From 7b7c0b8229cb9e59337b1ebbd4fd0d3d074f2600 Mon Sep 17 00:00:00 2001
From: latasianguy <54046695+latasianguy@users.noreply.github.com>
Date: Tue, 22 Sep 2020 19:28:04 -0400
Subject: [PATCH] Revert "Merge remote-tracking branch 'upstream/master'"

This reverts commit f48f6c283dfd2702564fb6e96f14efe4398c5cd9, reversing
changes made to aa3ecf1fc62b19f67e9da587609f306146d1b841.
---
 .coveragerc                                   |   6 +-
 CONTRIBUTING.md                               |   4 +-
 README.md                                     |   2 +-
 docs/contributing.rst                         |  12 +-
 .../parity_experiment/experiment.ipynb        | 958 ------------------
 .../parity_experiment/generate_paper_plot.py  | 270 -----
 .../parity_experiment/plots/parity_exp.pdf    | Bin 68830 -> 0 bytes
 experiments/rotation_xor/bte_90/experiment.py |  97 ++
 experiments/rotation_xor/figs/RXOR_suite.pdf  | Bin 0 -> 23738 bytes
 experiments/rotation_xor/plot.py              |  82 ++
 .../rotation_xor/te_exp/fte_bte_exp.py        | 100 ++
 experiments/xor_nxor_exp/control_exp.py       | 174 ++++
 experiments/xor_nxor_exp/experiment.py        | 425 ++++++++
 experiments/xor_nxor_exp/result/figs/TE.pdf   | Bin 0 -> 13791 bytes
 .../result/figs/control_xor_rxor.pdf          | Bin 0 -> 15154 bytes
 .../result/figs/control_xor_rxor.png          | Bin 0 -> 800075 bytes
 .../result/figs/gaussian-nxor.pdf             | Bin 0 -> 19009 bytes
 .../xor_nxor_exp/result/figs/gaussian-xor.pdf | Bin 0 -> 18713 bytes
 .../result/figs/generalization_error_nxor.pdf | Bin 0 -> 14096 bytes
 .../result/figs/generalization_error_xor.pdf  | Bin 0 -> 14413 bytes
 .../xor_rxor_experiment.py                    | 422 ++++++++
 proglearn/base.py                             |  96 +-
 proglearn/forest.py                           |  92 --
 proglearn/sims/make_XOR.py                    |  80 ++
 .../tests/test_KNNClassificationVoter.py      |  45 +
 proglearn/tests/test_forest.py                |  49 -
 .../test_lifelongclassificationforest.py      |  50 +
 proglearn/tests/test_system.py                |   3 +-
 ...nsformer.py => test_treeclassification.py} |  32 +-
 proglearn/tests/test_voter.py                 |  56 +-
 proglearn/voters.py                           |   4 +-
 pytest.ini                                    |   3 +-
 runtime.txt                                   |   1 -
 ...taintyForest_Tutorial_1-Installation.ipynb |  80 --
 ...aintyForest_Tutorial_2-Package-Setup.ipynb |  72 --
 35 files changed, 1553 insertions(+), 1662 deletions(-)
 delete mode 100644 experiments/parity_experiment/experiment.ipynb
 delete mode 100644 experiments/parity_experiment/generate_paper_plot.py
 delete mode 100644 experiments/parity_experiment/plots/parity_exp.pdf
 create mode 100644 experiments/rotation_xor/bte_90/experiment.py
 create mode 100644 experiments/rotation_xor/figs/RXOR_suite.pdf
 create mode 100644 experiments/rotation_xor/plot.py
 create mode 100644 experiments/rotation_xor/te_exp/fte_bte_exp.py
 create mode 100644 experiments/xor_nxor_exp/control_exp.py
 create mode 100644 experiments/xor_nxor_exp/experiment.py
 create mode 100644 experiments/xor_nxor_exp/result/figs/TE.pdf
 create mode 100644 experiments/xor_nxor_exp/result/figs/control_xor_rxor.pdf
 create mode 100644 experiments/xor_nxor_exp/result/figs/control_xor_rxor.png
 create mode 100644 experiments/xor_nxor_exp/result/figs/gaussian-nxor.pdf
 create mode 100644 experiments/xor_nxor_exp/result/figs/gaussian-xor.pdf
 create mode 100644 experiments/xor_nxor_exp/result/figs/generalization_error_nxor.pdf
 create mode 100644 experiments/xor_nxor_exp/result/figs/generalization_error_xor.pdf
 create mode 100644 experiments/xor_rxor_spiral_exp/xor_rxor_experiment.py
 create mode 100755 proglearn/sims/make_XOR.py
 create mode 100755 proglearn/tests/test_KNNClassificationVoter.py
 delete mode 100644 proglearn/tests/test_forest.py
 create mode 100644 proglearn/tests/test_lifelongclassificationforest.py
 rename proglearn/tests/{test_transformer.py => test_treeclassification.py} (54%)
 delete mode 100644 runtime.txt
 delete mode 100644 tutorials/UncertaintyForestTutorials/UncertaintyForest_Tutorial_1-Installation.ipynb
 delete mode 100644 tutorials/UncertaintyForestTutorials/UncertaintyForest_Tutorial_2-Package-Setup.ipynb

diff --git a/.coveragerc b/.coveragerc
index 345d6f55a8..7d8e453937 100644
--- a/.coveragerc
+++ b/.coveragerc
@@ -3,8 +3,4 @@ exclude_lines =
     # Have to re-enable the standard pragma
     pragma: no cover
     @abstract
-    NotImplementedError
-    
-[run]
-omit =
-     *__init__.py
+    NotImplementedError
\ No newline at end of file
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index 38ebacfb55..98eb056d0c 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -11,7 +11,7 @@ being contribution of code or documentation to the project. Improving the
 documentation is no less important than improving the library itself. If you
 find a typo in the documentation, or have made improvements, do not hesitate to
 submit a GitHub pull request. Documentation can be found under the
-[docs/](https://github.com/neurodata/progressive-learning/tree/master/docs) directory.
+[doc/](https://github.com/neurodata/progressive-learning/tree/master/docs) directory.
 
 But there are many other ways to help. In particular answering queries on the
 [issue tracker](https://github.com/neurodata/progressive-learning/issues), and
@@ -27,4 +27,4 @@ Code of Conduct
 ---------------
 
 We abide by the principles of openness, respect, and consideration of others
-of the Python Software Foundation: https://www.python.org/psf/codeofconduct/.
+of the Python Software Foundation: https://www.python.org/psf/codeofconduct/.
\ No newline at end of file
diff --git a/README.md b/README.md
index 3f203a12e4..13def3bd43 100644
--- a/README.md
+++ b/README.md
@@ -60,7 +60,7 @@ python3 setup.py install
 ```
 
 # Contributing
-We welcome contributions from anyone. Please see our [contribution guidelines](https://github.com/neurodata/progressive-learning/blob/master/CONTRIBUTING.md) before making a pull request. Our 
+We welcome contributions from anyone. Please see our [contribution guidelines](http://docs.neurodata.io/progressive-learning/) before making a pull request. Our 
 [issues](https://github.com/neurodata/progressive-learning/issues) page is full of places we could use help! 
 If you have an idea for an improvement not listed there, please 
 [make an issue](https://github.com/neurodata/progressive-learning/issues/new) first so you can discuss with the 
diff --git a/docs/contributing.rst b/docs/contributing.rst
index 3794f89e43..106450ef4d 100644
--- a/docs/contributing.rst
+++ b/docs/contributing.rst
@@ -46,7 +46,7 @@ good feedback:
 - If an exception is raised, please **provide the full traceback**.
 
 - Please include your **operating system type and version number**, as well as
-  your **Python and ProgL versions**. This information
+  your **Python and hyppo versions**. This information
   can be found by running the following code snippet::
 
     import platform; print(platform.platform())
@@ -61,7 +61,7 @@ good feedback:
 Contributing Code
 -----------------
 
-The preferred workflow for contributing to `ProgL` is to fork the main
+The preferred workflow for contributing to `hyppo` is to fork the main
 repository on GitHub, clone, and develop on a branch. Steps:
 
 1. Fork the `project repository <https://github.com/neurodata/progressive-learning>`__ by clicking
@@ -70,12 +70,12 @@ repository on GitHub, clone, and develop on a branch. Steps:
    fork a repository see `this
    guide <https://help.github.com/articles/fork-a-repo/>`__.
 
-2. Clone your fork of the ``ProgL`` repo from your GitHub account to your
+2. Clone your fork of the ``hyppo`` repo from your GitHub account to your
    local disk:
 
    .. code:: bash
 
-      $ git clone git@github.com:YourLogin/progressive-learning.git
+      $ git clone git@github.com:YourLogin/hyppo.git
       $ cd progressive-learning
 
 3. Create a ``feature`` branch to hold your development changes:
@@ -150,7 +150,7 @@ before you submit a pull request:
 Coding Guidelines
 -----------------
 
-Uniformly formatted code makes it easier to share code ownership. ``ProgL``
+Uniformly formatted code makes it easier to share code ownership. ``hyppo``
 package closely follows the official Python guidelines detailed in
 `PEP8 <https://www.python.org/dev/peps/pep-0008/>`__ that detail how
 code should be formatted and indented. Please read it and follow it.
@@ -164,4 +164,4 @@ guidelines. Please read and follow the
 `numpydoc <https://numpydoc.readthedocs.io/en/latest/format.html#overview>`__
 guidelines. Refer to the
 `example.py <https://numpydoc.readthedocs.io/en/latest/example.html#example>`__
-provided by numpydoc.
+provided by numpydoc.
\ No newline at end of file
diff --git a/experiments/parity_experiment/experiment.ipynb b/experiments/parity_experiment/experiment.ipynb
deleted file mode 100644
index 275585c8b9..0000000000
--- a/experiments/parity_experiment/experiment.ipynb
+++ /dev/null
@@ -1,958 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import random\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "import tensorflow as tf\n",
-    "import tensorflow.keras as keras\n",
-    "import seaborn as sns\n",
-    "import numpy as np\n",
-    "import pickle\n",
-    "from joblib import Parallel, delayed\n",
-    "from math import log2, ceil\n",
-    "from proglearn.progressive_learner import ProgressiveLearner\n",
-    "from proglearn.deciders import SimpleArgmaxAverage\n",
-    "from proglearn.transformers import TreeClassificationTransformer, NeuralClassificationTransformer\n",
-    "from proglearn.voters import TreeClassificationVoter, KNNClassificationVoter\n",
-    "from proglearn.sims import generate_gaussian_parity"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Simulation Data Generation\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
-      "text/plain": "<Figure size 1728x576 with 3 Axes>",
-      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"568.834375pt\" version=\"1.1\" viewBox=\"0 0 1720.8 568.834375\" width=\"1720.8pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <defs>\n  <style type=\"text/css\">\n*{stroke-linecap:butt;stroke-linejoin:round;}\n  </style>\n </defs>\n <g id=\"figure_1\">\n  <g id=\"patch_1\">\n   <path d=\"M 0 568.834375 \nL 1720.8 568.834375 \nL 1720.8 0 \nL 0 0 \nz\n\" style=\"fill:none;\"/>\n  </g>\n  <g id=\"axes_1\">\n   <g id=\"PathCollection_1\">\n    <defs>\n     <path d=\"M 0 3.535534 \nC 0.937635 3.535534 1.836992 3.163008 2.5 2.5 \nC 3.163008 1.836992 3.535534 0.937635 3.535534 0 \nC 3.535534 -0.937635 3.163008 -1.836992 2.5 -2.5 \nC 1.836992 -3.163008 0.937635 -3.535534 0 -3.535534 \nC 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\n"
-     },
-     "metadata": {}
-    }
-   ],
-   "source": [
-    "X_xor, y_xor = generate_gaussian_parity(1000)\n",
-    "X_nxor, y_nxor = generate_gaussian_parity(1000, angle_params=np.pi/2)\n",
-    "X_rxor, y_rxor = generate_gaussian_parity(1000, angle_params=np.pi/4)\n",
-    "\n",
-    "colors = sns.color_palette('Dark2', n_colors=2)\n",
-    "\n",
-    "fig, ax = plt.subplots(1,3, figsize=(24,8))\n",
-    "\n",
-    "clr = [colors[i] for i in y_xor]\n",
-    "ax[0].scatter(X_xor[:, 0], X_xor[:, 1], c=clr, s=50)\n",
-    "ax[0].set_xticks([])\n",
-    "ax[0].set_yticks([])\n",
-    "ax[0].set_title('Gaussian XOR', fontsize=30)\n",
-    "ax[0].axis('off')\n",
-    "\n",
-    "clr = [colors[i] for i in y_nxor]\n",
-    "ax[1].scatter(X_nxor[:, 0], X_nxor[:, 1], c=clr, s=50)\n",
-    "ax[1].set_xticks([])\n",
-    "ax[1].set_yticks([])\n",
-    "ax[1].set_title('Gaussian NXOR', fontsize=30)\n",
-    "ax[1].axis('off')\n",
-    "\n",
-    "clr = [colors[i] for i in y_rxor]\n",
-    "ax[2].scatter(X_rxor[:, 0], X_rxor[:, 1], c=clr, s=50)\n",
-    "ax[2].set_xticks([])\n",
-    "ax[2].set_yticks([])\n",
-    "ax[2].set_title('Gaussian RXOR', fontsize=30)\n",
-    "ax[2].axis('off')\n",
-    "\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "source": [
-    "# Experiment definition"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def experiment(n_task1, n_task2, n_test=1000, \n",
-    "               task1_angle=0, task2_angle=np.pi/2, \n",
-    "               n_trees=10, max_depth=None, random_state=None):\n",
-    "    \n",
-    "    \"\"\"\n",
-    "    A function to do progressive experiment between two tasks\n",
-    "    where the task data is generated using Gaussian parity.\n",
-    "    \n",
-    "    Parameters\n",
-    "    ----------\n",
-    "    n_task1 : int\n",
-    "        Total number of train sample for task 1.\n",
-    "    \n",
-    "    n_task2 : int\n",
-    "        Total number of train dsample for task 2\n",
-    "\n",
-    "    n_test : int, optional (default=1000)\n",
-    "        Number of test sample for each task.\n",
-    "        \n",
-    "    task1_angle : float, optional (default=0)\n",
-    "        Angle in radian for task 1.\n",
-    "            \n",
-    "    task2_angle : float, optional (default=numpy.pi/2)\n",
-    "        Angle in radian for task 2.\n",
-    "            \n",
-    "    n_trees : int, optional (default=10)\n",
-    "        Number of total trees to train for each task.\n",
-    "\n",
-    "    max_depth : int, optional (default=None)\n",
-    "        Maximum allowable depth for each tree.\n",
-    "        \n",
-    "    random_state : int, RandomState instance, default=None\n",
-    "        Determines random number generation for dataset creation. Pass an int\n",
-    "        for reproducible output across multiple function calls.\n",
-    "        \n",
-    "    \n",
-    "    Returns\n",
-    "    -------\n",
-    "    errors : array of shape [6]\n",
-    "        Elements of the array is organized as single task error task1,\n",
-    "        multitask error task1, single task error task2,\n",
-    "        multitask error task2, naive UF error task1,\n",
-    "        naive UF task2.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    if n_task1==0 and n_task2==0:\n",
-    "        raise ValueError('Wake up and provide samples to train!!!')\n",
-    "\n",
-    "    if random_state != None:\n",
-    "        np.random.seed(random_state)\n",
-    "\n",
-    "    errors = np.zeros(6,dtype=float)\n",
-    "\n",
-    "    default_transformer_class = TreeClassificationTransformer\n",
-    "    default_transformer_kwargs = {\"kwargs\" : {\"max_depth\" : max_depth}}\n",
-    "\n",
-    "    default_voter_class = TreeClassificationVoter\n",
-    "    default_voter_kwargs = {}\n",
-    "\n",
-    "    default_decider_class = SimpleArgmaxAverage\n",
-    "    default_decider_kwargs = {\"classes\" : np.arange(2)}\n",
-    "    progressive_learner = ProgressiveLearner(default_transformer_class = default_transformer_class,\n",
-    "                                            default_transformer_kwargs = default_transformer_kwargs,\n",
-    "                                            default_voter_class = default_voter_class,\n",
-    "                                            default_voter_kwargs = default_voter_kwargs,\n",
-    "                                            default_decider_class = default_decider_class,\n",
-    "                                            default_decider_kwargs = default_decider_kwargs)\n",
-    "    uf = ProgressiveLearner(default_transformer_class = default_transformer_class,\n",
-    "                                            default_transformer_kwargs = default_transformer_kwargs,\n",
-    "                                            default_voter_class = default_voter_class,\n",
-    "                                            default_voter_kwargs = default_voter_kwargs,\n",
-    "                                            default_decider_class = default_decider_class,\n",
-    "                                            default_decider_kwargs = default_decider_kwargs)\n",
-    "    naive_uf = ProgressiveLearner(default_transformer_class = default_transformer_class,\n",
-    "                                            default_transformer_kwargs = default_transformer_kwargs,\n",
-    "                                            default_voter_class = default_voter_class,\n",
-    "                                            default_voter_kwargs = default_voter_kwargs,\n",
-    "                                            default_decider_class = default_decider_class,\n",
-    "                                            default_decider_kwargs = default_decider_kwargs)\n",
-    "    \n",
-    "    #source data\n",
-    "    X_task1, y_task1 = generate_gaussian_parity(n_task1, angle_params=task1_angle)\n",
-    "    test_task1, test_label_task1 = generate_gaussian_parity(n_test, angle_params=task1_angle)\n",
-    "\n",
-    "    #target data\n",
-    "    X_task2, y_task2 = generate_gaussian_parity(n_task2, angle_params=task2_angle)\n",
-    "    test_task2, test_label_task2 = generate_gaussian_parity(n_test, angle_params=task2_angle)\n",
-    "\n",
-    "    if n_task1 == 0:\n",
-    "        progressive_learner.add_task(X_task2, y_task2, num_transformers=n_trees)\n",
-    "\n",
-    "        errors[0] = 0.5\n",
-    "        errors[1] = 0.5\n",
-    "\n",
-    "        uf_task2=progressive_learner.predict(test_task2,\n",
-    "                                             transformer_ids=[0], task_id=0)\n",
-    "        l2f_task2=progressive_learner.predict(test_task2, task_id=0)\n",
-    "\n",
-    "        errors[2] = 1 - np.mean(uf_task2 == test_label_task2)\n",
-    "        errors[3] = 1 - np.mean(l2f_task2 == test_label_task2)\n",
-    "        \n",
-    "        errors[4] = 0.5\n",
-    "        errors[5] = 1 - np.mean(uf_task2 == test_label_task2)\n",
-    "    elif n_task2 == 0:\n",
-    "        progressive_learner.add_task(X_task1, y_task1,\n",
-    "                                     num_transformers=n_trees)\n",
-    "\n",
-    "        uf_task1=progressive_learner.predict(test_task1, \n",
-    "                                             transformer_ids=[0], task_id=0)\n",
-    "        l2f_task1=progressive_learner.predict(test_task1, task_id=0)\n",
-    "\n",
-    "        errors[0] = 1 - np.mean(uf_task1 == test_label_task1)\n",
-    "        errors[1] = 1 - np.mean(l2f_task1 == test_label_task1)\n",
-    "        \n",
-    "        errors[2] = 0.5\n",
-    "        errors[3] = 0.5\n",
-    "        \n",
-    "        errors[4] = 1 - np.mean(uf_task1 == test_label_task1)\n",
-    "        errors[5] = 0.5\n",
-    "    else:\n",
-    "        progressive_learner.add_task(X_task1, y_task1, num_transformers=n_trees)\n",
-    "        progressive_learner.add_task(X_task2, y_task2, num_transformers=n_trees)\n",
-    "\n",
-    "        uf.add_task(X_task1, y_task1, num_transformers=2*n_trees)\n",
-    "        uf.add_task(X_task2, y_task2, num_transformers=2*n_trees)\n",
-    "        \n",
-    "        naive_uf_train_x = np.concatenate((X_task1,X_task2),axis=0)\n",
-    "        naive_uf_train_y = np.concatenate((y_task1,y_task2),axis=0)\n",
-    "        naive_uf.add_task(\n",
-    "                naive_uf_train_x, naive_uf_train_y, num_transformers=n_trees\n",
-    "                )\n",
-    "        \n",
-    "        uf_task1=uf.predict(test_task1, transformer_ids=[0], task_id=0)\n",
-    "        l2f_task1=progressive_learner.predict(test_task1, task_id=0)\n",
-    "        uf_task2=uf.predict(test_task2, transformer_ids=[1], task_id=1)\n",
-    "        l2f_task2=progressive_learner.predict(test_task2, task_id=1)\n",
-    "        naive_uf_task1 = naive_uf.predict(\n",
-    "            test_task1, transformer_ids=[0], task_id=0\n",
-    "        )\n",
-    "        naive_uf_task2 = naive_uf.predict(\n",
-    "            test_task2, transformer_ids=[0], task_id=0\n",
-    "        )\n",
-    "\n",
-    "        errors[0] = 1 - np.mean(\n",
-    "            uf_task1 == test_label_task1\n",
-    "        )\n",
-    "        errors[1] = 1 - np.mean(\n",
-    "            l2f_task1 == test_label_task1\n",
-    "        )\n",
-    "        errors[2] = 1 - np.mean(\n",
-    "            uf_task2 == test_label_task2\n",
-    "        )\n",
-    "        errors[3] = 1 - np.mean(\n",
-    "            l2f_task2 == test_label_task2\n",
-    "        )\n",
-    "        errors[4] = 1 - np.mean(\n",
-    "            naive_uf_task1 == test_label_task1\n",
-    "        )\n",
-    "        errors[5] = 1 - np.mean(\n",
-    "            naive_uf_task2 == test_label_task2\n",
-    "        )\n",
-    "\n",
-    "    return errors\n"
-   ]
-  },
-  {
-   "source": [
-    "# Run experiment for XOR N-XOR tasks"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [],
-   "source": [
-    "mc_rep = 1000\n",
-    "n_test = 1000\n",
-    "n_trees = 10\n",
-    "n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)\n",
-    "n_nxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)\n",
-    "\n",
-    "mean_error = np.zeros((6, len(n_xor)+len(n_nxor)))\n",
-    "std_error = np.zeros((6, len(n_xor)+len(n_nxor)))\n",
-    "\n",
-    "mean_te = np.zeros((4, len(n_xor)+len(n_nxor)))\n",
-    "std_te = np.zeros((4, len(n_xor)+len(n_nxor)))\n",
-    "\n",
-    "for i,n1 in enumerate(n_xor):\n",
-    "    print('starting to compute %s xor\\n'%n1)\n",
-    "    error = np.array(\n",
-    "        Parallel(n_jobs=-1,verbose=1)(\n",
-    "        delayed(experiment)(\n",
-    "            n1,0,max_depth=ceil(log2(n1))\n",
-    "        ) for _ in range(mc_rep)\n",
-    "      )\n",
-    "    )\n",
-    "    mean_error[:,i] = np.mean(error,axis=0)\n",
-    "    std_error[:,i] = np.std(error,ddof=1,axis=0)\n",
-    "    mean_te[0,i] = np.mean(error[:,0])/np.mean(error[:,1])\n",
-    "    mean_te[1,i] = np.mean(error[:,2])/np.mean(error[:,3])\n",
-    "    mean_te[2,i] = np.mean(error[:,0])/np.mean(error[:,4])\n",
-    "    mean_te[3,i] = np.mean(error[:,2])/np.mean(error[:,5])\n",
-    "    \n",
-    "\n",
-    "    if n1==n_xor[-1]:\n",
-    "        for j,n2 in enumerate(n_nxor):\n",
-    "            print('starting to compute %s nxor\\n'%n2)\n",
-    "            \n",
-    "            error = np.array(\n",
-    "                Parallel(n_jobs=-1,verbose=1)(\n",
-    "                delayed(experiment)(\n",
-    "                    n1,n2,max_depth=ceil(log2(750))\n",
-    "                ) for _ in range(mc_rep)\n",
-    "              )\n",
-    "            )\n",
-    "            mean_error[:,i+j+1] = np.mean(error,axis=0)\n",
-    "            std_error[:,i+j+1] = np.std(error,ddof=1,axis=0)\n",
-    "            mean_te[0,i+j+1] = np.mean(error[:,0])/np.mean(error[:,1])\n",
-    "            mean_te[1,i+j+1] = np.mean(error[:,2])/np.mean(error[:,3])\n",
-    "            mean_te[2,i+j+1] = np.mean(error[:,0])/np.mean(error[:,4])\n",
-    "            mean_te[3,i+j+1] = np.mean(error[:,2])/np.mean(error[:,5])\n",
-    "\n",
-    "with open('./data/mean_xor_nxor.pickle','wb') as f:\n",
-    "    pickle.dump(mean_error,f)\n",
-    "    \n",
-    "with open('./data/std_xor_nxor.pickle','wb') as f:\n",
-    "    pickle.dump(std_error,f)\n",
-    "    \n",
-    "with open('./data/mean_te_xor_nxor.pickle','wb') as f:\n",
-    "    pickle.dump(mean_te,f)\n",
-    "    "
-   ]
-  },
-  {
-   "source": [
-    "# Plot XOR N-XOR results"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
-      "text/plain": "(-1.2865128424350156,\n 1.4879660966326593,\n -1.2529479199200702,\n 1.3480024397987906)"
-     },
-     "metadata": {},
-     "execution_count": 6
-    },
-    {
-     "output_type": "display_data",
-     "data": {
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "with open('data/mean_xor_nxor.pickle','rb') as f:\n",
-    "    mean_error = pickle.load(f)\n",
-    "\n",
-    "n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)\n",
-    "n_nxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)\n",
-    "\n",
-    "n1s = n_xor\n",
-    "n2s = n_nxor\n",
-    "\n",
-    "ns = np.concatenate((n1s, n2s + n1s[-1]))\n",
-    "ls=['-', '--']\n",
-    "algorithms = ['XOR Forest', 'N-XOR Forest', 'Lifelong Forest', 'Naive Forest']\n",
-    "\n",
-    "\n",
-    "TASK1='XOR'\n",
-    "TASK2='N-XOR'\n",
-    "\n",
-    "fontsize=30\n",
-    "labelsize=28\n",
-    "\n",
-    "colors = sns.color_palette(\"Set1\", n_colors = 2)\n",
-    "\n",
-    "fig = plt.figure(constrained_layout=True,figsize=(21,14))\n",
-    "gs = fig.add_gridspec(14, 21)\n",
-    "ax1 = fig.add_subplot(gs[7:,:6])\n",
-    "ax1.plot(n1s, mean_error[0,:len(n1s)], label=algorithms[0], c=colors[1], ls=ls[np.sum(0 > 1).astype(int)], lw=3)\n",
-    "ax1.plot(ns[len(n1s):], mean_error[2, len(n1s):], label=algorithms[1], c=colors[1], ls=ls[1], lw=3)\n",
-    "ax1.plot(ns, mean_error[1], label=algorithms[2], c=colors[0], ls=ls[np.sum(1 > 1).astype(int)], lw=3)\n",
-    "ax1.plot(ns, mean_error[4], label=algorithms[3], c='g', ls=ls[np.sum(1 > 1).astype(int)], lw=3)\n",
-    "\n",
-    "ax1.set_ylabel('Generalization Error (%s)'%(TASK1), fontsize=fontsize)\n",
-    "ax1.legend(loc='upper right', fontsize=20, frameon=False)\n",
-    "#ax1.set_ylim(0.09, 0.21)\n",
-    "ax1.set_xlabel('Total Sample Size', fontsize=fontsize)\n",
-    "ax1.tick_params(labelsize=labelsize)\n",
-    "#ax1.set_yticks([0.5,0.15, 0.25])\n",
-    "ax1.set_xticks([250,750,1500])\n",
-    "ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle=\"dashed\")\n",
-    "ax1.set_title('XOR', fontsize=30)\n",
-    "\n",
-    "right_side = ax1.spines[\"right\"]\n",
-    "right_side.set_visible(False)\n",
-    "top_side = ax1.spines[\"top\"]\n",
-    "top_side.set_visible(False)\n",
-    "\n",
-    "ax1.text(400, np.mean(ax1.get_ylim()), \"%s\"%(TASK1), fontsize=26)\n",
-    "ax1.text(900, np.mean(ax1.get_ylim()), \"%s\"%(TASK2), fontsize=26)\n",
-    "\n",
-    "#####################################\n",
-    "with open('data/mean_xor_nxor.pickle','rb') as f:\n",
-    "    mean_error = pickle.load(f)\n",
-    "\n",
-    "algorithms = ['XOR Forest', 'N-XOR Forest', 'Lifelong Forest', 'Naive Forest']\n",
-    "\n",
-    "TASK1='XOR'\n",
-    "TASK2='N-XOR'\n",
-    "\n",
-    "ax1 = fig.add_subplot(gs[7:,7:13])\n",
-    "ax1.plot(n1s, mean_error[0,:len(n1s)], label=algorithms[0], c=colors[1], ls=ls[np.sum(0 > 1).astype(int)], lw=3)\n",
-    "ax1.plot(ns[len(n1s):], mean_error[2, len(n1s):], label=algorithms[1], c=colors[1], ls=ls[1], lw=3)\n",
-    "\n",
-    "ax1.plot(ns[len(n1s):], mean_error[3, len(n1s):], label=algorithms[2], c=colors[0], ls=ls[1], lw=3)\n",
-    "ax1.plot(ns[len(n1s):], mean_error[5, len(n1s):], label=algorithms[3], c='g', ls=ls[1], lw=3)\n",
-    "\n",
-    "ax1.set_ylabel('Generalization Error (%s)'%(TASK2), fontsize=fontsize)\n",
-    "ax1.legend(loc='upper right', fontsize=18, frameon=False)\n",
-    "ax1.set_xlabel('Total Sample Size', fontsize=fontsize)\n",
-    "ax1.tick_params(labelsize=labelsize)\n",
-    "ax1.set_xticks([250,750,1500])\n",
-    "ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle=\"dashed\")\n",
-    "\n",
-    "\n",
-    "right_side = ax1.spines[\"right\"]\n",
-    "right_side.set_visible(False)\n",
-    "top_side = ax1.spines[\"top\"]\n",
-    "top_side.set_visible(False)\n",
-    "\n",
-    "ax1.text(400, np.mean(ax1.get_ylim()), \"%s\"%(TASK1), fontsize=26)\n",
-    "ax1.text(900, np.mean(ax1.get_ylim()), \"%s\"%(TASK2), fontsize=26)\n",
-    "\n",
-    "ax1.set_title('N-XOR', fontsize=30)\n",
-    "\n",
-    "#####################################\n",
-    "with open('data/mean_te_xor_nxor.pickle','rb') as f:\n",
-    "    mean_te = pickle.load(f)\n",
-    "\n",
-    "algorithms = ['Lifelong BTE', 'Lifelong FTE', 'Naive BTE', 'Naive FTE']\n",
-    "\n",
-    "TASK1='XOR'\n",
-    "TASK2='N-XOR'\n",
-    "\n",
-    "ax1 = fig.add_subplot(gs[7:,14:])\n",
-    "\n",
-    "ax1.plot(ns, mean_te[0], label=algorithms[0], c=colors[0], ls=ls[0], lw=3)\n",
-    "ax1.plot(ns[len(n1s):], mean_te[1, len(n1s):], label=algorithms[1], c=colors[0], ls=ls[1], lw=3)\n",
-    "ax1.plot(ns, mean_te[2], label=algorithms[2], c='g', ls=ls[0], lw=3)\n",
-    "ax1.plot(ns[len(n1s):], mean_te[3, len(n1s):], label=algorithms[3], c='g', ls=ls[1], lw=3)\n",
-    "\n",
-    "ax1.set_ylabel('Forward/Backward \\n Transfer Efficiency (FTE/BTE)', fontsize=fontsize)\n",
-    "ax1.legend(loc='upper right', fontsize=20, frameon=False)\n",
-    "ax1.set_xlabel('Total Sample Size', fontsize=fontsize)\n",
-    "ax1.tick_params(labelsize=labelsize)\n",
-    "#ax1.set_yticks([0,.5,1,1.5])\n",
-    "ax1.set_xticks([250,750,1500])\n",
-    "ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle=\"dashed\")\n",
-    "right_side = ax1.spines[\"right\"]\n",
-    "right_side.set_visible(False)\n",
-    "top_side = ax1.spines[\"top\"]\n",
-    "top_side.set_visible(False)\n",
-    "ax1.hlines(1, 50,1500, colors='gray', linestyles='dashed',linewidth=1.5)\n",
-    "\n",
-    "ax1.text(400, np.mean(ax1.get_ylim()), \"%s\"%(TASK1), fontsize=26)\n",
-    "ax1.text(900, np.mean(ax1.get_ylim()), \"%s\"%(TASK2), fontsize=26)\n",
-    "\n",
-    "colors = sns.color_palette('Dark2', n_colors=2)\n",
-    "\n",
-    "X, Y = generate_gaussian_parity(750, angle_params=0)\n",
-    "Z, W = generate_gaussian_parity(750, angle_params=np.pi/2)\n",
-    "\n",
-    "ax = fig.add_subplot(gs[:6,4:10])\n",
-    "clr = [colors[i] for i in Y]\n",
-    "ax.scatter(X[:, 0], X[:, 1], c=clr, s=50)\n",
-    "\n",
-    "ax.set_xticks([])\n",
-    "ax.set_yticks([])\n",
-    "ax.set_title('Gaussian XOR', fontsize=30)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "ax.axis('off')\n",
-    "\n",
-    "colors = sns.color_palette('Dark2', n_colors=2)\n",
-    "\n",
-    "ax = fig.add_subplot(gs[:6,11:16])\n",
-    "clr = [colors[i] for i in W]\n",
-    "ax.scatter(Z[:, 0], Z[:, 1], c=clr, s=50)\n",
-    "\n",
-    "ax.set_xticks([])\n",
-    "ax.set_yticks([])\n",
-    "ax.set_title('Gaussian N-XOR', fontsize=30)\n",
-    "ax.axis('off')"
-   ]
-  },
-  {
-   "source": [
-    "# Run experiment for XOR R-XOR tasks"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mc_rep = 1000\n",
-    "n_test = 1000\n",
-    "n_trees = 10\n",
-    "n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)\n",
-    "n_rxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)\n",
-    "\n",
-    "mean_error = np.zeros((6, len(n_xor)+len(n_rxor)))\n",
-    "std_error = np.zeros((6, len(n_xor)+len(n_rxor)))\n",
-    "\n",
-    "mean_te = np.zeros((4, len(n_xor)+len(n_rxor)))\n",
-    "std_te = np.zeros((4, len(n_xor)+len(n_rxor)))\n",
-    "\n",
-    "for i,n1 in enumerate(n_xor):\n",
-    "    print('starting to compute %s xor\\n'%n1)\n",
-    "    error = np.array(\n",
-    "        Parallel(n_jobs=-1,verbose=1)(\n",
-    "        delayed(experiment)(\n",
-    "            n1,0,task2_angle=np.pi/4,\n",
-    "            max_depth=ceil(log2(750))\n",
-    "        ) for _ in range(mc_rep)\n",
-    "      )\n",
-    "    )\n",
-    "    mean_error[:,i] = np.mean(error,axis=0)\n",
-    "    std_error[:,i] = np.std(error,ddof=1,axis=0)\n",
-    "    mean_te[0,i] = np.mean(error[:,0])/np.mean(error[:,1])\n",
-    "    mean_te[1,i] = np.mean(error[:,2])/np.mean(error[:,3])\n",
-    "    mean_te[2,i] = np.mean(error[:,0])/np.mean(error[:,4])\n",
-    "    mean_te[3,i] = np.mean(error[:,2])/np.mean(error[:,5])\n",
-    "\n",
-    "    if n1==n_xor[-1]:\n",
-    "        for j,n2 in enumerate(n_rxor):\n",
-    "            print('starting to compute %s rxor\\n'%n2)\n",
-    "            \n",
-    "            error = np.array(\n",
-    "                Parallel(n_jobs=-1,verbose=1)(\n",
-    "                delayed(experiment)(\n",
-    "                    n1,n2,task2_angle=np.pi/4,\n",
-    "                    max_depth=ceil(log2(750))\n",
-    "                ) for _ in range(mc_rep)\n",
-    "              )\n",
-    "            )\n",
-    "            mean_error[:,i+j+1] = np.mean(error,axis=0)\n",
-    "            std_error[:,i+j+1] = np.std(error,ddof=1,axis=0)\n",
-    "            mean_te[0,i+j+1] = np.mean(error[:,0])/np.mean(error[:,1])\n",
-    "            mean_te[1,i+j+1] = np.mean(error[:,2])/np.mean(error[:,3])\n",
-    "            mean_te[2,i+j+1] = np.mean(error[:,0])/np.mean(error[:,4])\n",
-    "            mean_te[3,i+j+1] = np.mean(error[:,2])/np.mean(error[:,5])\n",
-    "\n",
-    "with open('./data/mean_xor_rxor.pickle','wb') as f:\n",
-    "    pickle.dump(mean_error,f)\n",
-    "    \n",
-    "with open('./data/std_xor_rxor.pickle','wb') as f:\n",
-    "    pickle.dump(std_error,f)\n",
-    "    \n",
-    "with open('./data/mean_te_xor_rxor.pickle','wb') as f:\n",
-    "    pickle.dump(mean_te,f)"
-   ]
-  },
-  {
-   "source": [
-    "# Plot XOR R-XOR results"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "execute_result",
-     "data": {
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-     "metadata": {},
-     "execution_count": 7
-    },
-    {
-     "output_type": "display_data",
-     "data": {
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "with open('data/mean_xor_rxor.pickle','rb') as f:\n",
-    "    mean_error = pickle.load(f)\n",
-    "\n",
-    "n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)\n",
-    "n_nxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)\n",
-    "\n",
-    "n1s = n_xor\n",
-    "n2s = n_nxor\n",
-    "\n",
-    "ns = np.concatenate((n1s, n2s + n1s[-1]))\n",
-    "ls=['-', '--']\n",
-    "algorithms = ['XOR Forest', 'R-XOR Forest', 'Lifelong Forest', 'Naive Forest']\n",
-    "\n",
-    "\n",
-    "TASK1='XOR'\n",
-    "TASK2='R-XOR'\n",
-    "\n",
-    "fontsize=30\n",
-    "labelsize=28\n",
-    "\n",
-    "colors = sns.color_palette(\"Set1\", n_colors = 2)\n",
-    "\n",
-    "fig = plt.figure(constrained_layout=True,figsize=(21,14))\n",
-    "gs = fig.add_gridspec(14, 21)\n",
-    "ax1 = fig.add_subplot(gs[7:,:6])\n",
-    "ax1.plot(n1s, mean_error[0,:len(n1s)], label=algorithms[0], c=colors[1], ls=ls[np.sum(0 > 1).astype(int)], lw=3)\n",
-    "ax1.plot(ns[len(n1s):], mean_error[2, len(n1s):], label=algorithms[1], c=colors[1], ls=ls[1], lw=3)\n",
-    "ax1.plot(ns, mean_error[1], label=algorithms[2], c=colors[0], ls=ls[np.sum(1 > 1).astype(int)], lw=3)\n",
-    "ax1.plot(ns, mean_error[4], label=algorithms[3], c='g', ls=ls[np.sum(1 > 1).astype(int)], lw=3)\n",
-    "\n",
-    "ax1.set_ylabel('Generalization Error (%s)'%(TASK1), fontsize=fontsize)\n",
-    "ax1.legend(loc='upper right', fontsize=20, frameon=False)\n",
-    "#ax1.set_ylim(0.09, 0.21)\n",
-    "ax1.set_xlabel('Total Sample Size', fontsize=fontsize)\n",
-    "ax1.tick_params(labelsize=labelsize)\n",
-    "#ax1.set_yticks([0.1,0.15, 0.2])\n",
-    "ax1.set_xticks([250,750,1500])\n",
-    "ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle=\"dashed\")\n",
-    "ax1.set_title('XOR', fontsize=30)\n",
-    "\n",
-    "right_side = ax1.spines[\"right\"]\n",
-    "right_side.set_visible(False)\n",
-    "top_side = ax1.spines[\"top\"]\n",
-    "top_side.set_visible(False)\n",
-    "\n",
-    "ax1.text(400, np.mean(ax1.get_ylim()), \"%s\"%(TASK1), fontsize=26)\n",
-    "ax1.text(900, np.mean(ax1.get_ylim()), \"%s\"%(TASK2), fontsize=26)\n",
-    "\n",
-    "#####################################\n",
-    "with open('data/mean_xor_rxor.pickle','rb') as f:\n",
-    "    mean_error = pickle.load(f)\n",
-    "\n",
-    "algorithms = ['XOR Forest', 'R-XOR Forest', 'Lifelong Forest', 'Naive Forest']\n",
-    "\n",
-    "TASK1='XOR'\n",
-    "TASK2='R-XOR'\n",
-    "\n",
-    "ax1 = fig.add_subplot(gs[7:,7:13])\n",
-    "ax1.plot(n1s, mean_error[0,:len(n1s)], label=algorithms[0], c=colors[1], ls=ls[np.sum(0 > 1).astype(int)], lw=3)\n",
-    "ax1.plot(ns[len(n1s):], mean_error[2, len(n1s):], label=algorithms[1], c=colors[1], ls=ls[1], lw=3)\n",
-    "\n",
-    "ax1.plot(ns[len(n1s):], mean_error[3, len(n1s):], label=algorithms[2], c=colors[0], ls=ls[1], lw=3)\n",
-    "ax1.plot(ns[len(n1s):], mean_error[5, len(n1s):], label=algorithms[3], c='g', ls=ls[1], lw=3)\n",
-    "\n",
-    "ax1.set_ylabel('Generalization Error (%s)'%(TASK2), fontsize=fontsize)\n",
-    "ax1.legend(loc='upper right', fontsize=18, frameon=False)\n",
-    "ax1.set_xlabel('Total Sample Size', fontsize=fontsize)\n",
-    "ax1.tick_params(labelsize=labelsize)\n",
-    "ax1.set_xticks([250,750,1500])\n",
-    "ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle=\"dashed\")\n",
-    "\n",
-    "\n",
-    "right_side = ax1.spines[\"right\"]\n",
-    "right_side.set_visible(False)\n",
-    "top_side = ax1.spines[\"top\"]\n",
-    "top_side.set_visible(False)\n",
-    "\n",
-    "ax1.text(400, np.mean(ax1.get_ylim()), \"%s\"%(TASK1), fontsize=26)\n",
-    "ax1.text(900, np.mean(ax1.get_ylim()), \"%s\"%(TASK2), fontsize=26)\n",
-    "\n",
-    "ax1.set_title('R-XOR', fontsize=30)\n",
-    "\n",
-    "#####################################\n",
-    "with open('data/mean_te_xor_rxor.pickle','rb') as f:\n",
-    "    mean_te = pickle.load(f)\n",
-    "\n",
-    "algorithms = ['Lifelong BTE', 'Lifelong FTE', 'Naive BTE', 'Naive FTE']\n",
-    "\n",
-    "TASK1='XOR'\n",
-    "TASK2='R-XOR'\n",
-    "\n",
-    "ax1 = fig.add_subplot(gs[7:,14:])\n",
-    "\n",
-    "ax1.plot(ns, mean_te[0], label=algorithms[0], c=colors[0], ls=ls[0], lw=3)\n",
-    "ax1.plot(ns[len(n1s):], mean_te[1, len(n1s):], label=algorithms[1], c=colors[0], ls=ls[1], lw=3)\n",
-    "ax1.plot(ns, mean_te[2], label=algorithms[2], c='g', ls=ls[0], lw=3)\n",
-    "ax1.plot(ns[len(n1s):], mean_te[3, len(n1s):], label=algorithms[3], c='g', ls=ls[1], lw=3)\n",
-    "\n",
-    "ax1.set_ylabel('Forward/Backward \\n Transfer Efficiency (FTE/BTE)', fontsize=fontsize)\n",
-    "ax1.legend(loc='upper right', fontsize=20, frameon=False)\n",
-    "ax1.set_xlabel('Total Sample Size', fontsize=fontsize)\n",
-    "ax1.tick_params(labelsize=labelsize)\n",
-    "#ax1.set_yticks([0,.5,1,1.5])\n",
-    "ax1.set_xticks([250,750,1500])\n",
-    "ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle=\"dashed\")\n",
-    "right_side = ax1.spines[\"right\"]\n",
-    "right_side.set_visible(False)\n",
-    "top_side = ax1.spines[\"top\"]\n",
-    "top_side.set_visible(False)\n",
-    "ax1.hlines(1, 50,1500, colors='gray', linestyles='dashed',linewidth=1.5)\n",
-    "\n",
-    "ax1.text(400, np.mean(ax1.get_ylim()), \"%s\"%(TASK1), fontsize=26)\n",
-    "ax1.text(900, np.mean(ax1.get_ylim()), \"%s\"%(TASK2), fontsize=26)\n",
-    "\n",
-    "colors = sns.color_palette('Dark2', n_colors=2)\n",
-    "\n",
-    "X, Y = generate_gaussian_parity(750, angle_params=0)\n",
-    "Z, W = generate_gaussian_parity(750, angle_params=np.pi/4)\n",
-    "\n",
-    "ax = fig.add_subplot(gs[:6,4:10])\n",
-    "clr = [colors[i] for i in Y]\n",
-    "ax.scatter(X[:, 0], X[:, 1], c=clr, s=50)\n",
-    "\n",
-    "ax.set_xticks([])\n",
-    "ax.set_yticks([])\n",
-    "ax.set_title('Gaussian XOR', fontsize=30)\n",
-    "\n",
-    "plt.tight_layout()\n",
-    "ax.axis('off')\n",
-    "\n",
-    "colors = sns.color_palette('Dark2', n_colors=2)\n",
-    "\n",
-    "ax = fig.add_subplot(gs[:6,11:16])\n",
-    "clr = [colors[i] for i in W]\n",
-    "ax.scatter(Z[:, 0], Z[:, 1], c=clr, s=50)\n",
-    "\n",
-    "ax.set_xticks([])\n",
-    "ax.set_yticks([])\n",
-    "ax.set_title('Gaussian R-XOR', fontsize=30)\n",
-    "ax.axis('off')"
-   ]
-  },
-  {
-   "source": [
-    "# Experiment for Backward Transfer Efficiency (BTE) vs. angle of rotation for the second task"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "###main hyperparameters###\n",
-    "angle_sweep = range(0,90,1)\n",
-    "task1_sample = 100\n",
-    "task2_sample = 100\n",
-    "mc_rep = 10000\n",
-    "\n",
-    "mean_te = np.zeros(len(angle_sweep), dtype=float)\n",
-    "for ii,angle in enumerate(angle_sweep):\n",
-    "    error = np.array(\n",
-    "        Parallel(n_jobs=-1,verbose=1)(\n",
-    "        delayed(experiment)(\n",
-    "            task1_sample,task2_sample,\n",
-    "            task2_angle=angle*np.pi/180, \n",
-    "            max_depth=ceil(log2(task1_sample))\n",
-    "        ) for _ in range(mc_rep)\n",
-    "      )\n",
-    "    )\n",
-    "\n",
-    "    mean_te[ii] = np.mean(error[:,0])/np.mean(error[:,1])\n",
-    "\n",
-    "with open('./data/mean_angle_te.pickle','wb') as f:\n",
-    "    pickle.dump(mean_te,f)\n"
-   ]
-  },
-  {
-   "source": [
-    "# Plot the result"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "metadata": {},
-   "outputs": [
-    {
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "with open('./data/mean_angle_te.pickle','rb') as f:\n",
-    "    te = pickle.load(f)\n",
-    "angle_sweep = range(0,90,1)\n",
-    "\n",
-    "sns.set_context(\"talk\")\n",
-    "fig, ax = plt.subplots(1,1, figsize=(8,8))\n",
-    "ax.plot(angle_sweep,te,linewidth = 3)\n",
-    "ax.set_xticks(range(0,91,10))\n",
-    "ax.set_xlabel('Angle of Rotation (Degrees)')\n",
-    "ax.set_ylabel('Backward Transfer Efficiency (XOR)')\n",
-    "ax.hlines(1, 0,90, colors='gray', linestyles='dashed',linewidth=1.5)\n",
-    "\n",
-    "right_side = ax.spines[\"right\"]\n",
-    "right_side.set_visible(False)\n",
-    "top_side = ax.spines[\"top\"]\n",
-    "top_side.set_visible(False)\n"
-   ]
-  },
-  {
-   "source": [
-    "# Experiment for Backward Transfer Efficiency (BTE) vs. number of training samples for the second task"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stderr",
-     "text": "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    6.0s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    8.0s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   10.6s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   13.5s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.5s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    2.7s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    5.6s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    8.6s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.6s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    2.6s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    5.3s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    8.3s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.5s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    2.7s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    5.4s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    8.4s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.6s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    2.5s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    5.3s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    8.2s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.5s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    2.6s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    5.3s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    8.5s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.6s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    2.7s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    5.6s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    8.7s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.6s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    2.7s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    5.7s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    8.8s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.6s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    2.8s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    5.7s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    9.0s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.7s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    2.8s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    5.9s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    9.2s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.7s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    2.9s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    6.1s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    9.4s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.7s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    3.0s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    6.3s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    9.6s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.7s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    3.0s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    6.4s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:    9.9s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.8s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    3.2s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    6.7s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   10.4s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.7s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    3.3s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    7.0s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   10.9s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.8s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    3.6s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    7.4s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   11.5s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    1.0s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    3.7s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    8.0s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   12.4s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    0.9s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    4.0s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    8.4s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   13.2s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    1.0s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    4.3s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    9.1s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   14.1s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    1.1s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    4.4s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:    9.6s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   15.1s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    1.2s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    4.8s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   10.4s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   16.2s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    1.4s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    5.1s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   11.4s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   17.7s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    1.5s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    5.7s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   12.4s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   19.3s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    1.7s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    6.2s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   13.8s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   21.3s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    1.9s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    6.9s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   15.3s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   23.7s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    2.3s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    7.7s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   17.3s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   26.8s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    2.6s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:    8.8s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   19.8s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   30.6s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    3.1s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:   10.1s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   22.9s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   35.4s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    3.6s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:   11.8s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   26.7s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   41.1s finished\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 96 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   8 tasks      | elapsed:    4.2s\n[Parallel(n_jobs=-1)]: Done 258 tasks      | elapsed:   13.9s\n[Parallel(n_jobs=-1)]: Done 608 tasks      | elapsed:   31.7s\n[Parallel(n_jobs=-1)]: Done 1000 out of 1000 | elapsed:   48.7s finished\n"
-    }
-   ],
-   "source": [
-    "###main hyperparameters###\n",
-    "task2_sample_sweep = (2**np.arange(np.log2(60), np.log2(5010)+1, .25)).astype('int')\n",
-    "task1_sample = 500\n",
-    "task2_angle = 25*np.pi/180\n",
-    "mc_rep = 1000\n",
-    "\n",
-    "mean_te = np.zeros(len(task2_sample_sweep), dtype=float)\n",
-    "for ii,sample_no in enumerate(task2_sample_sweep):\n",
-    "    error = np.array(\n",
-    "        Parallel(n_jobs=-1,verbose=1)(\n",
-    "        delayed(experiment)(\n",
-    "            task1_sample,sample_no,\n",
-    "            task2_angle=task2_angle, \n",
-    "            max_depth=None\n",
-    "        ) for _ in range(mc_rep)\n",
-    "      )\n",
-    "    )\n",
-    "\n",
-    "    mean_te[ii] = np.mean(error[:,0])/np.mean(error[:,1])\n",
-    "\n",
-    "with open('./data/mean_sample_te.pickle','wb') as f:\n",
-    "    pickle.dump(mean_te,f)"
-   ]
-  },
-  {
-   "source": [
-    "# Plot the result"
-   ],
-   "cell_type": "markdown",
-   "metadata": {}
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "output_type": "display_data",
-     "data": {
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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
-     }
-    }
-   ],
-   "source": [
-    "with open('./data/mean_sample_te.pickle','rb') as f:\n",
-    "    te = pickle.load(f)\n",
-    "task2_sample_sweep = (2**np.arange(np.log2(60), np.log2(5010)+1, .25)).astype('int')\n",
-    "\n",
-    "sns.set_context(\"talk\")\n",
-    "fig, ax = plt.subplots(1,1, figsize=(8,8))\n",
-    "ax.plot(task2_sample_sweep,te,linewidth = 3)\n",
-    "ax.hlines(1, 60,5200, colors='gray', linestyles='dashed',linewidth=1.5)\n",
-    "ax.set_xscale('log')\n",
-    "ax.set_xticks([])\n",
-    "#ax.set_yticks([0.87,0.9,0.93])\n",
-    "ax.tick_params(labelsize=26)\n",
-    "ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())\n",
-    "ax.text(50, np.mean(ax.get_ylim())-.042, \"50\", fontsize=26)\n",
-    "ax.text(500, np.mean(ax.get_ylim())-.042, \"500\", fontsize=26)\n",
-    "ax.text(5000, np.mean(ax.get_ylim())-.042, \"5000\", fontsize=26)\n",
-    "\n",
-    "ax.text(50, np.mean(ax.get_ylim())-.046, \"Number of $25^\\circ$-RXOR Training Samples\", fontsize=24)\n",
-    "ax.set_ylabel('Backward Transfer Efficiency (XOR)',fontsize=24)\n",
-    "\n",
-    "right_side = ax.spines[\"right\"]\n",
-    "right_side.set_visible(False)\n",
-    "top_side = ax.spines[\"top\"]\n",
-    "top_side.set_visible(False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.9-final"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/experiments/parity_experiment/generate_paper_plot.py b/experiments/parity_experiment/generate_paper_plot.py
deleted file mode 100644
index c1457c0ab5..0000000000
--- a/experiments/parity_experiment/generate_paper_plot.py
+++ /dev/null
@@ -1,270 +0,0 @@
-#%%
-import random
-import matplotlib.pyplot as plt
-import tensorflow as tf
-import tensorflow.keras as keras
-import seaborn as sns 
-import matplotlib
-import numpy as np
-import pickle
-from proglearn.sims import generate_gaussian_parity
-
-from sklearn.model_selection import StratifiedKFold
-from math import log2, ceil 
-
-
-#%%
-def unpickle(file):
-    with open(file, 'rb') as fo:
-        dict = pickle.load(fo, encoding='bytes')
-    return dict
-
-def get_colors(colors, inds):
-    c = [colors[i] for i in inds]
-    return c
-
-#%%#%% Plotting the result
-#mc_rep = 50
-fontsize=30
-labelsize=28
-
-
-fig = plt.figure(constrained_layout=True,figsize=(21,30))
-gs = fig.add_gridspec(30, 21)
-
-colors = sns.color_palette('Dark2', n_colors=2)
-
-X, Y = generate_gaussian_parity(750)
-Z, W = generate_gaussian_parity(750, angle_params=np.pi/2)
-P, Q = generate_gaussian_parity(750, angle_params=np.pi/4)
-
-ax = fig.add_subplot(gs[:6,:6])
-ax.scatter(X[:, 0], X[:, 1], c=get_colors(colors, Y), s=50)
-
-ax.set_xticks([])
-ax.set_yticks([])
-ax.set_title('Gaussian XOR', fontsize=30)
-
-plt.tight_layout()
-ax.axis('off')
-#plt.savefig('./result/figs/gaussian-xor.pdf')
-
-#####################
-ax = fig.add_subplot(gs[:6,7:13])
-ax.scatter(Z[:, 0], Z[:, 1], c=get_colors(colors, W), s=50)
-
-ax.set_xticks([])
-ax.set_yticks([])
-ax.set_title('Gaussian N-XOR', fontsize=30)
-ax.axis('off')
-
-#####################
-ax = fig.add_subplot(gs[:6,14:20])
-ax.scatter(P[:, 0], P[:, 1], c=get_colors(colors, Q), s=50)
-
-ax.set_xticks([])
-ax.set_yticks([])
-ax.set_title('Gaussian R-XOR', fontsize=30)
-ax.axis('off')
-
-######################
-mean_error = unpickle('plots/mean_xor_nxor.pickle')
-
-n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)
-n_nxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)
-
-n1s = n_xor
-n2s = n_nxor
-
-ns = np.concatenate((n1s, n2s + n1s[-1]))
-ls=['-', '--']
-algorithms = ['XOR Forest', 'N-XOR Forest', 'Lifelong Forest', 'Naive Forest']
-
-
-TASK1='XOR'
-TASK2='N-XOR'
-
-fontsize=30
-labelsize=28
-
-colors = sns.color_palette("Set1", n_colors = 2)
-
-ax1 = fig.add_subplot(gs[7:13,2:9])
-# 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, mean_error[1], label=algorithms[2], c=colors[0], ls=ls[np.sum(1 > 1).astype(int)], lw=3)
-
-ax1.plot(ns, mean_error[4], label=algorithms[3], c='g', ls=ls[np.sum(1 > 1).astype(int)], lw=3)
-
-ax1.set_ylabel('Generalization Error (%s)'%(TASK1), fontsize=fontsize)
-ax1.legend(loc='upper left', fontsize=20, frameon=False)
-#ax1.set_ylim(0.09, 0.21)
-ax1.set_xlabel('Total Sample Size', fontsize=fontsize)
-ax1.tick_params(labelsize=labelsize)
-#ax1.set_yticks([0.1,0.15, 0.2])
-ax1.set_xticks([250,750,1500])
-#ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
-ax1.set_title('XOR', fontsize=30)
-
-right_side = ax1.spines["right"]
-right_side.set_visible(False)
-top_side = ax1.spines["top"]
-top_side.set_visible(False)
-
-ax1.text(400, np.mean(ax1.get_ylim()), "%s"%(TASK1), fontsize=26)
-ax1.text(900, np.mean(ax1.get_ylim()), "%s"%(TASK2), fontsize=26)
-
-#######################
-mean_error = unpickle('plots/mean_xor_nxor.pickle')
-
-algorithms = ['XOR Forest', 'N-XOR Forest', 'Lifelong Forest', 'Naive Forest']
-
-TASK1='XOR'
-TASK2='N-XOR'
-
-ax1 = fig.add_subplot(gs[7:13,12:19])
-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)
-
-ax1.set_ylabel('Generalization Error (%s)'%(TASK2), fontsize=fontsize)
-#ax1.legend(loc='upper left', fontsize=18, frameon=False)
-#         ax1.set_ylim(-0.01, 0.22)
-ax1.set_xlabel('Total Sample Size', fontsize=fontsize)
-ax1.tick_params(labelsize=labelsize)
-# ax1.set_yticks([0.15, 0.25, 0.35])
-#ax1.set_yticks([0.15, 0.2])
-ax1.set_xticks([250,750,1500])
-#ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
-
-#ax1.set_ylim(0.11, 0.21)
-
-right_side = ax1.spines["right"]
-right_side.set_visible(False)
-top_side = ax1.spines["top"]
-top_side.set_visible(False)
-
-# ax1.set_ylim(0.14, 0.36)
-ax1.text(400, np.mean(ax1.get_ylim()), "%s"%(TASK1), fontsize=26)
-ax1.text(900, np.mean(ax1.get_ylim()), "%s"%(TASK2), fontsize=26)
-
-ax1.set_title('N-XOR', fontsize=30)
-
-##################
-mean_te = unpickle('plots/mean_te_xor_nxor.pickle')
-algorithms = ['Lifelong BTE', 'Lifelong FTE', 'Naive BTE', 'Naive FTE']
-
-TASK1='XOR'
-TASK2='N-XOR'
-
-ax1 = fig.add_subplot(gs[15:21,2:9])
-
-ax1.plot(ns, mean_te[0], label=algorithms[0], c=colors[0], ls=ls[0], lw=3)
-
-ax1.plot(ns[len(n1s):], 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)
-
-ax1.set_ylabel('Forward/Backward \n Transfer Efficiency (FTE/BTE)', fontsize=fontsize)
-ax1.legend(loc='upper left', fontsize=20, frameon=False)
-#ax1.set_ylim(.99, 1.4)
-ax1.set_xlabel('Total Sample Size', fontsize=fontsize)
-ax1.tick_params(labelsize=labelsize)
-ax1.set_yticks([0,.5,1,1.5])
-ax1.set_xticks([250,750,1500])
-#ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
-right_side = ax1.spines["right"]
-right_side.set_visible(False)
-top_side = ax1.spines["top"]
-top_side.set_visible(False)
-ax1.hlines(1, 50,1500, colors='gray', linestyles='dashed',linewidth=1.5)
-
-ax1.text(400, np.mean(ax1.get_ylim()), "%s"%(TASK1), fontsize=26)
-ax1.text(900, np.mean(ax1.get_ylim()), "%s"%(TASK2), fontsize=26)
-
-######################
-mean_te = unpickle('plots/mean_te_xor_rxor.pickle')
-algorithms = ['Lifelong BTE', 'Lifelong FTE', 'Naive BTE', 'Naive FTE']
-
-TASK1='XOR'
-TASK2='R-XOR'
-
-ax1 = fig.add_subplot(gs[15:21,12:19])
-
-ax1.plot(ns, mean_te[0], label=algorithms[0], c=colors[0], ls=ls[0], lw=3)
-
-ax1.plot(ns[len(n1s):], 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)
-
-ax1.set_ylabel('Forward/Backward \n Transfer Efficiency (FTE/BTE)', fontsize=fontsize)
-#ax1.legend(loc='upper left', fontsize=20, frameon=False)
-#ax1.set_ylim(.99, 1.4)
-ax1.set_xlabel('Total Sample Size', fontsize=fontsize)
-ax1.tick_params(labelsize=labelsize)
-ax1.set_yticks([0,.5,1])
-ax1.set_xticks([250,750,1500])
-#ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
-right_side = ax1.spines["right"]
-right_side.set_visible(False)
-top_side = ax1.spines["top"]
-top_side.set_visible(False)
-ax1.hlines(1, 50,1500, colors='gray', linestyles='dashed',linewidth=1.5)
-
-ax1.text(400, np.mean(ax1.get_ylim()), "%s"%(TASK1), fontsize=26)
-ax1.text(900, np.mean(ax1.get_ylim()), "%s"%(TASK2), fontsize=26)
-
-########################################################
-ax = fig.add_subplot(gs[23:29,2:9])
-with open('plots/mean_angle_te.pickle','rb') as f:
-    te = pickle.load(f)
-angle_sweep = range(0,90,1)
-
-ax.plot(angle_sweep,te,c='r',linewidth = 3)
-ax.set_xticks(range(0,91,15))
-ax.tick_params(labelsize=labelsize)
-ax.set_xlabel('Angle of Rotation (Degrees)', fontsize=fontsize)
-ax.set_ylabel('Backward Transfer Efficiency (XOR)', fontsize=fontsize)
-#ax.set_title("XOR vs. Rotated-XOR", fontsize = fontsize)
-ax.hlines(1,0,90, colors='grey', linestyles='dashed',linewidth=1.5)
-
-right_side = ax.spines["right"]
-right_side.set_visible(False)
-top_side = ax.spines["top"]
-top_side.set_visible(False)
-
-#####################################
-ax = fig.add_subplot(gs[23:29,12:19])
-
-with open('plots/mean_sample_te.pickle','rb') as f:
-    te = pickle.load(f)
-task2_sample_sweep = (2**np.arange(np.log2(60), np.log2(5010)+1, .25)).astype('int')
-
-ax.plot(task2_sample_sweep,te,c='r',linewidth = 3)
-ax.hlines(1, 60,5200, colors='gray', linestyles='dashed',linewidth=1.5)
-ax.set_xscale('log')
-ax.set_xticks([])
-ax.set_yticks([0.98,1,1.02,1.04])
-ax.tick_params(labelsize=26)
-ax.get_xaxis().set_major_formatter(matplotlib.ticker.ScalarFormatter())
-ax.text(50, np.mean(ax.get_ylim())-.042, "50", fontsize=labelsize)
-ax.text(500, np.mean(ax.get_ylim())-.042, "500", fontsize=labelsize)
-ax.text(5000, np.mean(ax.get_ylim())-.042, "5000", fontsize=labelsize)
-
-ax.text(50, np.mean(ax.get_ylim())-.047, "Number of $25^\circ$-RXOR Training Samples", fontsize=fontsize-4)
-ax.set_ylabel('Backward Transfer Efficiency (XOR)',fontsize=24)
-
-right_side = ax.spines["right"]
-right_side.set_visible(False)
-top_side = ax.spines["top"]
-top_side.set_visible(False)
-
-
-plt.savefig('./plots/parity_exp.pdf')
-# %%
diff --git a/experiments/parity_experiment/plots/parity_exp.pdf b/experiments/parity_experiment/plots/parity_exp.pdf
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diff --git a/experiments/rotation_xor/bte_90/experiment.py b/experiments/rotation_xor/bte_90/experiment.py
new file mode 100644
index 0000000000..f5bed49ca4
--- /dev/null
+++ b/experiments/rotation_xor/bte_90/experiment.py
@@ -0,0 +1,97 @@
+import matplotlib.pyplot as plt
+import random
+import pickle
+from skimage.transform import rotate
+from scipy import ndimage
+from skimage.util import img_as_ubyte
+from joblib import Parallel, delayed
+from sklearn.ensemble.forest import _generate_unsampled_indices
+from sklearn.ensemble.forest import _generate_sample_indices
+import numpy as np
+from sklearn.ensemble import BaggingClassifier
+from sklearn.tree import DecisionTreeClassifier
+from itertools import product
+
+from joblib import Parallel, delayed
+from multiprocessing import Pool
+
+from proglearn.progressive_learner import ProgressiveLearner
+from proglearn.deciders import SimpleArgmaxAverage
+from proglearn.transformers import TreeClassificationTransformer, NeuralClassificationTransformer
+from proglearn.voters import TreeClassificationVoter, KNNClassificationVoter
+
+def generate_gaussian_parity(n, cov_scale=1, angle_params=None, k=1, acorn=None):
+    means = [[-1, -1], [-1, 1], [1, -1], [1, 1]]
+    blob = np.concatenate([np.random.multivariate_normal(mean, cov_scale * np.eye(len(mean)),
+                                                 size=int(n / 4)) for mean in means])
+
+    X = np.zeros_like(blob)
+    Y = np.logical_xor(blob[:, 0] > 0, blob[:, 1] > 0)
+    X[:, 0] = blob[:, 0] * np.cos(angle_params * np.pi / 180) + blob[:, 1] * np.sin(angle_params * np.pi / 180)
+    X[:, 1] = -blob[:, 0] * np.sin(angle_params * np.pi / 180) + blob[:, 1] * np.cos(angle_params * np.pi / 180)
+    return X, Y.astype(int)
+
+
+def LF_experiment(angle, reps=1, ntrees=10, acorn=None):
+
+    errors = np.zeros(2)
+
+    for rep in range(reps):
+        print("Starting Rep {} of Angle {}".format(rep, angle))
+        X_base_train, y_base_train = generate_gaussian_parity(n = 100, angle_params = 0, acorn=rep)
+        X_base_test, y_base_test = generate_gaussian_parity(n = 10000, angle_params = 0, acorn=rep)
+        X_rotated_train, y_rotated_train = generate_gaussian_parity(n = 100, angle_params = angle, acorn=rep)
+
+
+        default_transformer_class = TreeClassificationTransformer
+        default_transformer_kwargs = {"kwargs" : {"max_depth" : 10}}
+
+        default_voter_class = TreeClassificationVoter
+        default_voter_kwargs = {}
+
+        default_decider_class = SimpleArgmaxAverage
+        default_decider_kwargs = {}
+        progressive_learner = ProgressiveLearner(default_transformer_class = default_transformer_class,
+                                             default_transformer_kwargs = default_transformer_kwargs,
+                                             default_voter_class = default_voter_class,
+                                             default_voter_kwargs = default_voter_kwargs,
+                                             default_decider_class = default_decider_class)
+        progressive_learner.add_task(
+            X_base_train,
+            y_base_train,
+            num_transformers = ntrees,
+            transformer_voter_decider_split = [0.67, 0.33, 0],
+            decider_kwargs = {"classes" : np.unique(y_base_train)}
+        )
+        base_predictions_test = progressive_learner.predict(X_base_test, task_id=0)
+        progressive_learner.add_transformer(
+            X = X_rotated_train,
+            y = y_rotated_train,
+            transformer_data_proportion = 1,
+            num_transformers = 10,
+            backward_task_ids = [0]
+        )
+
+        all_predictions_test=progressive_learner.predict(X_base_test, task_id=0)
+
+        errors[1] = errors[1]+(1 - np.mean(all_predictions_test == y_base_test))
+        errors[0] = errors[0]+(1 - np.mean(base_predictions_test == y_base_test))
+
+
+    errors = errors/reps
+    print("Errors For Angle {}: {}".format(angle, errors))
+    with open('results/angle_'+str(angle)+'.pickle', 'wb') as f:
+        pickle.dump(errors, f, protocol = 2)
+
+
+### MAIN HYPERPARAMS ###
+granularity = 1
+reps = 10
+########################
+
+
+def perform_angle(angle):
+    LF_experiment(angle, reps=reps, ntrees=10)
+
+angles = np.arange(0,90 + granularity,granularity)
+Parallel(n_jobs=-1, verbose = 1)(delayed(LF_experiment)(angle, reps=reps, ntrees=10) for angle in angles)
diff --git a/experiments/rotation_xor/figs/RXOR_suite.pdf b/experiments/rotation_xor/figs/RXOR_suite.pdf
new file mode 100644
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diff --git a/experiments/rotation_xor/plot.py b/experiments/rotation_xor/plot.py
new file mode 100644
index 0000000000..6fbaaaa8ab
--- /dev/null
+++ b/experiments/rotation_xor/plot.py
@@ -0,0 +1,82 @@
+#%%
+import pickle
+import matplotlib.pyplot as plt
+from matplotlib import rcParams
+rcParams.update({'figure.autolayout': True})
+import numpy as np
+from itertools import product
+import seaborn as sns
+from matplotlib.pyplot import cm
+
+#%%
+def unpickle(file):
+    with open(file, 'rb') as fo:
+        dict = pickle.load(fo, encoding='bytes')
+    return dict
+
+fig, ax = plt.subplots(1,2, figsize=(18, 8))
+
+alg_name = ['L2F']
+angles = np.arange(0,91,1)
+tes = [[] for _ in range(len(alg_name))]
+
+for algo_no,alg in enumerate(alg_name):
+    for angle in angles:
+        orig_error, transfer_error = pickle.load(
+                open("bte_90/results/angle_" + str(angle) + ".pickle", "rb")
+                )
+        tes[algo_no].append(orig_error / transfer_error)
+
+# %%
+clr = ["#e41a1c"]
+c = sns.color_palette(clr, n_colors=len(clr))
+
+for alg_no,alg in enumerate(alg_name):
+    if alg_no<2:
+        ax[0].plot(angles,tes[alg_no], c=c[alg_no], label=alg_name[alg_no], linewidth=3)
+    else:
+        ax[0].plot(angles,tes[alg_no], c=c[alg_no], label=alg_name[alg_no])
+
+
+ax[0].set_xticks(range(0, 90 + 15, 15))
+ax[0].tick_params(labelsize=25)
+ax[0].set_xlabel('Angle of Rotation (Degrees)', fontsize=24)
+ax[0].set_ylabel('Backward Transfer Efficiency (XOR)', fontsize=24)
+ax[0].hlines(1,0,90, colors='grey', linestyles='dashed',linewidth=1.5)
+
+
+
+#%%
+te_ra = []
+n1_ra = range(10, 6000, 50)
+for n1 in n1_ra:
+    te_across_reps = []
+    for rep in range(500):
+        filename = 'te_exp/result/'+str(n1)+'_'+str(rep)+'.pickle'
+        df = unpickle(filename)
+        te_across_reps.append(float(df['te']))
+    te_ra.append(np.mean(te_across_reps))
+
+
+#%%
+sns.set()
+
+fontsize=22
+ticksize=20
+
+ax[1].plot(n1_ra, te_ra, c="#e41a1c", linewidth = 2.6)
+ax[1].set_xticks([100, 1000, 2500, 5000])
+ax[1].tick_params(labelsize=25)
+ax[1].hlines(1, 1, max(n1_ra), colors='grey', linestyles='dashed',linewidth=1.5)
+ax[1].set_xlabel(r'Number of $10^\circ$-XOR Training Samples', fontsize=24)
+ax[1].set_ylabel('Backward Transfer Efficiency (XOR)', fontsize=24)
+
+for a in ax:
+    right_side = a.spines["right"]
+    right_side.set_visible(False)
+    top_side = a.spines["top"]
+    top_side.set_visible(False)
+plt.tight_layout()
+
+plt.savefig('figs/RXOR_suite.pdf')
+# %%
diff --git a/experiments/rotation_xor/te_exp/fte_bte_exp.py b/experiments/rotation_xor/te_exp/fte_bte_exp.py
new file mode 100644
index 0000000000..a2fdad812d
--- /dev/null
+++ b/experiments/rotation_xor/te_exp/fte_bte_exp.py
@@ -0,0 +1,100 @@
+#%%
+import random
+import matplotlib.pyplot as plt
+from itertools import product
+import pandas as pd
+
+import numpy as np
+import pickle
+
+from sklearn.model_selection import StratifiedKFold
+from math import log2, ceil
+
+from proglearn.progressive_learner import ProgressiveLearner
+from proglearn.deciders import SimpleArgmaxAverage
+from proglearn.transformers import TreeClassificationTransformer, NeuralClassificationTransformer
+from proglearn.voters import TreeClassificationVoter, KNNClassificationVoter
+
+from joblib import Parallel, delayed
+from multiprocessing import Pool
+
+#%%
+def LF_experiment(num_task_1_data, rep):
+
+    default_transformer_class = TreeClassificationTransformer
+    default_transformer_kwargs = {"kwargs" : {"max_depth" : 30}}
+
+    default_voter_class = TreeClassificationVoter
+    default_voter_kwargs = {}
+
+    default_decider_class = SimpleArgmaxAverage
+    default_decider_kwargs = {}
+    progressive_learner = ProgressiveLearner(default_transformer_class = default_transformer_class,
+                                         default_transformer_kwargs = default_transformer_kwargs,
+                                         default_voter_class = default_voter_class,
+                                         default_voter_kwargs = default_voter_kwargs,
+                                         default_decider_class = default_decider_class)
+
+    X_train_task0, y_train_task0 = generate_gaussian_parity(n = num_task_1_data, angle_params = 0, acorn=1)
+    X_train_task1, y_train_task1 = generate_gaussian_parity(n = 100, angle_params = 10, acorn=1)
+    X_test_task0, y_test_task0 = generate_gaussian_parity(n = 10000, angle_params = 0, acorn=2)
+
+
+    progressive_learner.add_task(
+        X_train_task0,
+        y_train_task0,
+        num_transformers = 10,
+        transformer_voter_decider_split = [0.67, 0.33, 0],
+        decider_kwargs = {"classes" : np.unique(y_train_task0)}
+        )
+    llf_task=progressive_learner.predict(X_test_task0, task_id=0)
+    single_task_accuracy = np.nanmean(llf_task == y_test_task0)
+    single_task_error = 1 - single_task_accuracy
+
+    progressive_learner.add_transformer(
+            X = X_train_task1,
+            y = y_train_task1,
+            transformer_data_proportion = 1,
+            num_transformers = 10,
+            backward_task_ids = [0]
+        )
+
+    llf_task=progressive_learner.predict(X_test_task0, task_id=0)
+    double_task_accuracy = np.nanmean(llf_task == y_test_task0)
+    double_task_error = 1 - double_task_accuracy
+
+    if double_task_error == 0 or single_task_error == 0:
+        te = 1
+    else:
+        te = (single_task_error + 1e-6) / (double_task_error + 1e-6)
+
+    df = pd.DataFrame()
+    df['te'] = [te]
+
+    print('n = {}, te = {}'.format(num_task_1_data, te))
+    file_to_save = 'result/'+str(num_task_1_data)+'_'+str(rep)+'.pickle'
+    with open(file_to_save, 'wb') as f:
+        pickle.dump(df, f)
+
+#%%
+def generate_gaussian_parity(n, cov_scale=1, angle_params=None, k=1, acorn=None):
+    means = [[-1, -1], [-1, 1], [1, -1], [1, 1]]
+    blob = np.concatenate([np.random.multivariate_normal(mean, cov_scale * np.eye(len(mean)),
+                                                 size=int(n / 4)) for mean in means])
+
+    X = np.zeros_like(blob)
+    Y = np.logical_xor(blob[:, 0] > 0, blob[:, 1] > 0)
+    X[:, 0] = blob[:, 0] * np.cos(angle_params * np.pi / 180) + blob[:, 1] * np.sin(angle_params * np.pi / 180)
+    X[:, 1] = -blob[:, 0] * np.sin(angle_params * np.pi / 180) + blob[:, 1] * np.cos(angle_params * np.pi / 180)
+    return X, Y.astype(int)
+
+#%%
+num_task_1_data_ra=(2**np.arange(np.log2(60), np.log2(5010)+1, .25)).astype('int')
+reps = range(1000)
+iterable = product(num_task_1_data_ra, reps)
+Parallel(n_jobs=-1,verbose=1)(delayed(LF_experiment)(num_task_1_data, rep) for num_task_1_data, rep in iterable)
+
+
+
+
+# %%
diff --git a/experiments/xor_nxor_exp/control_exp.py b/experiments/xor_nxor_exp/control_exp.py
new file mode 100644
index 0000000000..fd952ed530
--- /dev/null
+++ b/experiments/xor_nxor_exp/control_exp.py
@@ -0,0 +1,174 @@
+#%%
+import random
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import tensorflow.keras as keras
+
+import numpy as np
+import pickle
+
+from sklearn.model_selection import StratifiedKFold
+from math import log2, ceil 
+
+import sys
+sys.path.append("../../src/")
+from lifelong_dnn import LifeLongDNN
+from joblib import Parallel, delayed
+
+# %%
+def unpickle(file):
+    with open(file, 'rb') as fo:
+        dict = pickle.load(fo, encoding='bytes')
+    return dict
+
+def get_colors(colors, inds):
+    c = [colors[i] for i in inds]
+    return c
+
+def generate_2d_rotation(theta=0, acorn=None):
+    if acorn is not None:
+        np.random.seed(acorn)
+    
+    R = np.array([
+        [np.cos(theta), np.sin(theta)],
+        [-np.sin(theta), np.cos(theta)]
+    ])
+    
+    return R
+
+
+def generate_gaussian_parity(n, mean=np.array([-1, -1]), cov_scale=1, angle_params=None, k=1, acorn=None):
+    if acorn is not None:
+        np.random.seed(acorn)
+        
+    d = len(mean)
+    
+    if mean[0] == -1 and mean[1] == -1:
+        mean = mean + 1 / 2**k
+    
+    mnt = np.random.multinomial(n, 1/(4**k) * np.ones(4**k))
+    cumsum = np.cumsum(mnt)
+    cumsum = np.concatenate(([0], cumsum))
+    
+    Y = np.zeros(n)
+    X = np.zeros((n, d))
+    
+    for i in range(2**k):
+        for j in range(2**k):
+            temp = np.random.multivariate_normal(mean, cov_scale * np.eye(d), 
+                                                 size=mnt[i*(2**k) + j])
+            temp[:, 0] += i*(1/2**(k-1))
+            temp[:, 1] += j*(1/2**(k-1))
+            
+            X[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = temp
+            
+            if i % 2 == j % 2:
+                Y[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = 0
+            else:
+                Y[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = 1
+                
+    if d == 2:
+        if angle_params is None:
+            angle_params = np.random.uniform(0, 2*np.pi)
+            
+        R = generate_2d_rotation(angle_params)
+        X = X @ R
+        
+    else:
+        raise ValueError('d=%i not implemented!'%(d))
+       
+    return X, Y.astype(int)
+
+
+# %%
+def exp(n_sample, n_test, angle_params, n_trees, reps, acorn=None):
+    
+    if acorn != None:
+        np.random.seed(acorn)
+        
+    error = np.zeros(reps,dtype=float)
+    
+    for i in range(reps):
+        train, label = generate_gaussian_parity(n_sample,cov_scale=0.1,angle_params=angle_params)
+        test, test_label = generate_gaussian_parity(n_test,cov_scale=0.1,angle_params=angle_params)
+    
+        l2f = LifeLongDNN()
+        l2f.new_forest(train, label, n_estimators=n_trees, max_samples=ceil(log2(n_sample)))
+    
+        uf_task = l2f.predict(test, representation=0, decider=0)
+        error[i] = 1 - np.sum(uf_task == test_label)/n_test
+    
+    return np.mean(error,axis=0), np.std(error,ddof=1,axis=0)
+
+#%%
+n_trees = range(1,50,1)
+n_test = 1000
+n_sample = 1500
+reps = 20
+error1 = np.zeros(len(n_trees),dtype=float)
+error2 = np.zeros(len(n_trees),dtype=float)
+
+'''for count,n_tree in enumerate(n_trees):
+    print(count)
+    error1[count],_ = exp(n_sample,n_test,angle_params=0,n_trees=n_tree,reps=reps)
+
+for count,n_tree in enumerate(n_trees):
+    error2[count],_ = exp(n_sample,n_test,angle_params=np.pi/4,n_trees=n_tree,reps=reps)'''
+
+error1 = np.array(
+                Parallel(n_jobs=-2,verbose=1)(
+                delayed(exp)(n_sample,n_test,angle_params=0,n_trees=n_tree,reps=reps) for n_tree in n_trees
+                )
+            )
+
+error2 = np.array(
+                Parallel(n_jobs=-2,verbose=1)(
+                delayed(exp)(n_sample,n_test,angle_params=np.pi/4,n_trees=n_tree,reps=reps) for n_tree in n_trees
+                )
+            )
+    
+with open('./result/control_xor.pickle','wb') as f:
+    pickle.dump(error1,f)
+    
+with open('./result/control_nxor.pickle','wb') as f:
+    pickle.dump(error2,f)
+    
+#%% plotting the results
+n_trees = np.arange(1,50,1)
+
+tmp1 = unpickle('./result/control_xor.pickle')
+tmp2 = unpickle('./result/control_nxor.pickle')
+
+err1 = np.zeros(len(n_trees),dtype=float)
+err2 = np.zeros(len(n_trees),dtype=float)
+
+for i in range(len(n_trees)):
+    err1[i] = 1-tmp1[i][0]
+    err2[i] = 1-tmp2[i][0]
+
+
+fig, ax = plt.subplots(1,2, figsize=(26,8))
+ax[0].plot(n_trees, err1, marker='.', markersize=14, linewidth=3)
+ax[1].plot(n_trees, err2, marker='.', markersize=14, linewidth=3)
+
+ax[0].set_title('XOR',fontsize=30)
+ax[0].set_xlabel('Accuracy')
+ax[0].set_ylabel('Accuracy', fontsize=30)
+ax[0].set_xlabel('Number of trees', fontsize=30)
+ax[0].tick_params(labelsize=27.5)
+#ax.set_xticks(rotation=90)
+
+ax[1].set_title('RXOR',fontsize=30)
+ax[1].set_xlabel('Accuracy')
+ax[1].set_ylabel('Accuracy', fontsize=30)
+ax[1].set_xlabel('Number of trees', fontsize=30)
+ax[1].tick_params(labelsize=27.5)
+
+for i in range(1,50,10):
+    ax[0].axvline(x = i, linewidth=1.5,alpha=0.5, color='k')
+    ax[1].axvline(x = i, linewidth=1.5,alpha=0.5, color='k')
+    
+plt.savefig('./result/control_xor_rxor.png',dpi=500)
+plt.savefig('./result/control_xor_rxor.pdf',dpi=500)
+
+# %%
diff --git a/experiments/xor_nxor_exp/experiment.py b/experiments/xor_nxor_exp/experiment.py
new file mode 100644
index 0000000000..679ef8c75f
--- /dev/null
+++ b/experiments/xor_nxor_exp/experiment.py
@@ -0,0 +1,425 @@
+#%%
+import random
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import tensorflow.keras as keras
+import seaborn as sns
+
+import numpy as np
+import pickle
+
+from sklearn.model_selection import StratifiedKFold
+from math import log2, ceil
+
+import sys
+
+from proglearn.progressive_learner import ProgressiveLearner
+from proglearn.deciders import SimpleArgmaxAverage
+from proglearn.transformers import TreeClassificationTransformer, NeuralClassificationTransformer
+from proglearn.voters import TreeClassificationVoter, KNNClassificationVoter
+
+from joblib import Parallel, delayed
+
+#%%
+def unpickle(file):
+    with open(file, 'rb') as fo:
+        dict = pickle.load(fo, encoding='bytes')
+    return dict
+
+def get_colors(colors, inds):
+    c = [colors[i] for i in inds]
+    return c
+
+def generate_2d_rotation(theta=0, acorn=None):
+    if acorn is not None:
+        np.random.seed(acorn)
+
+    R = np.array([
+        [np.cos(theta), np.sin(theta)],
+        [-np.sin(theta), np.cos(theta)]
+    ])
+
+    return R
+
+
+def generate_gaussian_parity(n, mean=np.array([-1, -1]), cov_scale=1, angle_params=None, k=1, acorn=None):
+    if acorn is not None:
+        np.random.seed(acorn)
+
+    d = len(mean)
+
+    if mean[0] == -1 and mean[1] == -1:
+        mean = mean + 1 / 2**k
+
+    mnt = np.random.multinomial(n, 1/(4**k) * np.ones(4**k))
+    cumsum = np.cumsum(mnt)
+    cumsum = np.concatenate(([0], cumsum))
+
+    Y = np.zeros(n)
+    X = np.zeros((n, d))
+
+    for i in range(2**k):
+        for j in range(2**k):
+            temp = np.random.multivariate_normal(mean, cov_scale * np.eye(d),
+                                                 size=mnt[i*(2**k) + j])
+            temp[:, 0] += i*(1/2**(k-1))
+            temp[:, 1] += j*(1/2**(k-1))
+
+            X[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = temp
+
+            if i % 2 == j % 2:
+                Y[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = 0
+            else:
+                Y[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = 1
+
+    if d == 2:
+        if angle_params is None:
+            angle_params = np.random.uniform(0, 2*np.pi)
+
+        R = generate_2d_rotation(angle_params)
+        X = X @ R
+
+    else:
+        raise ValueError('d=%i not implemented!'%(d))
+
+    return X, Y.astype(int)
+
+
+#%%
+def experiment(n_xor, n_nxor, n_test, reps, n_trees, max_depth, acorn=None):
+    #print(1)
+    if n_xor==0 and n_nxor==0:
+        raise ValueError('Wake up and provide samples to train!!!')
+
+    if acorn != None:
+        np.random.seed(acorn)
+
+    errors = np.zeros((reps,4),dtype=float)
+
+    for i in range(reps):
+        default_transformer_class = TreeClassificationTransformer
+        default_transformer_kwargs = {"kwargs" : {"max_depth" : max_depth}}
+
+        default_voter_class = TreeClassificationVoter
+        default_voter_kwargs = {}
+
+        default_decider_class = SimpleArgmaxAverage
+        default_decider_kwargs = {"classes" : np.arange(2)}
+        progressive_learner = ProgressiveLearner(default_transformer_class = default_transformer_class,
+                                             default_transformer_kwargs = default_transformer_kwargs,
+                                             default_voter_class = default_voter_class,
+                                             default_voter_kwargs = default_voter_kwargs,
+                                             default_decider_class = default_decider_class,
+                                             default_decider_kwargs = default_decider_kwargs)
+        uf = ProgressiveLearner(default_transformer_class = default_transformer_class,
+                                             default_transformer_kwargs = default_transformer_kwargs,
+                                             default_voter_class = default_voter_class,
+                                             default_voter_kwargs = default_voter_kwargs,
+                                             default_decider_class = default_decider_class,
+                                             default_decider_kwargs = default_decider_kwargs)
+        #source data
+        xor, label_xor = generate_gaussian_parity(n_xor,cov_scale=0.1,angle_params=0)
+        test_xor, test_label_xor = generate_gaussian_parity(n_test,cov_scale=0.1,angle_params=0)
+
+        #target data
+        nxor, label_nxor = generate_gaussian_parity(n_nxor,cov_scale=0.1,angle_params=np.pi/2)
+        test_nxor, test_label_nxor = generate_gaussian_parity(n_test,cov_scale=0.1,angle_params=np.pi/2)
+
+        if n_xor == 0:
+            progressive_learner.add_task(nxor, label_nxor, num_transformers=n_trees)
+
+            errors[i,0] = 0.5
+            errors[i,1] = 0.5
+
+            uf_task2=progressive_learner.predict(test_nxor, transformer_ids=[0], task_id=0)
+            l2f_task2=progressive_learner.predict(test_nxor, task_id=0)
+
+            errors[i,2] = 1 - np.sum(uf_task2 == test_label_nxor)/n_test
+            errors[i,3] = 1 - np.sum(l2f_task2 == test_label_nxor)/n_test
+        elif n_nxor == 0:
+            progressive_learner.add_task(xor, label_xor, num_transformers=n_trees)
+
+            uf_task1=progressive_learner.predict(test_xor, transformer_ids=[0], task_id=0)
+            l2f_task1=progressive_learner.predict(test_xor, task_id=0)
+
+            errors[i,0] = 1 - np.sum(uf_task1 == test_label_xor)/n_test
+            errors[i,1] = 1 - np.sum(l2f_task1 == test_label_xor)/n_test
+            errors[i,2] = 0.5
+            errors[i,3] = 0.5
+        else:
+            progressive_learner.add_task(xor, label_xor, num_transformers=n_trees)
+            progressive_learner.add_task(nxor, label_nxor, num_transformers=n_trees)
+
+            uf.add_task(xor, label_xor, num_transformers=2*n_trees)
+            uf.add_task(nxor, label_nxor, num_transformers=2*n_trees)
+
+            uf_task1=uf.predict(test_xor, transformer_ids=[0], task_id=0)
+            l2f_task1=progressive_learner.predict(test_xor, task_id=0)
+            uf_task2=uf.predict(test_nxor, transformer_ids=[1], task_id=1)
+            l2f_task2=progressive_learner.predict(test_nxor, task_id=1)
+
+            errors[i,0] = 1 - np.sum(uf_task1 == test_label_xor)/n_test
+            errors[i,1] = 1 - np.sum(l2f_task1 == test_label_xor)/n_test
+            errors[i,2] = 1 - np.sum(uf_task2 == test_label_nxor)/n_test
+            errors[i,3] = 1 - np.sum(l2f_task2 == test_label_nxor)/n_test
+
+    return np.mean(errors,axis=0)
+
+#%%
+mc_rep = 1000
+n_test = 1000
+n_trees = 10
+n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)
+n_nxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)
+
+mean_error = np.zeros((4, len(n_xor)+len(n_nxor)))
+std_error = np.zeros((4, len(n_xor)+len(n_nxor)))
+
+mean_te = np.zeros((2, len(n_xor)+len(n_nxor)))
+std_te = np.zeros((2, len(n_xor)+len(n_nxor)))
+
+for i,n1 in enumerate(n_xor):
+    print('starting to compute %s xor\n'%n1)
+    error = np.array(
+        Parallel(n_jobs=-1,verbose=1)(
+        delayed(experiment)(n1,0,n_test,1,n_trees=n_trees,max_depth=ceil(log2(750))) for _ in range(mc_rep)
+    )
+    )
+    mean_error[:,i] = np.mean(error,axis=0)
+    std_error[:,i] = np.std(error,ddof=1,axis=0)
+    mean_te[0,i] = np.mean(error[:,0]/error[:,1])
+    mean_te[1,i] = np.mean(error[:,2]/error[:,3])
+    std_te[0,i] = np.std(error[:,0]/error[:,1],ddof=1)
+    std_te[1,i] = np.std(error[:,2]/error[:,3],ddof=1)
+
+    if n1==n_xor[-1]:
+        for j,n2 in enumerate(n_nxor):
+            print('starting to compute %s nxor\n'%n2)
+
+            error = np.array(
+                Parallel(n_jobs=40,verbose=1)(
+                delayed(experiment)(n1,n2,n_test,1,n_trees=n_trees,max_depth=ceil(log2(750))) for _ in range(mc_rep)
+            )
+            )
+            mean_error[:,i+j+1] = np.mean(error,axis=0)
+            std_error[:,i+j+1] = np.std(error,ddof=1,axis=0)
+            mean_te[0,i+j+1] = np.mean(error[:,0]/error[:,1])
+            mean_te[1,i+j+1] = np.mean(error[:,2]/error[:,3])
+            std_te[0,i+j+1] = np.std(error[:,0]/error[:,1],ddof=1)
+            std_te[1,i+j+1] = np.std(error[:,2]/error[:,3],ddof=1)
+
+with open('./result/mean_xor_nxor.pickle','wb') as f:
+    pickle.dump(mean_error,f)
+
+with open('./result/std_xor_nxor.pickle','wb') as f:
+     pickle.dump(std_error,f)
+
+with open('./result/mean_te_xor_nxor.pickle','wb') as f:
+     pickle.dump(mean_te,f)
+
+with open('./result/std_te_xor_nxor.pickle','wb') as f:
+     pickle.dump(std_te,f)
+
+#%% Plotting the result
+#mc_rep = 50
+mean_error = unpickle('result/mean_xor_nxor.pickle')
+std_error = unpickle('result/std_xor_nxor.pickle')
+
+n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)
+n_nxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)
+
+n1s = n_xor
+n2s = n_nxor
+
+ns = np.concatenate((n1s, n2s + n1s[-1]))
+ls=['-', '--']
+algorithms = ['Uncertainty Forest', 'Lifelong Forest']
+
+
+TASK1='XOR'
+TASK2='N-XOR'
+
+fontsize=35
+labelsize=27.5
+
+colors = sns.color_palette("Set1", n_colors = 2)
+
+fig1 = plt.figure(figsize=(8,8))
+ax1 = fig1.add_subplot(1,1,1)
+# for i, algo in enumerate(algorithms):
+ax1.plot(ns, mean_error[0], label=algorithms[0], c=colors[1], ls=ls[np.sum(0 > 1).astype(int)], lw=3)
+#ax1.fill_between(ns,
+#        mean_error[0] + 1.96*std_error[0],
+#        mean_error[0] - 1.96*std_error[0],
+#        where=mean_error[0] + 1.96*std_error[0] >= mean_error[0] - 1.96*std_error[0],
+#        facecolor=colors[1],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.plot(ns, mean_error[1], label=algorithms[1], c=colors[0], ls=ls[np.sum(1 > 1).astype(int)], lw=3)
+#ax1.fill_between(ns,
+#        mean_error[1] + 1.96*std_error[1, ],
+#        mean_error[1] - 1.96*std_error[1, ],
+#        where=mean_error[1] + 1.96*std_error[1] >= mean_error[1] - 1.96*std_error[1],
+#        facecolor=colors[0],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.set_ylabel('Generalization Error (%s)'%(TASK1), fontname="Arial", fontsize=fontsize)
+ax1.legend(loc='upper right', fontsize=24, frameon=False)
+ax1.set_ylim(0.1, 0.21)
+ax1.set_xlabel('Total Sample Size', fontname="Arial", fontsize=fontsize)
+ax1.tick_params(labelsize=labelsize)
+ax1.set_yticks([0.15, 0.2])
+ax1.set_xticks([250,750,1500])
+ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
+
+right_side = ax1.spines["right"]
+right_side.set_visible(False)
+top_side = ax1.spines["top"]
+top_side.set_visible(False)
+
+ax1.text(400, np.mean(ax1.get_ylim()), "%s"%(TASK1), fontsize=30)
+ax1.text(900, np.mean(ax1.get_ylim()), "%s"%(TASK2), fontsize=30)
+
+plt.tight_layout()
+
+# plt.savefig('./result/figs/generalization_error_xor.pdf',dpi=500)
+
+#%%
+mean_error = unpickle('result/mean_xor_nxor.pickle')
+std_error = unpickle('result/std_xor_nxor.pickle')
+
+algorithms = ['Uncertainty Forest', 'Lifelong Forest']
+
+TASK1='XOR'
+TASK2='N-XOR'
+
+fig1 = plt.figure(figsize=(8,8))
+ax1 = fig1.add_subplot(1,1,1)
+# for i, algo in enumerate(algorithms):
+ax1.plot(ns[len(n1s):], mean_error[2, len(n1s):], label=algorithms[0], c=colors[1], ls=ls[1], lw=3)
+#ax1.fill_between(ns[len(n1s):],
+#        mean_error[2, len(n1s):] + 1.96*std_error[2, len(n1s):],
+#        mean_error[2, len(n1s):] - 1.96*std_error[2, len(n1s):],
+#        where=mean_error[2, len(n1s):] + 1.96*std_error[2, len(n1s):] >= mean_error[2, len(n1s):] - 1.96*std_error[2, len(n1s):],
+#        facecolor=colors[1],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.plot(ns[len(n1s):], mean_error[3, len(n1s):], label=algorithms[1], c=colors[0], ls=ls[1], lw=3)
+#ax1.fill_between(ns[len(n1s):],
+#        mean_error[3, len(n1s):] + 1.96*std_error[3, len(n1s):],
+#        mean_error[3, len(n1s):] - 1.96*std_error[3, len(n1s):],
+#        where=mean_error[3, len(n1s):] + 1.96*std_error[3, len(n1s):] >= mean_error[3, len(n1s):] - 1.96*std_error[3, len(n1s):],
+#        facecolor=colors[0],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.set_ylabel('Generalization Error (%s)'%(TASK2), fontsize=fontsize)
+ax1.legend(loc='upper right', fontsize=24, frameon=False)
+#         ax1.set_ylim(-0.01, 0.22)
+ax1.set_xlabel('Total Sample Size', fontsize=fontsize)
+ax1.tick_params(labelsize=labelsize)
+# ax1.set_yticks([0.15, 0.25, 0.35])
+ax1.set_yticks([0.15, 0.2])
+ax1.set_xticks([250,750,1500])
+ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
+
+ax1.set_ylim(0.11, 0.21)
+
+ax1.set_xlim(-10)
+right_side = ax1.spines["right"]
+right_side.set_visible(False)
+top_side = ax1.spines["top"]
+top_side.set_visible(False)
+
+# ax1.set_ylim(0.14, 0.36)
+ax1.text(400, np.mean(ax1.get_ylim()), "%s"%(TASK1), fontsize=30)
+ax1.text(900, np.mean(ax1.get_ylim()), "%s"%(TASK2), fontsize=30)
+
+plt.tight_layout()
+
+# plt.savefig('./result/figs/generalization_error_nxor.pdf',dpi=500)
+
+#%%
+mean_error = unpickle('result/mean_te_xor_nxor.pickle')
+std_error = unpickle('result/std_te_xor_nxor.pickle')
+
+algorithms = ['Forward Transfer', 'Backward Transfer']
+
+TASK1='XOR'
+TASK2='N-XOR'
+
+fig1 = plt.figure(figsize=(8,8))
+ax1 = fig1.add_subplot(1,1,1)
+
+ax1.plot(ns, mean_error[0], label=algorithms[0], c=colors[0], ls=ls[0], lw=3)
+#ax1.fill_between(ns,
+#        mean_error[0] + 1.96*std_error[0],
+#        mean_error[0] - 1.96*std_error[0],
+#        where=mean_error[1] + 1.96*std_error[0] >= mean_error[0] - 1.96*std_error[0],
+#        facecolor=colors[0],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.plot(ns[len(n1s):], mean_error[1, len(n1s):], label=algorithms[1], c=colors[0], ls=ls[1], lw=3)
+#ax1.fill_between(ns[len(n1s):],
+#        mean_error[1, len(n1s):] + 1.96*std_error[1, len(n1s):],
+#        mean_error[1, len(n1s):] - 1.96*std_error[1, len(n1s):],
+#        where=mean_error[1, len(n1s):] + 1.96*std_error[1, len(n1s):] >= mean_error[1, len(n1s):] - 1.96*std_error[1, len(n1s):],
+#        facecolor=colors[0],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.set_ylabel('Transfer Efficiency', fontsize=fontsize)
+ax1.legend(loc='upper right', fontsize=24, frameon=False)
+ax1.set_ylim(0.95, 1.42)
+ax1.set_xlabel('Total Sample Size', fontsize=fontsize)
+ax1.tick_params(labelsize=labelsize)
+ax1.set_yticks([1, 1.4])
+ax1.set_xticks([250,750,1500])
+ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
+right_side = ax1.spines["right"]
+right_side.set_visible(False)
+top_side = ax1.spines["top"]
+top_side.set_visible(False)
+ax1.hlines(1, 50,1500, colors='gray', linestyles='dashed',linewidth=1.5)
+
+ax1.text(400, np.mean(ax1.get_ylim()), "%s"%(TASK1), fontsize=30)
+ax1.text(900, np.mean(ax1.get_ylim()), "%s"%(TASK2), fontsize=30)
+
+plt.tight_layout()
+
+# plt.savefig('./result/figs/TE.pdf',dpi=500)
+
+#%%
+colors = sns.color_palette('Dark2', n_colors=2)
+
+X, Y = generate_gaussian_parity(750, cov_scale=0.1, angle_params=0)
+Z, W = generate_gaussian_parity(750, cov_scale=0.1, angle_params=np.pi/2)
+
+fig, ax = plt.subplots(1,1, figsize=(8,8))
+ax.scatter(X[:, 0], X[:, 1], c=get_colors(colors, Y), s=50)
+
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_title('Gaussian XOR', fontsize=30)
+
+plt.tight_layout()
+ax.axis('off')
+# plt.savefig('./result/figs/gaussian-xor.pdf')
+
+#%%
+colors = sns.color_palette('Dark2', n_colors=2)
+fig, ax = plt.subplots(1,1, figsize=(8,8))
+ax.scatter(Z[:, 0], Z[:, 1], c=get_colors(colors, W), s=50)
+
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_title('Gaussian N-XOR', fontsize=30)
+ax.axis('off')
+plt.tight_layout()
+# plt.savefig('./result/figs/gaussian-nxor.pdf')
+
+# %%
diff --git a/experiments/xor_nxor_exp/result/figs/TE.pdf b/experiments/xor_nxor_exp/result/figs/TE.pdf
new file mode 100644
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diff --git a/experiments/xor_nxor_exp/result/figs/control_xor_rxor.pdf b/experiments/xor_nxor_exp/result/figs/control_xor_rxor.pdf
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diff --git a/experiments/xor_nxor_exp/result/figs/gaussian-nxor.pdf b/experiments/xor_nxor_exp/result/figs/gaussian-nxor.pdf
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diff --git a/experiments/xor_nxor_exp/result/figs/generalization_error_nxor.pdf b/experiments/xor_nxor_exp/result/figs/generalization_error_nxor.pdf
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E0lFn2^Z)<=

literal 0
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diff --git a/experiments/xor_rxor_spiral_exp/xor_rxor_experiment.py b/experiments/xor_rxor_spiral_exp/xor_rxor_experiment.py
new file mode 100644
index 0000000000..df5381ea70
--- /dev/null
+++ b/experiments/xor_rxor_spiral_exp/xor_rxor_experiment.py
@@ -0,0 +1,422 @@
+#%%
+import random
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import tensorflow.keras as keras
+import seaborn as sns
+
+import numpy as np
+import pickle
+
+from sklearn.model_selection import StratifiedKFold
+from math import log2, ceil
+
+from proglearn.progressive_learner import ProgressiveLearner
+from proglearn.deciders import SimpleArgmaxAverage
+from proglearn.transformers import TreeClassificationTransformer, NeuralClassificationTransformer
+from proglearn.voters import TreeClassificationVoter, KNNClassificationVoter
+
+from joblib import Parallel, delayed
+
+#%%
+def unpickle(file):
+    with open(file, 'rb') as fo:
+        dict = pickle.load(fo, encoding='bytes')
+    return dict
+
+def get_colors(colors, inds):
+    c = [colors[i] for i in inds]
+    return c
+
+def generate_2d_rotation(theta=0, acorn=None):
+    if acorn is not None:
+        np.random.seed(acorn)
+
+    R = np.array([
+        [np.cos(theta), np.sin(theta)],
+        [-np.sin(theta), np.cos(theta)]
+    ])
+
+    return R
+
+
+def generate_gaussian_parity(n, mean=np.array([-1, -1]), cov_scale=1, angle_params=None, k=1, acorn=None):
+    if acorn is not None:
+        np.random.seed(acorn)
+
+    d = len(mean)
+
+    if mean[0] == -1 and mean[1] == -1:
+        mean = mean + 1 / 2**k
+
+    mnt = np.random.multinomial(n, 1/(4**k) * np.ones(4**k))
+    cumsum = np.cumsum(mnt)
+    cumsum = np.concatenate(([0], cumsum))
+
+    Y = np.zeros(n)
+    X = np.zeros((n, d))
+
+    for i in range(2**k):
+        for j in range(2**k):
+            temp = np.random.multivariate_normal(mean, cov_scale * np.eye(d),
+                                                 size=mnt[i*(2**k) + j])
+            temp[:, 0] += i*(1/2**(k-1))
+            temp[:, 1] += j*(1/2**(k-1))
+
+            X[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = temp
+
+            if i % 2 == j % 2:
+                Y[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = 0
+            else:
+                Y[cumsum[i*(2**k) + j]:cumsum[i*(2**k) + j + 1]] = 1
+
+    if d == 2:
+        if angle_params is None:
+            angle_params = np.random.uniform(0, 2*np.pi)
+
+        R = generate_2d_rotation(angle_params)
+        X = X @ R
+
+    else:
+        raise ValueError('d=%i not implemented!'%(d))
+
+    return X, Y.astype(int)
+
+#%%
+def experiment(n_xor, n_nxor, n_test, reps, n_trees, max_depth, acorn=None):
+    #print(1)
+    if n_xor==0 and n_nxor==0:
+        raise ValueError('Wake up and provide samples to train!!!')
+
+    if acorn != None:
+        np.random.seed(acorn)
+
+    errors = np.zeros((reps,4),dtype=float)
+
+    for i in range(reps):
+        default_transformer_class = TreeClassificationTransformer
+        default_transformer_kwargs = {"kwargs" : {"max_depth" : max_depth}}
+
+        default_voter_class = TreeClassificationVoter
+        default_voter_kwargs = {}
+
+        default_decider_class = SimpleArgmaxAverage
+        default_decider_kwargs = {"classes" : np.arange(2)}
+        progressive_learner = ProgressiveLearner(default_transformer_class = default_transformer_class,
+                                             default_transformer_kwargs = default_transformer_kwargs,
+                                             default_voter_class = default_voter_class,
+                                             default_voter_kwargs = default_voter_kwargs,
+                                             default_decider_class = default_decider_class,
+                                             default_decider_kwargs = default_decider_kwargs)
+        uf = ProgressiveLearner(default_transformer_class = default_transformer_class,
+                                             default_transformer_kwargs = default_transformer_kwargs,
+                                             default_voter_class = default_voter_class,
+                                             default_voter_kwargs = default_voter_kwargs,
+                                             default_decider_class = default_decider_class,
+                                             default_decider_kwargs = default_decider_kwargs)
+        #source data
+        xor, label_xor = generate_gaussian_parity(n_xor,cov_scale=0.1,angle_params=0)
+        test_xor, test_label_xor = generate_gaussian_parity(n_test,cov_scale=0.1,angle_params=0)
+
+        #target data
+        nxor, label_nxor = generate_gaussian_parity(n_nxor,cov_scale=0.1,angle_params=np.pi/4)
+        test_nxor, test_label_nxor = generate_gaussian_parity(n_test,cov_scale=0.1,angle_params=np.pi/4)
+
+        if n_xor == 0:
+            progressive_learner.add_task(nxor, label_nxor, num_transformers=n_trees)
+
+            errors[i,0] = 0.5
+            errors[i,1] = 0.5
+
+            uf_task2=progressive_learner.predict(test_nxor, transformer_ids=[0], task_id=0)
+            l2f_task2=progressive_learner.predict(test_nxor, task_id=0)
+
+            errors[i,2] = 1 - np.sum(uf_task2 == test_label_nxor)/n_test
+            errors[i,3] = 1 - np.sum(l2f_task2 == test_label_nxor)/n_test
+        elif n_nxor == 0:
+            progressive_learner.add_task(xor, label_xor, num_transformers=n_trees)
+
+            uf_task1=progressive_learner.predict(test_xor, transformer_ids=[0], task_id=0)
+            l2f_task1=progressive_learner.predict(test_xor, task_id=0)
+
+            errors[i,0] = 1 - np.sum(uf_task1 == test_label_xor)/n_test
+            errors[i,1] = 1 - np.sum(l2f_task1 == test_label_xor)/n_test
+            errors[i,2] = 0.5
+            errors[i,3] = 0.5
+        else:
+            progressive_learner.add_task(xor, label_xor, num_transformers=n_trees)
+            progressive_learner.add_task(nxor, label_nxor, num_transformers=n_trees)
+
+            uf.add_task(xor, label_xor, num_transformers=2*n_trees)
+            uf.add_task(nxor, label_nxor, num_transformers=2*n_trees)
+
+            uf_task1=uf.predict(test_xor, transformer_ids=[0], task_id=0)
+            l2f_task1=progressive_learner.predict(test_xor, task_id=0)
+            uf_task2=uf.predict(test_nxor, transformer_ids=[1], task_id=1)
+            l2f_task2=progressive_learner.predict(test_nxor, task_id=1)
+
+            errors[i,0] = 1 - np.sum(uf_task1 == test_label_xor)/n_test
+            errors[i,1] = 1 - np.sum(l2f_task1 == test_label_xor)/n_test
+            errors[i,2] = 1 - np.sum(uf_task2 == test_label_nxor)/n_test
+            errors[i,3] = 1 - np.sum(l2f_task2 == test_label_nxor)/n_test
+
+    return np.mean(errors,axis=0)
+
+#%%
+mc_rep = 1000
+n_test = 1000
+n_trees = 10
+n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)
+n_rxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)
+
+mean_error = np.zeros((4, len(n_xor)+len(n_rxor)))
+std_error = np.zeros((4, len(n_xor)+len(n_rxor)))
+
+mean_te = np.zeros((2, len(n_xor)+len(n_rxor)))
+std_te = np.zeros((2, len(n_xor)+len(n_rxor)))
+
+for i,n1 in enumerate(n_xor):
+    print('starting to compute %s xor\n'%n1)
+    error = np.array(
+        Parallel(n_jobs=-1,verbose=1)(
+        delayed(experiment)(n1,0,n_test,1,n_trees=n_trees,max_depth=ceil(log2(750))) for _ in range(mc_rep)
+    )
+    )
+    mean_error[:,i] = np.mean(error,axis=0)
+    std_error[:,i] = np.std(error,ddof=1,axis=0)
+    mean_te[0,i] = np.mean(error[:,0]/error[:,1])
+    mean_te[1,i] = np.mean(error[:,2]/error[:,3])
+    std_te[0,i] = np.std(error[:,0]/error[:,1],ddof=1)
+    std_te[1,i] = np.std(error[:,2]/error[:,3],ddof=1)
+
+    if n1==n_xor[-1]:
+        for j,n2 in enumerate(n_rxor):
+            print('starting to compute %s rxor\n'%n2)
+
+            error = np.array(
+                Parallel(n_jobs=-1,verbose=1)(
+                delayed(experiment)(n1,n2,n_test,1,n_trees=n_trees,max_depth=ceil(log2(750))) for _ in range(mc_rep)
+            )
+            )
+            mean_error[:,i+j+1] = np.mean(error,axis=0)
+            std_error[:,i+j+1] = np.std(error,ddof=1,axis=0)
+            mean_te[0,i+j+1] = np.mean(error[:,0]/error[:,1])
+            mean_te[1,i+j+1] = np.mean(error[:,2]/error[:,3])
+            std_te[0,i+j+1] = np.std(error[:,0]/error[:,1],ddof=1)
+            std_te[1,i+j+1] = np.std(error[:,2]/error[:,3],ddof=1)
+
+with open('result/mean_xor_rxor.pickle','wb') as f:
+    pickle.dump(mean_error,f)
+
+with open('result/std_xor_rxor.pickle','wb') as f:
+    pickle.dump(std_error,f)
+
+with open('result/mean_te_xor_rxor.pickle','wb') as f:
+    pickle.dump(mean_te,f)
+
+with open('result/std_te_xor_rxor.pickle','wb') as f:
+    pickle.dump(std_te,f)
+
+#%%
+#mc_rep = 50
+mean_error = unpickle('result/mean_xor_rxor.pickle')
+std_error = unpickle('result/std_xor_rxor.pickle')
+
+n_xor = (100*np.arange(0.5, 7.25, step=0.25)).astype(int)
+n_rxor = (100*np.arange(0.5, 7.50, step=0.25)).astype(int)
+
+n1s = n_xor
+n2s = n_rxor
+
+ns = np.concatenate((n1s, n2s + n1s[-1]))
+ls=['-', '--']
+algorithms = ['Uncertainty Forest', 'Lifelong Forest']
+
+
+TASK1='XOR'
+TASK2='R-XOR'
+
+fontsize=30
+labelsize=27.5
+
+colors = sns.color_palette("Set1", n_colors = 2)
+
+fig1 = plt.figure(figsize=(8,8))
+ax1 = fig1.add_subplot(1,1,1)
+# for i, algo in enumerate(algorithms):
+ax1.plot(ns, mean_error[0], label=algorithms[0], c=colors[1], ls=ls[np.sum(0 > 1).astype(int)], lw=3)
+#ax1.fill_between(ns,
+#        mean_error[0] + 1.96*std_error[0],
+#        mean_error[0] - 1.96*std_error[0],
+#        where=mean_error[0] + 1.96*std_error[0] >= mean_error[0] - 1.96*std_error[0],
+#        facecolor=colors[1],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.plot(ns, mean_error[1], label=algorithms[1], c=colors[0], ls=ls[np.sum(1 > 1).astype(int)], lw=3)
+#ax1.fill_between(ns,
+#        mean_error[1] + 1.96*std_error[1, ],
+#        mean_error[1] - 1.96*std_error[1, ],
+#        where=mean_error[1] + 1.96*std_error[1] >= mean_error[1] - 1.96*std_error[1],
+#        facecolor=colors[0],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.set_ylabel('Generalization Error (%s)'%(TASK1), fontname="Arial", fontsize=fontsize)
+ax1.legend(loc='upper right', fontsize=20, frameon=False)
+ax1.set_ylim(0.1, 0.21)
+ax1.set_xlabel('Total Sample Size', fontname="Arial", fontsize=fontsize)
+ax1.tick_params(labelsize=labelsize)
+ax1.set_yticks([0.15, 0.2])
+ax1.set_xticks([250,750,1500])
+ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
+
+right_side = ax1.spines["right"]
+right_side.set_visible(False)
+top_side = ax1.spines["top"]
+top_side.set_visible(False)
+
+ax1.text(400, np.mean(ax1.get_ylim()), "%s"%(TASK1), fontsize=25)
+ax1.text(900, np.mean(ax1.get_ylim()), "%s"%(TASK2), fontsize=25)
+
+plt.tight_layout()
+
+plt.savefig('result/figs/generalization_error_xor.pdf',dpi=500)
+
+#%%
+mean_error = unpickle('result/mean_xor_rxor.pickle')
+std_error = unpickle('result/std_xor_rxor.pickle')
+
+algorithms = ['Uncertainty Forest', 'Lifelong Forest']
+
+TASK1='XOR'
+TASK2='R-XOR'
+
+fig1 = plt.figure(figsize=(8,8))
+ax1 = fig1.add_subplot(1,1,1)
+# for i, algo in enumerate(algorithms):
+ax1.plot(ns[len(n1s):], mean_error[2, len(n1s):], label=algorithms[0], c=colors[1], ls=ls[1], lw=3)
+#ax1.fill_between(ns[len(n1s):],
+#        mean_error[2, len(n1s):] + 1.96*std_error[2, len(n1s):],
+#        mean_error[2, len(n1s):] - 1.96*std_error[2, len(n1s):],
+#        where=mean_error[2, len(n1s):] + 1.96*std_error[2, len(n1s):] >= mean_error[2, len(n1s):] - 1.96*std_error[2, len(n1s):],
+#        facecolor=colors[1],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.plot(ns[len(n1s):], mean_error[3, len(n1s):], label=algorithms[1], c=colors[0], ls=ls[1], lw=3)
+#ax1.fill_between(ns[len(n1s):],
+#        mean_error[3, len(n1s):] + 1.96*std_error[3, len(n1s):],
+#        mean_error[3, len(n1s):] - 1.96*std_error[3, len(n1s):],
+#        where=mean_error[3, len(n1s):] + 1.96*std_error[3, len(n1s):] >= mean_error[3, len(n1s):] - 1.96*std_error[3, len(n1s):],
+#        facecolor=colors[0],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.set_ylabel('Generalization Error (%s)'%(TASK2), fontsize=fontsize)
+ax1.legend(loc='upper right', fontsize=20, frameon=False)
+#         ax1.set_ylim(-0.01, 0.22)
+ax1.set_xlabel('Total Sample Size', fontsize=fontsize)
+ax1.tick_params(labelsize=labelsize)
+# ax1.set_yticks([0.15, 0.25, 0.35])
+ax1.set_yticks([0.12, 0.15])
+ax1.set_xticks([250,750,1500])
+ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
+
+ax1.set_ylim(0.115, 0.17)
+
+ax1.set_xlim(-10)
+right_side = ax1.spines["right"]
+right_side.set_visible(False)
+top_side = ax1.spines["top"]
+top_side.set_visible(False)
+
+# ax1.set_ylim(0.14, 0.36)
+ax1.text(400, np.mean(ax1.get_ylim()), "%s"%(TASK1), fontsize=25)
+ax1.text(900, np.mean(ax1.get_ylim()), "%s"%(TASK2), fontsize=25)
+
+plt.tight_layout()
+
+plt.savefig('result/figs/generalization_error_rxor.pdf',dpi=500)
+
+#%%
+mean_error = unpickle('result/mean_te_xor_rxor.pickle')
+std_error = unpickle('result/std_te_xor_rxor.pickle')
+
+algorithms = ['Forward Transfer', 'Backward Transfer']
+
+TASK1='XOR'
+TASK2='R-XOR'
+
+fig1 = plt.figure(figsize=(8,8))
+ax1 = fig1.add_subplot(1,1,1)
+
+ax1.plot(ns, mean_error[0], label=algorithms[0], c=colors[0], ls=ls[0], lw=3)
+#ax1.fill_between(ns,
+#        mean_error[0] + 1.96*std_error[0],
+#        mean_error[0] - 1.96*std_error[0],
+#        where=mean_error[1] + 1.96*std_error[0] >= mean_error[0] - 1.96*std_error[0],
+#        facecolor=colors[0],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.plot(ns[len(n1s):], mean_error[1, len(n1s):], label=algorithms[1], c=colors[0], ls=ls[1], lw=3)
+#ax1.fill_between(ns[len(n1s):],
+#        mean_error[1, len(n1s):] + 1.96*std_error[1, len(n1s):],
+#        mean_error[1, len(n1s):] - 1.96*std_error[1, len(n1s):],
+#        where=mean_error[1, len(n1s):] + 1.96*std_error[1, len(n1s):] >= mean_error[1, len(n1s):] - 1.96*std_error[1, len(n1s):],
+#        facecolor=colors[0],
+#        alpha=0.15,
+#        interpolate=True)
+
+ax1.set_ylabel('Transfer Efficiency', fontsize=fontsize)
+ax1.legend(loc='upper right', fontsize=20, frameon=False)
+ax1.set_ylim(0.96, 1.045)
+ax1.set_xlabel('Total Sample Size', fontsize=fontsize)
+ax1.tick_params(labelsize=labelsize)
+ax1.set_yticks([1, 1.04])
+ax1.set_xticks([250,750,1500])
+ax1.axvline(x=750, c='gray', linewidth=1.5, linestyle="dashed")
+right_side = ax1.spines["right"]
+right_side.set_visible(False)
+top_side = ax1.spines["top"]
+top_side.set_visible(False)
+ax1.hlines(1, 50,1500, colors='gray', linestyles='dashed',linewidth=1.5)
+
+ax1.text(400, np.mean(ax1.get_ylim()), "%s"%(TASK1), fontsize=25)
+ax1.text(900, np.mean(ax1.get_ylim()), "%s"%(TASK2), fontsize=25)
+
+plt.tight_layout()
+
+plt.savefig('result/figs/TE.pdf',dpi=500)
+
+#%%
+colors = sns.color_palette('Dark2', n_colors=2)
+
+X, Y = generate_gaussian_parity(750, cov_scale=0.1, angle_params=0)
+Z, W = generate_gaussian_parity(750, cov_scale=0.1, angle_params=np.pi/4)
+
+fig, ax = plt.subplots(1,1, figsize=(8,8))
+ax.scatter(X[:, 0], X[:, 1], c=get_colors(colors, Y), s=50)
+
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_title('Gaussian XOR', fontsize=30)
+
+plt.tight_layout()
+ax.axis('off')
+plt.savefig('result/figs/gaussian-xor.pdf')
+
+#%%
+colors = sns.color_palette('Dark2', n_colors=2)
+fig, ax = plt.subplots(1,1, figsize=(8,8))
+ax.scatter(Z[:, 0], Z[:, 1], c=get_colors(colors, W), s=50)
+
+ax.set_xticks([])
+ax.set_yticks([])
+ax.set_title('Gaussian R-XOR', fontsize=30)
+ax.axis('off')
+plt.tight_layout()
+plt.savefig('result/figs/gaussian-rxor.pdf')
+
+# %%
diff --git a/proglearn/base.py b/proglearn/base.py
index e974e2703c..d4221132b3 100644
--- a/proglearn/base.py
+++ b/proglearn/base.py
@@ -2,26 +2,17 @@
 Main Author: Will LeVine 
 Corresponding Email: levinewill@icloud.com
 '''
-from abc import ABC, abstractmethod
+import abc
 
-class BaseTransformer(ABC):
+class BaseTransformer(abc.ABC):
     """
     A base class for a transformer.
     
     Parameters
     ----------
     None
-    
-    Methods
-    ----------
-    fit(X, y)
-        fits the transformer to data X with labels y
-    transform(X)
-        transformers the given data, X
-    is_fitted()
-        indicates whether the transformer is fitted
     """
-    @abstractmethod
+    @abc.abstractmethod
     def fit(self, X=None, y=None):
         """
         Fits the transformer.
@@ -35,7 +26,7 @@ def fit(self, X=None, y=None):
         """
         pass
 
-    @abstractmethod
+    @abc.abstractmethod
     def transform(self, X):
         """
         Perform inference using the transformer.
@@ -47,7 +38,7 @@ def transform(self, X):
         """
         pass
 
-    @abstractmethod
+    @abc.abstractmethod
     def is_fitted(self):
         """
         Indicates whether the transformer is fitted.
@@ -59,24 +50,15 @@ def is_fitted(self):
         pass
 
 
-class BaseVoter(ABC):
+class BaseVoter(abc.ABC):
     """
     A base class for a voter.
     
     Parameters
     ----------
     None
-    
-    Methods
-    ----------
-    fit(X, y)
-        fits the voter to data X with labels y
-    vote(X)
-        provides inference votes on the given transformed data, X
-    is_fitted()
-        indicates whether the voter is fitted
     """
-    @abstractmethod
+    @abc.abstractmethod
     def fit(self, X, y):
         """
         Fits the voter.
@@ -84,16 +66,16 @@ def fit(self, X, y):
         Parameters
         ----------
         X : ndarray
-            Transformed data matrix. 
+            Input data matrix. 
         y : ndarray
             Output (i.e. response) data matrix.
         """
         pass
 
-    @abstractmethod
+    @abc.abstractmethod
     def vote(self, X):
         """
-        Perform inference using the voter on transformed data X.
+        Perform inference using the voter.
         
         Parameters
         ----------
@@ -102,7 +84,7 @@ def vote(self, X):
         """
         pass
 
-    @abstractmethod
+    @abc.abstractmethod
     def is_fitted(self):
         """
         Indicates whether the voter is fitted.
@@ -114,24 +96,15 @@ def is_fitted(self):
         pass
 
 
-class BaseDecider(ABC):
+class BaseDecider(abc.ABC):
     """
     A base class for a decider.
     
     Parameters
     ----------
     None
-    
-    Methods
-    ----------
-    fit(X, y, transformer_id_to_transformers, voter_id_to_voters)
-        fits transformer to data X with labels y
-    predict(X)
-        decides on the given input data X
-    is_fitted()
-        indicates whether the decider is fitted
     """
-    @abstractmethod
+    @abc.abstractmethod
     def fit(self, X, y, transformer_id_to_transformers, voter_id_to_voters):
         """
         Fits the decider.
@@ -149,7 +122,7 @@ def fit(self, X, y, transformer_id_to_transformers, voter_id_to_voters):
         """
         pass
 
-    @abstractmethod
+    @abc.abstractmethod
     def predict(self, X):
         """
         Perform inference using the decider.
@@ -161,7 +134,7 @@ def predict(self, X):
         """
         pass
     
-    @abstractmethod
+    @abc.abstractmethod
     def is_fitted(self):
         """
         Indicates whether the decider is fitted.
@@ -181,13 +154,8 @@ class BaseClassificationDecider(BaseDecider):
     Parameters
     ----------
     None
-    
-    Methods
-    ----------
-    predict_proba(X)
-        returns class-posteriors for input data X
     """
-    @abstractmethod
+    @abc.abstractmethod
     def predict_proba(self, X):
         """
         Estimate posteriors using the decider.
@@ -199,26 +167,15 @@ def predict_proba(self, X):
         """
         pass
     
-class BaseProgressiveLearner(ABC):
+class BaseProgressiveLearner(abc.ABC):
     """
     A base class for a progressive learner.
     
     Parameters
     ----------
     None
-    
-    Methods
-    ----------
-    add_task(X, y)
-        adds a new unseen task to the progressive learner
-    add_transformer(X, y)
-        adds a new transformer (but no voters or transformers corresponding 
-        to the task from which the transformer data was collected.
-    predict(X, task_id):
-        performs inference corresponding to the input task_id using the 
-        progressive learner.
     """
-    @abstractmethod
+    @abc.abstractmethod
     def add_task(self, X, y):
         """
         Add a new unseen task to the progressive learner.
@@ -232,11 +189,11 @@ def add_task(self, X, y):
         """
         pass
 
-    @abstractmethod
+    @abc.abstractmethod
     def add_transformer(self, X, y):
         """
         Add a new transformer (but no voters or transformers corresponding to the task 
-        from which the transformer data was collected).
+        from which the transformer data was collected.
         
         Parameters
         ----------
@@ -247,11 +204,10 @@ def add_transformer(self, X, y):
         """
         pass
     
-    @abstractmethod
+    @abc.abstractmethod
     def predict(self, X, task_id):
         """
-        Perform inference corresponding to the input task_id on input data X using the 
-        progressive learner.
+        Perform inference corresponding to the input task_id using the progressive learner.
         
         Parameters
         ----------
@@ -271,14 +227,8 @@ class BaseClassificationProgressiveLearner(BaseProgressiveLearner):
     Parameters
     ----------
     None
-    
-    Methods
-    ----------
-    predict_proba(X, task_id):
-        provides class-posteriors corresponding to the input task_id on input data X using 
-        the progressive learner.
     """
-    @abstractmethod
+    @abc.abstractmethod
     def predict_proba(self, X, task_id):
         """
         Estimate posteriors under a given task_id using the decider.
diff --git a/proglearn/forest.py b/proglearn/forest.py
index ff3f71ee8d..e67050dc7d 100644
--- a/proglearn/forest.py
+++ b/proglearn/forest.py
@@ -9,34 +9,6 @@
 import numpy as np
 
 class LifelongClassificationForest:
-    """
-    A class used to represent a lifelong classification forest.
-    
-    Parameters:
-    ---
-    n_estimators : int, default=100
-        The number of estimators used in the Lifelong Classification Forest
-    tree_construction_proportion : int, default=0.67
-        The proportions of the input data set aside to train each decision 
-        tree. The remainder of the data is used to fill in voting posteriors.
-    finite_sample_correction : bool, default=False
-        Boolean indicating whether this learner will have finite sample correction
-
-    Methods
-    ---
-    add_task(X, y, task_id)
-        adds a task with id task_id, given input data matrix X 
-        and output data matrix y, to the Lifelong Classification Forest
-    add_transformer(X, y, transformer_id)
-        adds a transformer with id transformer_id, given input data matrix, X 
-        and output data matrix, y, to the Lifelong Classification Forest 
-        and trains the voters and deciders from new transformer to previous tasks
-    predict(X, task_id)
-        predicts class labels of a task with id task_id given input data matrix X 
-    predict_proba(X, task_id)
-        predicts posterior probabilities of each class label of a task 
-        with id task_id given input data matrix X and 
-    """
     def __init__(self, n_estimators=100, tree_construction_proportion=0.67, finite_sample_correction=False):
         self.n_estimators = n_estimators
         self.tree_construction_proportion=tree_construction_proportion
@@ -53,27 +25,6 @@ def __init__(self, n_estimators=100, tree_construction_proportion=0.67, finite_s
        
     def add_task(
         self, X, y, task_id=None):
-        """
-        adds a task with id task_id, given input data matrix X 
-        and output data matrix y, to the Lifelong Classification Forest
-        
-        Parameters
-        ---
-        X : ndarray
-            The input data matrix.
-        y : ndarray
-            The output (response) data matrix.
-        task_id : obj, default=None
-            The id corresponding to the task being added.
-        
-        Attributes
-        ---
-        tree_construction_proportion : float
-            The proportions of the input data set aside to 
-            train each decision tree
-        n_estimators : int
-            The number of estimators used in the Lifelong Classification Forest
-        """
         self.pl.add_task(
             X,
             y,
@@ -85,27 +36,6 @@ def add_task(
         return self
 
     def add_transformer(self, X, y, transformer_id=None):
-        """
-        adds a transformer to the Lifelong Classification Forest and trains 
-        the voters and deciders from this new transformer to previous tasks
-        
-        Parameters
-        ---
-        X : ndarray
-            The input data matrix.
-        y : ndarray
-            The output (response) data matrix.
-        transformer_id : obj, default=None
-            The id corresponding to the transformer being added.
-
-        Attributes
-        ---
-        tree_construction_proportion : float
-            The proportions of the input data set aside to 
-            train each decision tree
-        n_estimators : int
-            The number of estimators used in the Lifelong Classification Forest
-        """
         self.pl.add_transformer(
             X,
             y,
@@ -117,31 +47,9 @@ def add_transformer(self, X, y, transformer_id=None):
         return self
 
     def predict(self, X, task_id):
-        """
-        predicts the class labels for a particular task 
-        given data, X and task id, task_id
-        
-        Parameters 
-        ---
-        X : ndarray
-            The input data matrix.
-        task_id : obj
-            The id corresponding to the task being mapped to.
-        """
         return self.pl.predict(X, task_id)
 
     def predict_proba(self, X, task_id):
-        """
-        predicts the posterior probabilities of each class for 
-        a particular task given data, X and task id, task_id
-        
-        Parameters
-        ---
-        X : ndarray
-            The input data matrix.
-        task_id:
-            The id corresponding to the task being mapped to.
-        """
         return self.pl.predict_proba(X, task_id)
 
 
diff --git a/proglearn/sims/make_XOR.py b/proglearn/sims/make_XOR.py
new file mode 100755
index 0000000000..46b5c4e000
--- /dev/null
+++ b/proglearn/sims/make_XOR.py
@@ -0,0 +1,80 @@
+import itertools
+import numpy as np
+from sklearn import datasets
+from sklearn.utils import check_random_state
+
+def make_XOR(n_samples=100, cluster_center=[0,0], cluster_std=0.25, 
+             dist_from_center=0.5, N_XOR=False, theta_rotation=0,
+             random_state=None):
+    """
+    Generate 2-dimensional Gaussian XOR distribution.
+    (Classic XOR problem but each point is the 
+     center of a Gaussian blob distribution)
+    
+    Parameters
+    ----------
+    n_samples : int, optional (default=100)
+        If int, it is the total number of points equally divided among
+        the four clusters.
+        
+    cluster_center : array of shape [2,], optional (default=[0,0])
+        The x1 and x2 coordinates of the center of the four clusters.
+
+    cluster_std : float, optional (default=0.25)
+        The standard deviation of the clusters.
+    
+    dist_from_center : float, optional (default=0.5)
+        X value distance of each cluster to the center of the four clusters.
+        
+    N_XOR : boolean, optional (default=False)
+        Change to Gaussian N_XOR distribution (inverts the class labels).
+        
+    theta_rotation : float, optional (default=0)
+        Number of radians to rotate the distribution by. 
+    
+    random_state : int, RandomState instance, default=None
+        Determines random number generation for dataset creation. Pass an int
+        for reproducible output across multiple function calls.
+      
+   
+    Returns
+    -------
+    X : array of shape [n_samples, 2]
+        The generated samples.
+    y : array of shape [n_samples]
+        The integer labels for cluster membership of each sample.
+    """
+    
+    #variable setup
+    seed = random_state
+    dist = dist_from_center
+    std = cluster_std
+    n = int(n_samples/4)
+    
+    cluster_centers = np.array(list(itertools.product([dist, -dist], repeat=2)))
+    cluster_centers = cluster_center - cluster_centers
+    n_per_cluster = np.full(shape=2, fill_value=n)
+
+    #make blobs
+    X1,_ = datasets.make_blobs(n_samples=n_per_cluster, n_features=2, 
+                               centers=cluster_centers[[0,3], :], 
+                               cluster_std=std, random_state=seed)
+    X2,_ = datasets.make_blobs(n_samples=n_per_cluster, n_features=2, 
+                               centers=cluster_centers[[1,2], :], 
+                               cluster_std=std, random_state=seed)
+    
+    #assign classe
+    if N_XOR:
+        y1, y2 = np.zeros(n*2), np.ones(n*2)
+    else:
+        y1, y2 = np.ones(n*2), np.zeros(n*2)
+
+    X = np.concatenate((X1, X2))
+    y = np.concatenate((y1, y2))
+
+    #rotation
+    c, s = np.cos(theta_rotation), np.sin(theta_rotation)
+    R = np.array([[c, -s], [s, c]])
+    X = (R @ X.T).T
+    
+    return X,y
diff --git a/proglearn/tests/test_KNNClassificationVoter.py b/proglearn/tests/test_KNNClassificationVoter.py
new file mode 100755
index 0000000000..d264acd73a
--- /dev/null
+++ b/proglearn/tests/test_KNNClassificationVoter.py
@@ -0,0 +1,45 @@
+import unittest
+import pytest
+import numpy as np
+from numpy.testing import assert_allclose
+from sklearn.exceptions import NotFittedError
+
+from proglearn.voters import KNNClassificationVoter
+from proglearn.base import BaseVoter
+    
+class TestKNNClassificationVoter(unittest.TestCase):
+
+
+    def test_vote_without_fit(self):
+        #generate random data
+        X = np.random.randn(100,3)
+    
+        #check if NotFittedError is raised
+        with self.assertRaises(NotFittedError):
+            kcv = KNNClassificationVoter(3)
+            kcv.vote(X)
+
+
+    def test_correct_vote(self):
+        #set random seed
+        np.random.seed(0)
+        
+        
+        #generate training data and classes
+        X = np.concatenate((np.zeros(100), np.ones(100))).reshape(-1,1)
+        Y = np.concatenate((np.zeros(100), np.ones(100)))
+
+        #train model
+        kcv = KNNClassificationVoter(3)
+        kcv.fit(X, Y)
+
+        #generate testing data and class probability
+        X_test = np.ones(6).reshape(-1,1)
+        Y_test = np.concatenate((np.zeros((6,1)), np.ones((6,1))), axis = 1)
+
+        #check if model predicts as expected
+        assert_allclose(Y_test, kcv.vote(X_test), atol=1e-4)
+        
+
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/proglearn/tests/test_forest.py b/proglearn/tests/test_forest.py
deleted file mode 100644
index 0faef8eaeb..0000000000
--- a/proglearn/tests/test_forest.py
+++ /dev/null
@@ -1,49 +0,0 @@
-import unittest
-import pytest
-import numpy as np
-import random
-
-from proglearn.forest import LifelongClassificationForest
-from proglearn.transformers import TreeClassificationTransformer
-from proglearn.voters import TreeClassificationVoter
-from proglearn.deciders import SimpleArgmaxAverage
-
-class TestLifelongClassificationForest:
-    
-    def test_initialize(self):
-        l2f = LifelongClassificationForest()
-        assert True
-        
-    def test_correct_default_transformer(self):
-        l2f = LifelongClassificationForest()
-        assert l2f.pl.default_transformer_class == TreeClassificationTransformer
-        
-    def test_correct_default_voter(self):
-        l2f = LifelongClassificationForest()
-        assert l2f.pl.default_voter_class == TreeClassificationVoter
-        
-    def test_correct_default_decider(self):
-        l2f = LifelongClassificationForest()
-        assert l2f.pl.default_decider_class == SimpleArgmaxAverage
-        
-    def test_correct_default_kwargs(self):
-        l2f = LifelongClassificationForest()
-        
-        #transformer 
-        assert l2f.pl.default_transformer_kwargs == {}
-        
-        #voter
-        assert len(l2f.pl.default_voter_kwargs) == 1
-        assert "finite_sample_correction" in list(l2f.pl.default_voter_kwargs.keys())
-        assert l2f.pl.default_voter_kwargs["finite_sample_correction"] == False
-        
-        #decider
-        assert l2f.pl.default_decider_kwargs == {}
-        
-    def test_correct_default_n_estimators(self):
-        l2f = LifelongClassificationForest()
-        assert l2f.n_estimators == 100
-    
-    def test_correct_true_initilization_finite_sample_correction(self):
-        l2f = LifelongClassificationForest(finite_sample_correction=True)
-        assert l2f.pl.default_voter_kwargs == {"finite_sample_correction": True}
diff --git a/proglearn/tests/test_lifelongclassificationforest.py b/proglearn/tests/test_lifelongclassificationforest.py
new file mode 100644
index 0000000000..e5d0d10b52
--- /dev/null
+++ b/proglearn/tests/test_lifelongclassificationforest.py
@@ -0,0 +1,50 @@
+import unittest
+import pytest
+import numpy as np
+import random
+
+from proglearn.forest import LifelongClassificationForest
+from proglearn.transformers import TreeClassificationTransformer
+from proglearn.voters import TreeClassificationVoter
+from proglearn.deciders import SimpleArgmaxAverage
+
+class test_LifelongClassificationForest(unittest.TestCase):
+    
+    def setUp(self):
+        self.l2f = LifelongClassificationForest()
+    
+    def test_initialize(self):
+        self.assertTrue(True)
+        
+    def test_correct_default_transformer(self):
+        self.assertIs(self.l2f.pl.default_transformer_class, TreeClassificationTransformer)
+        
+    def test_correct_default_voter(self):
+        self.assertIs(self.l2f.pl.default_voter_class, TreeClassificationVoter)
+        
+    def test_correct_default_decider(self):
+        self.assertIs(self.l2f.pl.default_decider_class, SimpleArgmaxAverage)
+        
+    def test_correct_default_kwargs_transformer_decider_empty(self):
+        self.assertFalse(self.l2f.pl.default_transformer_kwargs)
+        self.assertFalse(self.l2f.pl.default_decider_kwargs)
+        
+    def test_correct_default_estimators(self):
+        self.assertIs(self.l2f.n_estimators, 100)
+        
+    def test_correct_estimator(self):
+        rand = random.randint(0, 100)
+        l2f = LifelongClassificationForest(n_estimators=rand)
+        self.assertIs(l2f.n_estimators, rand)
+        
+    def test_correct_default_finite_sample_correction(self):
+        tmp_dict = {"finite_sample_correction": False}
+        self.assertEqual(self.l2f.pl.default_voter_kwargs, tmp_dict)
+    
+    def test_correct_true_initilization_finite_sample_correction(self):
+        tmp_dict = {"finite_sample_correction": True}
+        l2f = LifelongClassificationForest(finite_sample_correction=True)
+        self.assertEqual(l2f.pl.default_voter_kwargs, tmp_dict)
+        
+if __name__ == '__main__':
+    unittest.main()
\ No newline at end of file
diff --git a/proglearn/tests/test_system.py b/proglearn/tests/test_system.py
index f52ecceca0..527b697152 100644
--- a/proglearn/tests/test_system.py
+++ b/proglearn/tests/test_system.py
@@ -1,6 +1,7 @@
 #%%
 import pytest
 import numpy as np
+from numpy.testing import assert_almost_equal, assert_warns, assert_raises
 
 from proglearn.progressive_learner import ProgressiveLearner
 from proglearn.deciders import SimpleArgmaxAverage
@@ -63,7 +64,7 @@ def generate_gaussian_parity(n, mean=np.array([-1, -1]), cov_scale=1, angle_para
 
 
 class TestSystem:
-    def test_nxor(self):
+    def simulation_data_test(self):
         #tests proglearn on xor nxor simulation data
         np.random.seed(12345)
 
diff --git a/proglearn/tests/test_transformer.py b/proglearn/tests/test_treeclassification.py
similarity index 54%
rename from proglearn/tests/test_transformer.py
rename to proglearn/tests/test_treeclassification.py
index 7a0b53fba4..1539b3eae7 100644
--- a/proglearn/tests/test_transformer.py
+++ b/proglearn/tests/test_treeclassification.py
@@ -1,15 +1,13 @@
 import pytest
+import unittest
 import numpy as np
 from numpy.testing import assert_allclose
 from sklearn.exceptions import NotFittedError
 
 from proglearn.transformers import TreeClassificationTransformer
+from proglearn.voters import TreeClassificationVoter
 
-class TestTreeClassificationTransformer:
-    
-    def test_init(self):
-        TreeClassificationTransformer()
-        assert True
+class test_treeclassification(unittest.TestCase):
     
     def test_predict_without_fit(self):
         # Generate random data
@@ -17,7 +15,12 @@ def test_predict_without_fit(self):
     
         with pytest.raises(NotFittedError):
             trt = TreeClassificationTransformer()
-            trt.transform(X)  
+            trt.transform(X)
+    
+        with pytest.raises(NotFittedError):
+            trv = TreeClassificationVoter()
+            trv.vote(X)
+    
     
     def test_correct_transformation(self):
         np.random.seed(1)
@@ -31,3 +34,20 @@ def test_correct_transformation(self):
         u1 = trt.transform(np.array([0]).reshape(1, -1))
         u2 = trt.transform(np.array([1]).reshape(1, -1))
         assert u1 != u2
+    
+    
+    def test_correct_vote(self):
+        np.random.seed(3)
+    
+        X = np.concatenate((np.zeros(100), np.ones(100)))
+        y = np.concatenate((np.zeros(100), np.ones(100)))
+    
+        trv = TreeClassificationVoter()
+        trv.fit(X, y)
+    
+        X_test = np.ones(6)
+        y_test = np.ones(6)
+        assert_allclose(y_test, trv.vote(X_test)[:,-1], atol=1e-4)
+    
+if __name__ == '__main__':
+    unittest.main()
diff --git a/proglearn/tests/test_voter.py b/proglearn/tests/test_voter.py
index 1f07849e91..8cd3f9ef30 100644
--- a/proglearn/tests/test_voter.py
+++ b/proglearn/tests/test_voter.py
@@ -3,24 +3,31 @@
 
 from sklearn.utils.validation import NotFittedError
 
+from proglearn.progressive_learner import ProgressiveLearner
+from proglearn.deciders import SimpleArgmaxAverage
+from proglearn.transformers import TreeClassificationTransformer, NeuralClassificationTransformer
 from proglearn.voters import TreeClassificationVoter, KNNClassificationVoter
 
-from numpy import testing
+import unittest
+
+from numpy.testing import assert_allclose
 
 def generate_data(n = 100):
     X = np.concatenate([np.zeros(n), np.ones(n)])
     y = np.concatenate([np.zeros(n), np.ones(n)])
     return X, y
 
-class TestTreeClassificationVoter:
+class TestTreeClassificationVoter(unittest.TestCase):
     def test_initialize(self):
         TreeClassificationVoter()
-        assert True
+        self.assertTrue(True)
 
 
     def test_predict_without_fit(self):
         X, y = generate_data()
-        testing.assert_raises(NotFittedError, TreeClassificationVoter().vote, X)
+        with self.assertRaises(NotFittedError):
+            voter = TreeClassificationVoter()
+            voter.vote(X)
 
     def test_correct_predict(self):
         X, y = generate_data()
@@ -30,42 +37,7 @@ def test_correct_predict(self):
 
         y_hat = np.argmax(voter.vote(X), axis = 1)
 
-        testing.assert_allclose(y, y_hat)
-        
-class TestKNNClassificationVoter:
-    def test_initialize(self):
-        KNNClassificationVoter()
-        assert True
-
-    def test_vote_without_fit(self):
-        #generate random data
-        X = np.random.randn(100,3)
-        testing.assert_raises(NotFittedError, KNNClassificationVoter().vote, X)
-        
-    def test_correct_k(self):
-        #generate training data and classes
-        X = np.concatenate((np.zeros(100), np.ones(100))).reshape(-1,1)
-        Y = np.concatenate((np.zeros(100), np.ones(100)))
-
-        #train model
-        assert KNNClassificationVoter(3).fit(X, Y).k == 3
-        assert KNNClassificationVoter().fit(X, Y).k == int(np.log2(len(X)))
-
-    def test_correct_vote(self):
-        #set random seed
-        np.random.seed(0)   
-        
-        #generate training data and classes
-        X = np.concatenate((np.zeros(100), np.ones(100))).reshape(-1,1)
-        Y = np.concatenate((np.zeros(100), np.ones(100)))
-
-        #train model
-        kcv = KNNClassificationVoter(3)
-        kcv.fit(X, Y)
-
-        #generate testing data and class probability
-        X_test = np.ones(6).reshape(-1,1)
-        Y_test = np.concatenate((np.zeros((6,1)), np.ones((6,1))), axis = 1)
+        assert_allclose(y, y_hat)
 
-        #check if model predicts as expected
-        testing.assert_allclose(Y_test, kcv.vote(X_test), atol=1e-4)
+if __name__ == '__main__':
+    unittest.main()
diff --git a/proglearn/voters.py b/proglearn/voters.py
index c3060c5f1b..e0db39b7ac 100755
--- a/proglearn/voters.py
+++ b/proglearn/voters.py
@@ -176,8 +176,8 @@ def fit(self, X, y):
             the label for class membership of the given data
         """
         X, y = check_X_y(X, y)
-        self.k = int(np.log2(len(X))) if self.k == None else self.k
-        self.knn = KNeighborsClassifier(self.k, **self.kwargs)
+        k = int(np.log2(len(X))) if self.k == None else self.k
+        self.knn = KNeighborsClassifier(k, **self.kwargs)
         self.knn.fit(X, y)
         self._is_fitted = True
 
diff --git a/pytest.ini b/pytest.ini
index fb7e62fc77..db82df5310 100644
--- a/pytest.ini
+++ b/pytest.ini
@@ -1,8 +1,7 @@
 [pytest]
-addopts = --cov=proglearn --doctest-modules
 
 
 filterwarnings = 
 # Matrix PendingDeprecationWarning.
     ignore:Using or importing the ABCs from 'collections'
-    ignore:the imp module is deprecated in favour of importlib
+    ignore:the imp module is deprecated in favour of importlib
\ No newline at end of file
diff --git a/runtime.txt b/runtime.txt
deleted file mode 100644
index 475ba515c0..0000000000
--- a/runtime.txt
+++ /dev/null
@@ -1 +0,0 @@
-3.7
diff --git a/tutorials/UncertaintyForestTutorials/UncertaintyForest_Tutorial_1-Installation.ipynb b/tutorials/UncertaintyForestTutorials/UncertaintyForest_Tutorial_1-Installation.ipynb
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-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial Overview\n",
-    "This set of five tutorials (installation, package setup, data setup, running, analyzing) will explain the UncertaintyForest class. After following the steps below, you should have the ability to run the code on your own machine and interpret the results.\n",
-    "\n",
-    "# 1. Installation\n",
-    "## *Goal: Clone the repository on your local machine and understand what it includes*\n",
-    "\n",
-    "### Let's clone the repository\n",
-    "Steps:\n",
-    "1. Open the command line on your local machine (called \"Terminal\" on Mac)\n",
-    "2. Navigate to the location where you'd like to put the repository.\n",
-    "    1. Find a location in a file explorer (\"Finder\" on Mac)\n",
-    "    2. Type \"cd \" in the command prompt\n",
-    "    3. Drag and drop the folder where you'd like to place the repository from the file explorer to the command line\n",
-    "    The command prompt should show something like:\n",
-    "    `bstraus@BS-Mac ~ % cd /Users/bstraus/Desktop `\n",
-    "3. Type `git clone REPOSITORY_URL` where `REPOSITORY_URL` is replaced by the URL of the neurodata/progressive-learning repository (as of 2020-09-21, it is https://github.com/neurodata/progressive-learning)\n",
-    "4. Wait for the process to finish. You'll know it's done because you'll see the first part of the command prompt pop up. For me, that looks like: `bstraus@BS-Mac ~ %`\n",
-    "\n",
-    "Congrats! You've now cloned the progressive-learning repository.\n",
-    "\n",
-    "### Let's take a tour\n",
-    "Currently, you're looking at this tutorial, which lives in progressive-learning/tutorials/.\n",
-    "This folder also currently houses a notebook running one of the experiments.\n",
-    "\n",
-    "In the root directory, we have:\n",
-    "* `progressive-learning/docs` : contains files that will tell you requirements (we'll use this later), contributing guidelines, and some other administrative files\n",
-    "\n",
-    "* `progressive-learning/experiments` : contains notebooks and results for many of the experiments that utilize the functions/classes in the repository\n",
-    "\n",
-    "* `progressive-learning/proglearn` : the heart of the repository containing the python files for the progressive learning classes. We'll focus on the UncertaintyForest class which lives in the `forest.py` file in this directory.\n",
-    "\n",
-    "* `progressive-learning/tests` : contains python files for various tests\n",
-    "\n",
-    "* `progressive-learning/tutorials` : contains python notebooks (like this one) that will guide you through using the classes in the repository and running the experiments\n",
-    "\n",
-    "In future notebooks of this tutorial, we'll discuss how to prepare to run the code for the UncertaintyForest class. That code lives in the `progressive-learning/proglearn/forest.py` file. \n",
-    "\n",
-    "But, for now, we'll prepare to do that by making a virtual environment and installing the required packages to run that code.\n",
-    "\n",
-    "### You're done with part 1 of the tutorial!\n",
-    "\n",
-    "### Move on to part 2 (called \"UncertaintyForest_Tutorial_2-Package-Setup\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/tutorials/UncertaintyForestTutorials/UncertaintyForest_Tutorial_2-Package-Setup.ipynb b/tutorials/UncertaintyForestTutorials/UncertaintyForest_Tutorial_2-Package-Setup.ipynb
deleted file mode 100644
index 1fe13fa458..0000000000
--- a/tutorials/UncertaintyForestTutorials/UncertaintyForest_Tutorial_2-Package-Setup.ipynb
+++ /dev/null
@@ -1,72 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial Overview\n",
-    "This set of five tutorials (installation, package setup, data setup, running, analyzing) will explain the UncertaintyForest class. After following the steps below, you should have the ability to run the code on your own machine and interpret the results.\n",
-    "\n",
-    "If you haven't seen it already, take a look at the first part of this set of tutorials called `UncertaintyForest_Tutorials_1-Installation`\n",
-    "\n",
-    "# 2: Package Setup\n",
-    "## *Goal: Create a virtual environment and install requirements per requirements.txt in order to run the UncertaintyForest class*\n",
-    "\n",
-    "### First, let's create the virtual environment \n",
-    "**Note:** that the following instructions were designed for Mac operating systems. If you're running another OS, look for the equivalent steps tailored to that OS.\n",
-    "\n",
-    "1. Open the command line on your local machine (called \"Terminal\" on Mac)\n",
-    "2. Navigate to the location where you'd like to put the virtual environment.\n",
-    "    1. Find a location in a file explorer (\"Finder\" on Mac)\n",
-    "    2. Type \"cd \" in the command prompt\n",
-    "    3. Drag and drop the folder where you'd like to place the virtual environment from the file explorer to the command line\n",
-    "    The command prompt should show something like:\n",
-    "    `bstraus@BS-Mac ~ % cd /Users/bstraus/Desktop `\n",
-    "3. Create the virtual environment by typing `python3 -m venv UncertaintyForestEnv`\n",
-    "\n",
-    "### Next, let's install the requirements for running the UncertaintyForest class\n",
-    "4. Activate the virtual environment by typing `source UncertaintyForestEnv/bin/activate`\n",
-    "5. Navigate to the folder `progressive-learning/docs/`. You can do this with the same process as in step 2 above.\n",
-    "5. Install necessary packages by typing `pip install -r requirements.txt`\n",
-    "6. You'll also want to install the following packages by typing the code below:\n",
-    "    1.`pip install jupyterlab`\n",
-    "    2.`pip install notebook`\n",
-    "    3.`pip install numpy scipy pandas scikit-learn matplotlib seaborn joblib keras tensorflow`\n",
-    "\n",
-    "You now have set up your virtual environment and installed necessary packages. Note that you'll need to activate your virtual environment each time you want to run things for this class. You can do this easily by repeating steps 1, 2, and 4.\n",
-    "\n",
-    "### You're done with part 2 of the tutorial!\n",
-    "\n",
-    "### Move on to part 3 (called \"UncertaintyForest_Tutorial_3-Data-Setup\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.7.0"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}