v0.0.3: merge staging into main

.codecov.yml

@@ -0,0 +1,24 @@
+codecov:
+  token: d73047ad-2322-4be0-962c-7de537deff4a
+  branch: main
+
+coverage:
+  status:
+    project:
+      default:
+        # Commits pushed to main should not make the overall
+        # project coverage decrease by more than 1%:
+        target: auto
+        threshold: 1%
+    patch:
+      default:
+        # Be tolerant on slight code coverage diff on PRs to limit
+        # noisy red coverage status on github PRs.
+        # Note: The coverage stats are still uploaded
+        # to codecov so that PR reviewers can see uncovered lines
+        target: auto
+        threshold: 1%
+
+ignore:
+- "proglearn/sims"
+- "proglearn/tests"

README.md

@@ -1,10 +1,13 @@
 # ProgLearn
 
 [![Build Status](https://travis-ci.org/neurodata/ProgLearn.svg?branch=main)](https://travis-ci.org/neurodata/ProgLearn)
-[![codecov](https://codecov.io/gh/neurodata/ProgLearn/branches/main/graph/badge.svg)](https://codecov.io/gh/neurodata/ProgLearn)
+[![Codecov](https://codecov.io/gh/neurodata/ProgLearn/branches/main/graph/badge.svg)](https://codecov.io/gh/neurodata/ProgLearn)
 [![PyPI version](https://img.shields.io/pypi/v/proglearn.svg)](https://pypi.org/project/proglearn/)
-[![arXiv shield](https://img.shields.io/badge/arXiv-2004.12908-red.svg?style=flat)](https://arxiv.org/abs/2004.12908)
+[![arXiv](https://img.shields.io/badge/arXiv-2004.12908-red.svg?style=flat)](https://arxiv.org/abs/2004.12908)
 [![License](https://img.shields.io/badge/License-MIT-blue)](https://opensource.org/licenses/MIT)
+[![Netlify Status](https://img.shields.io/netlify/97f86f49-81ed-4292-a100-f7031b54ecc7)](https://app.netlify.com/sites/neuro-data-proglearn/deploys)
+[![Downloads](https://img.shields.io/pypi/dm/proglearn.svg)](https://pypi.org/project/proglearn/#files)
+
 
 `ProgLearn` (**Prog**ressive **Learn**ing) is a package for exploring and using progressive learning algorithms developed by the [neurodata group](https://neurodata.io).
 

benchmarks/parity_experiment/generate_paper_plot.py

@@ -80,7 +80,7 @@ def get_colors(colors, inds):
 
 ns = np.concatenate((n1s, n2s + n1s[-1]))
 ls = ["-", "--"]
-algorithms = ["XOR Forest", "N-XOR Forest", "Lifelong Forest", "Naive Forest"]
+algorithms = ["XOR Forest", "N-XOR Forest", "Progressive Learning Forest (PLF)", "Random Forest (RF)"]
 
 
 TASK1 = "XOR"
@@ -92,23 +92,6 @@ def get_colors(colors, inds):
 colors = sns.color_palette("Set1", n_colors=2)
 
 ax1 = fig.add_subplot(gs[7:13, 2:8])
-# for i, algo in enumerate(algorithms):
-ax1.plot(
-    n1s,
-    mean_error[0, : len(n1s)],
-    label=algorithms[0],
-    c=colors[1],
-    ls=ls[np.sum(0 > 1).astype(int)],
-    lw=3,
-)
-ax1.plot(
-    ns[len(n1s) :],
-    mean_error[2, len(n1s) :],
-    label=algorithms[1],
-    c=colors[1],
-    ls=ls[1],
-    lw=3,
-)
 
 ax1.plot(
     ns,
@@ -134,7 +117,7 @@ def get_colors(colors, inds):
 ax1.set_xlabel("Total Sample Size", fontsize=fontsize)
 ax1.tick_params(labelsize=labelsize)
 ax1.set_yticks([0.1, 0.3, 0.5])
-ax1.set_xticks([250, 750, 1500])
+ax1.set_xticks([50, 750, 1500])
 # ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
 ax1.set_title("XOR", fontsize=30)
 
@@ -149,43 +132,25 @@ def get_colors(colors, inds):
 #######################
 mean_error = unpickle("plots/mean_xor_nxor.pickle")
 
-algorithms = ["XOR Forest", "N-XOR Forest", "Lifelong Forest", "Naive Forest"]
+algorithms = ["XOR Forest", "N-XOR Forest", "Progressive Learning Forest (PLF)", "Random Forest (RF)"]
 
 TASK1 = "XOR"
 TASK2 = "N-XOR"
 
 ax1 = fig.add_subplot(gs[7:13, 10:16])
-ax1.plot(
-    n1s,
-    mean_error[0, : len(n1s)],
-    label=algorithms[0],
-    c=colors[1],
-    ls=ls[np.sum(0 > 1).astype(int)],
-    lw=3,
-)
-ax1.plot(
-    ns[len(n1s) :],
-    mean_error[2, len(n1s) :],
-    label=algorithms[1],
-    c=colors[1],
-    ls=ls[1],
-    lw=3,
-)
 
 ax1.plot(
     ns[len(n1s) :],
     mean_error[3, len(n1s) :],
     label=algorithms[2],
     c=colors[0],
-    ls=ls[1],
     lw=3,
 )
 ax1.plot(
     ns[len(n1s) :],
     mean_error[5, len(n1s) :],
     label=algorithms[3],
     c="g",
-    ls=ls[1],
     lw=3,
 )
 
@@ -196,7 +161,7 @@ def get_colors(colors, inds):
 ax1.tick_params(labelsize=labelsize)
 ax1.set_yticks([0.1, 0.5, 0.9])
 # ax1.set_yticks([0.15, 0.2])
-ax1.set_xticks([250, 750, 1500])
+ax1.set_xticks([50, 750, 1500])
 # ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
 
 # ax1.set_ylim(0.11, 0.21)
@@ -214,7 +179,7 @@ def get_colors(colors, inds):
 
 ##################
 mean_te = unpickle("plots/mean_te_xor_nxor.pickle")
-algorithms = ["Lifelong BTE", "Lifelong FTE", "Naive BTE", "Naive FTE"]
+algorithms = ["PLF BTE", "PLF FTE", "RF BTE", "RF FTE"]
 
 TASK1 = "XOR"
 TASK2 = "N-XOR"
@@ -243,7 +208,7 @@ def get_colors(colors, inds):
 ax1.set_xlabel("Total Sample Size", fontsize=fontsize)
 ax1.tick_params(labelsize=labelsize)
 ax1.set_yticks([0.05, 1, 2.5])
-ax1.set_xticks([250, 750, 1500])
+ax1.set_xticks([50, 750, 1500])
 # ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
 right_side = ax1.spines["right"]
 right_side.set_visible(False)
@@ -270,13 +235,12 @@ def get_colors(colors, inds):
     mean_te[1, len(n1s) :],
     label=algorithms[1],
     c=colors[0],
-    ls=ls[1],
     lw=3,
 )
 
 ax1.plot(ns, mean_te[2], label=algorithms[2], c="g", ls=ls[0], lw=3)
 ax1.plot(
-    ns[len(n1s) :], mean_te[3, len(n1s) :], label=algorithms[3], c="g", ls=ls[1], lw=3
+    ns[len(n1s) :], mean_te[3, len(n1s) :], label=algorithms[3], c="g", lw=3
 )
 
 ax1.set_ylabel("Forward/Backward \n Transfer Efficiency (FTE/BTE)", fontsize=fontsize)
@@ -285,7 +249,7 @@ def get_colors(colors, inds):
 ax1.set_xlabel("Total Sample Size", fontsize=fontsize)
 ax1.tick_params(labelsize=labelsize)
 ax1.set_yticks([0.2, 0.6, 1, 1.2])
-ax1.set_xticks([250, 750, 1500])
+ax1.set_xticks([50, 750, 1500])
 # ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
 right_side = ax1.spines["right"]
 right_side.set_visible(False)