Merge pull request #507 from neurodata/staging

v0.0.6
neurodata · Oct 21, 2021 · 31b0f3d · 31b0f3d
2 parents 894d259 + 634d4d1
commit 31b0f3d
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diff --git a/.circleci/config.yml b/.circleci/config.yml
@@ -1,7 +1,7 @@
 version: 2.1
 
 orbs:
-  codecov: codecov/codecov@1.0.2
+  codecov: codecov/codecov@3.1.1
 
 jobs:
   build:
@@ -34,6 +34,12 @@ jobs:
     parameters:
       module:
         type: string
+      benchmarks:
+        type: string
+      experiments:
+        type: string
+      tutorials:
+        type: string
     docker:
       - image: cimg/python:3.8
     steps:
@@ -56,6 +62,9 @@ jobs:
           command: |
             . venv/bin/activate
             black --check --diff ./<< parameters.module >>
+            black --check --diff ./<< parameters.benchmarks >>
+            black --check --diff ./<< parameters.experiments >>
+            black --check --diff ./<< parameters.tutorials >>
       - run:
           name: run tests and coverage
           command: |
@@ -95,12 +104,12 @@ jobs:
           name: init .pypirc
           command: |
             echo -e "[pypi]" >> ~/.pypirc
-            echo -e "username = $PYPI_USERNAME" >> ~/.pypirc
-            echo -e "password = $PYPI_PASSWORD" >> ~/.pypirc
+            echo -e "username = __token__" >> ~/.pypirc
+            echo -e "password = $PIP_TOKEN" >> ~/.pypirc
       - run:
           name: create packages
           command: |
-            python setup.py sdist
+            python setup.py sdist bdist_wheel
       - run:
           name: upload to pypi
           command: |
@@ -121,13 +130,14 @@ workflows:
       - test-module:
           name: "proglearn"
           module: "proglearn"
+          benchmarks: "benchmarks/"
+          experiments: "docs/experiments/"
+          tutorials: "docs/tutorials/"
           requires:
             - "v3.8"
       - deploy:
-          requires:
-            - "proglearn"
           filters:
             tags:
-              only: /[0-9]+(\.[0-9]+)*/
+              only: /v[0-9]+(\.[0-9]+)*/
             branches:
               ignore: /.*/
diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md
@@ -1,7 +1,7 @@
 ---
 name: Bug Report
 about: Create a report to help us improve ProgLearn
-label: bug
+label:
 
 ---
 

diff --git a/.github/ISSUE_TEMPLATE/documentation_fix.md b/.github/ISSUE_TEMPLATE/documentation_fix.md
@@ -1,7 +1,7 @@
 ---
 name: Documentation Fix
 about: Create a report to help us improve the documentation of ProgLearn
-label: documentation
+label:
 
 ---
 

diff --git a/.github/ISSUE_TEMPLATE/feature_request.md b/.github/ISSUE_TEMPLATE/feature_request.md
@@ -1,17 +1,25 @@
 ---
 name: Feature Request
 about: Suggest an idea for ProgLearn
-label: enhancement
+label:
 
 ---
 
-**Is your feature request related to a problem? Please describe.**
+<!--
+Thank you for taking the time to file a bug report.
+Please fill in the fields below, deleting the sections that
+don't apply to your issue. You can view the final output
+by clicking the preview button above.
+Note: This is a comment, and won't appear in the output.
+-->
 
+#### Is your feature request related to a problem? Please describe.
 
-**Describe the solution you'd like**
 
+#### Describe the solution you'd like
 
-**Describe alternatives you've considered**
 
+#### Describe alternatives you've considered
 
-**Additional context (e.g. screenshots)**
+
+#### Additional context (e.g. screenshots)
diff --git a/CITATION.cff b/CITATION.cff
@@ -63,12 +63,15 @@ authors:
     affiliation: "Johns Hopkins University, Baltimore, MD"
     family-names: Priebe
     given-names: Carey
-cff-version: "1.1.0"
+cff-version: "1.2.0"
 date-released: 2021-09-18
 identifiers:
   -
     type: url
     value: "https://arxiv.org/pdf/2004.12908.pdf"
+  -
+    type: doi
+    value: 10.5281/zenodo.4060264
 keywords:
   - Python
   - classification
@@ -77,8 +80,9 @@ keywords:
   - "transfer learning"
   - "domain adaptation"
 license: MIT
-message: "If you use this software, please cite it using these metadata."
+doi: 10.5281/zenodo.4060264
+message: "If you use ProgLearn, please cite it using these metadata."
 repository-code: "https://github.com/neurodata/ProgLearn"
 title: "Omnidirectional Transfer for Quasilinear Lifelong Learning"
-version: "0.0.5"
+version: "0.0.6"
 ...
diff --git a/LICENSE b/LICENSE
@@ -1,6 +1,6 @@
 MIT License
 
-Copyright (c) 2020 Dr. Joshua T. Vogelstein
+Copyright (c) 2020 Neurodata
 
 Permission is hereby granted, free of charge, to any person obtaining a copy
 of this software and associated documentation files (the "Software"), to deal

diff --git a/README.md b/README.md
@@ -22,4 +22,4 @@
 Some system/package requirements:
 - **Python**: 3.6+
 - **OS**: All major platforms (Linux, macOS, Windows)
-- **Dependencies**: keras, scikit-learn, scipy, numpy, joblib
+- **Dependencies**: tensorflow, scikit-learn, scipy, numpy, joblib
diff --git a/benchmarks/cifar_exp/appendix_tables.ipynb b/benchmarks/cifar_exp/appendix_tables.ipynb
@@ -23,7 +23,8 @@
     "import pickle\n",
     "import matplotlib.pyplot as plt\n",
     "from matplotlib import rcParams\n",
-    "rcParams.update({'figure.autolayout': True})\n",
+    "\n",
+    "rcParams.update({\"figure.autolayout\": True})\n",
     "import numpy as np\n",
     "from itertools import product\n",
     "import seaborn as sns\n",
@@ -39,77 +40,80 @@
     "\n",
     "#%%\n",
     "def unpickle(file):\n",
-    "    with open(file, 'rb') as fo:\n",
-    "        dict = pickle.load(fo, encoding='bytes')\n",
+    "    with open(file, \"rb\") as fo:\n",
+    "        dict = pickle.load(fo, encoding=\"bytes\")\n",
     "    return dict\n",
     "\n",
+    "\n",
     "def get_fte_bte(err, single_err, ntrees):\n",
     "    bte = [[] for i in range(10)]\n",
     "    te = [[] for i in range(10)]\n",
     "    fte = []\n",
-    "    \n",
+    "\n",
     "    for i in range(10):\n",
-    "        for j in range(i,10):\n",
-    "            #print(err[j][i],j,i)\n",
-    "            bte[i].append(err[i][i]/err[j][i])\n",
-    "            te[i].append(single_err[i]/err[j][i])\n",
-    "                \n",
+    "        for j in range(i, 10):\n",
+    "            # print(err[j][i],j,i)\n",
+    "            bte[i].append(err[i][i] / err[j][i])\n",
+    "            te[i].append(single_err[i] / err[j][i])\n",
+    "\n",
     "    for i in range(10):\n",
-    "        #print(single_err[i],err[i][i])\n",
-    "        fte.append(single_err[i]/err[i][i])\n",
-    "            \n",
-    "            \n",
-    "    return fte,bte,te\n",
+    "        # print(single_err[i],err[i][i])\n",
+    "        fte.append(single_err[i] / err[i][i])\n",
     "\n",
-    "def calc_mean_bte(btes,task_num=10,reps=6):\n",
-    "    mean_bte = [[] for i in range(task_num)]\n",
+    "    return fte, bte, te\n",
     "\n",
     "\n",
+    "def calc_mean_bte(btes, task_num=10, reps=6):\n",
+    "    mean_bte = [[] for i in range(task_num)]\n",
+    "\n",
     "    for j in range(task_num):\n",
     "        tmp = 0\n",
     "        for i in range(reps):\n",
     "            tmp += np.array(btes[i][j])\n",
-    "        \n",
-    "        tmp=tmp/reps\n",
+    "\n",
+    "        tmp = tmp / reps\n",
     "        mean_bte[j].extend(tmp)\n",
-    "            \n",
-    "    return mean_bte     \n",
     "\n",
-    "def calc_mean_te(tes,task_num=10,reps=6):\n",
+    "    return mean_bte\n",
+    "\n",
+    "\n",
+    "def calc_mean_te(tes, task_num=10, reps=6):\n",
     "    mean_te = [[] for i in range(task_num)]\n",
     "\n",
     "    for j in range(task_num):\n",
     "        tmp = 0\n",
     "        for i in range(reps):\n",
     "            tmp += np.array(tes[i][j])\n",
-    "        \n",
-    "        tmp=tmp/reps\n",
+    "\n",
+    "        tmp = tmp / reps\n",
     "        mean_te[j].extend(tmp)\n",
-    "            \n",
-    "    return mean_te \n",
     "\n",
-    "def calc_mean_fte(ftes,task_num=10,reps=6):\n",
+    "    return mean_te\n",
+    "\n",
+    "\n",
+    "def calc_mean_fte(ftes, task_num=10, reps=6):\n",
     "    fte = np.asarray(ftes)\n",
-    "    \n",
-    "    return list(np.mean(np.asarray(fte_tmp),axis=0))\n",
     "\n",
-    "def calc_mean_err(err,task_num=10,reps=6):\n",
-    "    mean_err = [[] for i in range(task_num)]\n",
+    "    return list(np.mean(np.asarray(fte_tmp), axis=0))\n",
     "\n",
     "\n",
+    "def calc_mean_err(err, task_num=10, reps=6):\n",
+    "    mean_err = [[] for i in range(task_num)]\n",
+    "\n",
     "    for j in range(task_num):\n",
     "        tmp = 0\n",
     "        for i in range(reps):\n",
     "            tmp += np.array(err[i][j])\n",
-    "        \n",
-    "        tmp=tmp/reps\n",
-    "        #print(tmp)\n",
+    "\n",
+    "        tmp = tmp / reps\n",
+    "        # print(tmp)\n",
     "        mean_err[j].extend([tmp])\n",
-    "            \n",
-    "    return mean_err     \n",
+    "\n",
+    "    return mean_err\n",
+    "\n",
     "\n",
     "#%%\n",
-    "reps = slots*shifts\n",
+    "reps = slots * shifts\n",
     "\n",
     "btes = [[] for i in range(task_num)]\n",
     "ftes = [[] for i in range(task_num)]\n",
@@ -121,39 +125,49 @@
     "fte_tmp = [[] for _ in range(reps)]\n",
     "err_tmp = [[] for _ in range(reps)]\n",
     "\n",
-    "count = 0   \n",
+    "count = 0\n",
     "for slot in range(slots):\n",
     "    for shift in range(shifts):\n",
-    "        filename = 'result/'+model+str(ntrees)+'_'+str(shift+1)+'_'+str(slot)+'.pickle'\n",
+    "        filename = (\n",
+    "            \"result/\"\n",
+    "            + model\n",
+    "            + str(ntrees)\n",
+    "            + \"_\"\n",
+    "            + str(shift + 1)\n",
+    "            + \"_\"\n",
+    "            + str(slot)\n",
+    "            + \".pickle\"\n",
+    "        )\n",
     "        multitask_df, single_task_df = unpickle(filename)\n",
     "\n",
     "        err = [[] for _ in range(10)]\n",
     "\n",
     "        for ii in range(10):\n",
     "            err[ii].extend(\n",
-    "             1 - np.array(\n",
-    "                 multitask_df[multitask_df['base_task']==ii+1]['accuracy']\n",
-    "             )\n",
+    "                1\n",
+    "                - np.array(\n",
+    "                    multitask_df[multitask_df[\"base_task\"] == ii + 1][\"accuracy\"]\n",
+    "                )\n",
     "            )\n",
-    "        single_err = 1 - np.array(single_task_df['accuracy'])\n",
-    "        fte, bte, te = get_fte_bte(err,single_err,ntrees)\n",
-    "    \n",
+    "        single_err = 1 - np.array(single_task_df[\"accuracy\"])\n",
+    "        fte, bte, te = get_fte_bte(err, single_err, ntrees)\n",
+    "\n",
     "        err_ = [[] for i in range(task_num)]\n",
     "        for i in range(task_num):\n",
-    "            for j in range(task_num-i):\n",
-    "                #print(err[i+j][i])\n",
-    "                err_[i].append(err[i+j][i])\n",
-    "            \n",
+    "            for j in range(task_num - i):\n",
+    "                # print(err[i+j][i])\n",
+    "                err_[i].append(err[i + j][i])\n",
+    "\n",
     "        te_tmp[count].extend(te)\n",
     "        bte_tmp[count].extend(bte)\n",
     "        fte_tmp[count].extend(fte)\n",
     "        err_tmp[count].extend(err_)\n",
-    "        count+=1\n",
-    "    \n",
-    "te = calc_mean_te(te_tmp,reps=reps)\n",
-    "bte = calc_mean_bte(bte_tmp,reps=reps)\n",
-    "fte = calc_mean_fte(fte_tmp,reps=reps)\n",
-    "error = calc_mean_err(err_tmp,reps=reps)"
+    "        count += 1\n",
+    "\n",
+    "te = calc_mean_te(te_tmp, reps=reps)\n",
+    "bte = calc_mean_bte(bte_tmp, reps=reps)\n",
+    "fte = calc_mean_fte(fte_tmp, reps=reps)\n",
+    "error = calc_mean_err(err_tmp, reps=reps)"
    ]
   },
   {
@@ -162,9 +176,11 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "flat_te_per_rep = [[np.mean(te_tmp[rep][i]) for i in range(len(te))] for rep in range(reps)]\n",
-    "mean_te_per_rep = np.mean(flat_te_per_rep, axis = 1)\n",
-    "min_te_per_rep = np.min(flat_te_per_rep, axis = 1)"
+    "flat_te_per_rep = [\n",
+    "    [np.mean(te_tmp[rep][i]) for i in range(len(te))] for rep in range(reps)\n",
+    "]\n",
+    "mean_te_per_rep = np.mean(flat_te_per_rep, axis=1)\n",
+    "min_te_per_rep = np.min(flat_te_per_rep, axis=1)"
    ]
   },
   {
@@ -211,8 +227,16 @@
     }
    ],
    "source": [
-    "print(\"Mean FTE(Task 10): ({} +- {})\".format(np.mean(task_fte_task_10_per_rep), np.std(task_fte_task_10_per_rep)))\n",
-    "print(\"Mean BTE(Task 1): ({} +- {})\".format(np.mean(task_bte_task_1_per_rep), np.std(task_bte_task_1_per_rep)))"
+    "print(\n",
+    "    \"Mean FTE(Task 10): ({} +- {})\".format(\n",
+    "        np.mean(task_fte_task_10_per_rep), np.std(task_fte_task_10_per_rep)\n",
+    "    )\n",
+    ")\n",
+    "print(\n",
+    "    \"Mean BTE(Task 1): ({} +- {})\".format(\n",
+    "        np.mean(task_bte_task_1_per_rep), np.std(task_bte_task_1_per_rep)\n",
+    "    )\n",
+    ")"
    ]
   },
   {
@@ -240,7 +264,11 @@
    "source": [
     "for task in range(task_num):\n",
     "    final_te_of_task_per_rep = [te_tmp[rep][task][-1] for rep in range(reps)]\n",
-    "    print(\"Final TE of Task {}: ({} +- {})\".format(task, np.mean(final_te_of_task_per_rep), np.std(final_te_of_task_per_rep)))"
+    "    print(\n",
+    "        \"Final TE of Task {}: ({} +- {})\".format(\n",
+    "            task, np.mean(final_te_of_task_per_rep), np.std(final_te_of_task_per_rep)\n",
+    "        )\n",
+    "    )"
    ]
   },
   {