537\n"
+     ]
+    }
+   ],
    "source": [
     "parse_configs_and_run(configs, lr=0.001)"
    ]
@@ -95,7 +107,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.5"
   }
  },
  "nbformat": 4,