examples/time series classification/04-multi-label-generation.ipynb

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    "<h1 style=\"text-align: center;\">\n",
    "    🏷️ Multi-Label Multi-Class classification\n",
    "</h1>\n",
    "\n",
    "This notebook demonstrates how to generate **multi-label** streams with `streamgen`.\n",
    "\n",
    "The scikit-learn docs explain the concept well:\n",
    "\n",
    "> Multilabel classification (closely related to multioutput classification) is a classification task labeling each sample with `m` labels from `n_classes` possible classes, where `m´ can be 0 to ´n_classes´ inclusive. This can be thought of as predicting properties of a sample that are not mutually exclusive. Formally, a binary output is assigned to each class, for every sample. Positive classes are indicated with 1 and negative classes with 0 or -1. It is thus comparable to running ´n_classes´ binary classification tasks. This approach treats each label independently whereas multilabel classifiers may treat the multiple classes simultaneously, accounting for correlated behavior among them.\n",
    "\n",
    "we will start construct a simple multi-label use-case from our time series example.\n",
    "\n",
    "There is no obvious way to model such a use-case with a tree structure, so we will present several options.\n",
    "\n",
    "> 🤔 the `streamgen` API and the concept of a sampling tree are more suitable for the standard single-label multi-class task. Using the framework for the multi-label use-case will demonstrate the rougher edges of the framework, so we recommend this guide only for experienced users.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "outputs": [],
   "source": [
    "from functools import partial\n",
    "from pathlib import Path\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from streamgen.nodes import ClassLabelNode\n",
    "from streamgen.samplers.tree import SamplingTree\n",
    "from streamgen.transforms import noop, operate_on_index\n",
    "\n",
    "SEED = 42\n",
    "rng = np.random.default_rng(SEED)\n",
    "\n",
    "sns.set_theme()\n",
    "\n",
    "output_path = Path(\"./\")\n",
    "output_path.mkdir(parents=True, exist_ok=True)"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🌳 Using `streamgen` primitives\n",
    "\n",
    "previously, the label information was added in the leaf nodes, so there was no need to:\n",
    "\n",
    "+ adapt the implicit, default `None` input to the first transformation\n",
    "+ carry the label information through the sampling process\n",
    "\n",
    "In multi-label data generation, annotations can be added in every node in the tree, so we need to add the list of labels to the input and output of every node:\n",
    "\n",
    "```python\n",
    "def multi_label_transform(input: tuple[np.ndarray, list[str]], **kwargs) -> tuple[np.ndarray, list[str]]:\n",
    "    x, y = input\n",
    "\n",
    "    # do something with x and/or y\n",
    "\n",
    "    return (x, y)\n",
    "```\n",
    "\n",
    "Since most functions only transform either transform the sample `x` or the annotation `y`, `streamgen` provides a decorator `streamgen.transforms.operate_on_index` to automatically handle the correct passing of the part that are not modified. The following function passes the labels automatically to the output:\n",
    "\n",
    "```python\n",
    "@operate_on_index(0)\n",
    "def multi_label_transform(x: np.ndarray, **kwargs) -> np.ndarray:\n",
    "\n",
    "    # do something with x\n",
    "\n",
    "    return x # the decorator will return (x, y)\n",
    "```\n",
    "\n",
    "Using this decorator, we can re-use the time-series transformations from the first notebook:"
   ]
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   "source": [
    "# ➡️ transforms and generators\n",
    "\n",
    "\n",
    "@operate_on_index(0)\n",
    "def background(signal_, signal_length: int, offset: float, strength: float) -> np.ndarray:  # noqa: D103, ANN001, ARG001\n",
    "    return rng.normal(offset, strength, signal_length)\n",
    "\n",
    "\n",
    "@operate_on_index(0)\n",
    "def ramp(signal: np.ndarray, height: float, length: int) -> np.ndarray:  # noqa: D103\n",
    "    ramp_signal = np.zeros(len(signal))\n",
    "    ramp_start = rng.choice(range(len(signal) - length))\n",
    "    ramp_signal[ramp_start : ramp_start + length] = np.linspace(0.0, height, length)\n",
    "    return signal + ramp_signal\n",
    "\n",
    "\n",
    "@operate_on_index(0)\n",
    "def step(signal: np.ndarray, length: int, kernel_size: int) -> np.ndarray:  # noqa: D103\n",
    "    step_signal = np.zeros(len(signal))\n",
    "    step_start = rng.choice(range(len(signal) - length))\n",
    "    step_signal[step_start : step_start + length] = 1.0\n",
    "    kernel = np.ones(kernel_size) / kernel_size\n",
    "    step_signal = np.convolve(step_signal, kernel, mode=\"same\")\n",
    "    return signal + step_signal"
   ]
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   "source": [
    "Now we will construct a tree with these transformations. The resulting samples will either contain no pattern (`[]`), a single pattern (`[\"ramp\"]` or `[\"step\"]`), or both at the same time (`[\"ramp\", \"step\"]`).\n",
    "\n",
    "An alternative to the list representation of the labels is the *powerset approach*, where you convert the multi-label task into a single-label one by representing each combination of labels as a seperate class (´\"no-class\"´, ´\"ramp\"´, ´\"step\"´ and ´\"ramp-step\"´). This approach does not scale well to problems with a high cardinality and a big set of possible values. We will focus on the list representation of the labels to showcase the details of the label handling in streamgen.\n",
    "\n",
    "The default behavior of the shorthand string notation in the definition of a `streamgen.samplers.tree.SamplingTree` is to use the `streamgen.nodes.ClassLabelNode`, which by default just sets the label. For our task, we want those nodes to add/append the labels to the list of labels, so we need to tell streamgen to use these custom nodes when building the tree using the `string_node` argument.\n",
    "\n",
    "There are two ways to construct the class distributions:\n",
    "\n",
    "1. focus on the number of labels (cardinality) first and then sample which patterns to include.\n",
    "2. model the probabilities of the presence of each label\n",
    "\n",
    "We will first focus on the first modelling approach and later focus on the second one."
   ]
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  {
   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "MultiLabelNode = partial(ClassLabelNode, label_func=lambda input, label: (input[0], input[1] + [label]))  # noqa: A002\n",
    "\n",
    "transform_config = {\n",
    "    \"background.signal_length\": 512,\n",
    "    \"background.offset\": [0.0, 0.2, 0.5],\n",
    "    \"background.strength\": [0.1, 0.2, 0.4],\n",
    "    \"ramp.height\": 1.0,\n",
    "    \"ramp.length\": 128,\n",
    "    \"step.length\": 64,\n",
    "    \"step.kernel_size\": 1,\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"cardinality.probs\": [\n",
    "            [0.5, 0.3, 0.2],\n",
    "            [0.6, 0.2, 0.2],\n",
    "            [0.7, 0.2, 0.1],\n",
    "        ],\n",
    "        \"single label branch.probs\": [\n",
    "            [0.8, 0.2],\n",
    "            [0.6, 0.4],\n",
    "            [0.4, 0.6],\n",
    "        ],\n",
    "    }\n",
    "    | transform_config,\n",
    ")\n",
    "\n",
    "# 🎲🌳 tree of transformations\n",
    "tree = SamplingTree(\n",
    "    [\n",
    "        lambda _: (None, []),\n",
    "        background,\n",
    "        {\n",
    "            \"name\": \"cardinality\",\n",
    "            \"no label branch\": noop,\n",
    "            \"single label branch\": {\n",
    "                \"name\": \"single label branch\",\n",
    "                \"ramp\": [ramp, \"ramp\"],\n",
    "                \"step\": [step, \"step\"],\n",
    "            },\n",
    "            \"multi label branch\": [\n",
    "                ramp,\n",
    "                \"ramp\",\n",
    "                step,\n",
    "                \"step\",\n",
    "            ],\n",
    "        },\n",
    "    ],\n",
    "    df,\n",
    "    string_node=MultiLabelNode,\n",
    ")\n",
    "\n",
    "tree.to_svg(output_path / \"multi_label_tree_1\")"
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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = tree.sample()\n",
    "\n",
    "sns.lineplot(x).set_title(str(y));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we will construct a second tree, where we focus on modelling the probabilities of the individual patterns being present (instead of the cardinality). An elegant way to generate such scenarios is through the use of stochastic transformations, where the decision if a transformation is applied (and a corresponding label is added) is also sampled from a distribution. With streamgen, we can use branches to perform this sampling:\n",
    "\n",
    "```python\n",
    "{\n",
    "    \"name\": \"maybe apply step transformation\",\n",
    "    \"no step\": noop,\n",
    "    \"step\": [step, \"step\"],\n",
    "}\n",
    "```\n",
    "\n",
    "Generally, we want to add the same transformations to both branches, but unfortunately, in a tree, branches cannot be **merged** (which would create a more general, directed acyclic graph).\n",
    "\n",
    "> 🔮 other samplers like directed graphs of transformations might be added to `streamgen` in the future.\n",
    "\n",
    "For convenience, `streamgen` automatically copies and appends nodes that appear after the definition of branches to every branch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"450pt\" height=\"548pt\" viewBox=\"0.00 0.00 449.70 548.00\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 544)\">\n",
       "<title>tree</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-544 445.7,-544 445.7,4 -4,4\"/>\n",
       "<!-- 0x0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0x0</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"None\">\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"230.08\" cy=\"-522\" rx=\"62.54\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"230.08\" y=\"-516.58\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ &lt;lambda&gt;</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>0x1</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"{\n",
       "    'background.offset': 0.0,\n",
       "    'background.signal_length': 512,\n",
       "    'background.strength': 0.1\n",
       "}\">\n",
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       "<text text-anchor=\"middle\" x=\"230.08\" y=\"-444.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ background</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x0&#45;&gt;0x1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0x0-&gt;0x1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M230.08,-503.7C230.08,-496.41 230.08,-487.73 230.08,-479.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"233.58,-479.62 230.08,-469.62 226.58,-479.62 233.58,-479.62\"/>\n",
       "</g>\n",
       "<!-- 0x2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>0x2</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"probs=[0.8, 0.2]\">\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"230.08,-396 135.28,-378 230.08,-360 324.88,-378 230.08,-396\"/>\n",
       "<text text-anchor=\"middle\" x=\"230.08\" y=\"-372.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">🪴 maybe step</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x1&#45;&gt;0x2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>0x1-&gt;0x2</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M230.08,-431.7C230.08,-424.41 230.08,-415.73 230.08,-407.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"233.58,-407.62 230.08,-397.62 226.58,-407.62 233.58,-407.62\"/>\n",
       "</g>\n",
       "<!-- 0x3 -->\n",
       "<g id=\"node4\" class=\"node\">\n",
       "<title>0x3</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"None\">\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"179.08\" cy=\"-306\" rx=\"44.08\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"179.08\" y=\"-300.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ noop</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x2&#45;&gt;0x3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>0x2-&gt;0x3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M219.01,-361.81C212.86,-353.37 205.04,-342.63 197.95,-332.9\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"200.8,-330.87 192.08,-324.85 195.14,-334.99 200.8,-330.87\"/>\n",
       "</g>\n",
       "<!-- 0x8 -->\n",
       "<g id=\"node9\" class=\"node\">\n",
       "<title>0x8</title>\n",
       "<g id=\"a_node9\"><a xlink:title=\"{'step.kernel_size': 1, 'step.length': 64}\">\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"297.08\" cy=\"-306\" rx=\"40.09\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"297.08\" y=\"-300.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ step</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x2&#45;&gt;0x8 -->\n",
       "<g id=\"edge4\" class=\"edge\">\n",
       "<title>0x2-&gt;0x8</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M243.96,-362.5C252.67,-353.4 264.11,-341.44 274.15,-330.96\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"276.38,-333.69 280.77,-324.04 271.32,-328.85 276.38,-333.69\"/>\n",
       "</g>\n",
       "<!-- 0x4 -->\n",
       "<g id=\"node5\" class=\"node\">\n",
       "<title>0x4</title>\n",
       "<g id=\"a_node5\"><a xlink:title=\"probs=[0.1, 0.9]\">\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"148.08,-252 48.34,-234 148.08,-216 247.81,-234 148.08,-252\"/>\n",
       "<text text-anchor=\"middle\" x=\"148.08\" y=\"-228.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">🪴 maybe ramp</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x3&#45;&gt;0x4 -->\n",
       "<g id=\"edge5\" class=\"edge\">\n",
       "<title>0x3-&gt;0x4</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M171.57,-288.05C168.01,-280 163.66,-270.19 159.68,-261.2\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"162.95,-259.94 155.7,-252.21 156.55,-262.77 162.95,-259.94\"/>\n",
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       "<!-- 0x5 -->\n",
       "<g id=\"node6\" class=\"node\">\n",
       "<title>0x5</title>\n",
       "<g id=\"a_node6\"><a xlink:title=\"None\">\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"44.08\" cy=\"-162\" rx=\"44.08\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"44.08\" y=\"-156.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ noop</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x4&#45;&gt;0x5 -->\n",
       "<g id=\"edge6\" class=\"edge\">\n",
       "<title>0x4-&gt;0x5</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M128.03,-219.5C113.22,-209.54 92.78,-195.78 75.78,-184.34\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"77.73,-181.43 67.48,-178.75 73.83,-187.24 77.73,-181.43\"/>\n",
       "</g>\n",
       "<!-- 0x6 -->\n",
       "<g id=\"node7\" class=\"node\">\n",
       "<title>0x6</title>\n",
       "<g id=\"a_node7\"><a xlink:title=\"{'ramp.height': 1.0, 'ramp.length': 128}\">\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"150.08\" cy=\"-162\" rx=\"43.58\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"150.08\" y=\"-156.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ ramp</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x4&#45;&gt;0x6 -->\n",
       "<g id=\"edge7\" class=\"edge\">\n",
       "<title>0x4-&gt;0x6</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M148.57,-215.7C148.78,-208.41 149.03,-199.73 149.26,-191.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"152.76,-191.71 149.55,-181.62 145.76,-191.51 152.76,-191.71\"/>\n",
       "</g>\n",
       "<!-- 0x7 -->\n",
       "<g id=\"node8\" class=\"node\">\n",
       "<title>0x7</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"187.7,-102 100.45,-102 100.45,-78 187.7,-78 199.7,-90 187.7,-102\"/>\n",
       "<text text-anchor=\"middle\" x=\"150.08\" y=\"-84.58\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">🏷️ label=ramp</text>\n",
       "</g>\n",
       "<!-- 0x6&#45;&gt;0x7 -->\n",
       "<g id=\"edge8\" class=\"edge\">\n",
       "<title>0x6-&gt;0x7</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M150.08,-143.7C150.08,-136.41 150.08,-127.73 150.08,-119.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"153.58,-119.62 150.08,-109.62 146.58,-119.62 153.58,-119.62\"/>\n",
       "</g>\n",
       "<!-- 0x9 -->\n",
       "<g id=\"node10\" class=\"node\">\n",
       "<title>0x9</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"348.08,-246 266.08,-246 266.08,-222 348.08,-222 360.08,-234 348.08,-246\"/>\n",
       "<text text-anchor=\"middle\" x=\"313.08\" y=\"-228.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">🏷️ label=step</text>\n",
       "</g>\n",
       "<!-- 0x8&#45;&gt;0x9 -->\n",
       "<g id=\"edge9\" class=\"edge\">\n",
       "<title>0x8-&gt;0x9</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M301.03,-287.7C302.72,-280.32 304.73,-271.52 306.62,-263.25\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"310.01,-264.11 308.83,-253.58 303.19,-262.55 310.01,-264.11\"/>\n",
       "</g>\n",
       "<!-- 0xa -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>0xa</title>\n",
       "<g id=\"a_node11\"><a xlink:title=\"probs=[0.1, 0.9]\">\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"313.08,-180 213.34,-162 313.08,-144 412.81,-162 313.08,-180\"/>\n",
       "<text text-anchor=\"middle\" x=\"313.08\" y=\"-156.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">🪴 maybe ramp</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x9&#45;&gt;0xa -->\n",
       "<g id=\"edge10\" class=\"edge\">\n",
       "<title>0x9-&gt;0xa</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M313.08,-215.7C313.08,-208.41 313.08,-199.73 313.08,-191.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"316.58,-191.62 313.08,-181.62 309.58,-191.62 316.58,-191.62\"/>\n",
       "</g>\n",
       "<!-- 0xb -->\n",
       "<g id=\"node12\" class=\"node\">\n",
       "<title>0xb</title>\n",
       "<g id=\"a_node12\"><a xlink:title=\"None\">\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"286.08\" cy=\"-90\" rx=\"44.08\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"286.08\" y=\"-84.58\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ noop</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0xa&#45;&gt;0xb -->\n",
       "<g id=\"edge11\" class=\"edge\">\n",
       "<title>0xa-&gt;0xb</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M306.82,-144.76C303.84,-137.04 300.19,-127.59 296.79,-118.77\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"300.09,-117.61 293.23,-109.54 293.56,-120.13 300.09,-117.61\"/>\n",
       "</g>\n",
       "<!-- 0xc -->\n",
       "<g id=\"node13\" class=\"node\">\n",
       "<title>0xc</title>\n",
       "<g id=\"a_node13\"><a xlink:title=\"{'ramp.height': 1.0, 'ramp.length': 128}\">\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"392.08\" cy=\"-90\" rx=\"43.58\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"392.08\" y=\"-84.58\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ ramp</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0xa&#45;&gt;0xc -->\n",
       "<g id=\"edge12\" class=\"edge\">\n",
       "<title>0xa-&gt;0xc</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M329.45,-146.5C339.99,-137.15 353.94,-124.8 365.99,-114.12\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"368.06,-116.96 373.22,-107.71 363.42,-111.72 368.06,-116.96\"/>\n",
       "</g>\n",
       "<!-- 0xd -->\n",
       "<g id=\"node14\" class=\"node\">\n",
       "<title>0xd</title>\n",
       "<polygon fill=\"none\" stroke=\"black\" points=\"429.7,-30 342.45,-30 342.45,-6 429.7,-6 441.7,-18 429.7,-30\"/>\n",
       "<text text-anchor=\"middle\" x=\"392.08\" y=\"-12.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">🏷️ label=ramp</text>\n",
       "</g>\n",
       "<!-- 0xc&#45;&gt;0xd -->\n",
       "<g id=\"edge13\" class=\"edge\">\n",
       "<title>0xc-&gt;0xd</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M392.08,-71.7C392.08,-64.41 392.08,-55.73 392.08,-47.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"395.58,-47.62 392.08,-37.62 388.58,-47.62 395.58,-47.62\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"maybe step.probs\": [\n",
    "            [0.8, 0.2],\n",
    "            [0.6, 0.4],\n",
    "            [0.4, 0.6],\n",
    "        ],\n",
    "        \"maybe ramp.probs\": [\n",
    "            [0.1, 0.9],\n",
    "            [0.1, 0.9],\n",
    "            [0.4, 0.6],\n",
    "        ],\n",
    "    }\n",
    "    | transform_config,\n",
    ")\n",
    "\n",
    "# 🎲🌳 tree of transformations\n",
    "tree = SamplingTree(\n",
    "    [\n",
    "        lambda _: (None, []),\n",
    "        background,\n",
    "        {\n",
    "            \"name\": \"maybe step\",\n",
    "            \"no step\": noop,\n",
    "            \"step\": [step, \"step\"],\n",
    "        },\n",
    "        {\n",
    "            \"name\": \"maybe ramp\",\n",
    "            \"no ramp\": noop,\n",
    "            \"ramp\": [ramp, \"ramp\"],\n",
    "        },\n",
    "    ],\n",
    "    df,\n",
    "    string_node=MultiLabelNode,\n",
    ")\n",
    "\n",
    "tree.to_svg(output_path / \"multi_label_tree_2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7485bb7ed744db1846d453f19bec466",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tree.collect(100);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 🎲 Writing more expressive functions\n",
    "\n",
    "Another viable option is the inclusion of the stochastic application of the transform into the function definitions. This can drastically simplify the tree topology. In our simply example, we do not need any branches at all, falling back to a sequential composition of transformations (which can still be expressed by a `SamplingTree`).\n",
    "\n",
    "Since the transforms themselves take care of adding the labels, we neither need the function decorators nor the special labelling nodes from earlier:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"154pt\" height=\"260pt\" viewBox=\"0.00 0.00 154.04 260.00\">\n",
       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 256)\">\n",
       "<title>tree</title>\n",
       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-256 150.04,-256 150.04,4 -4,4\"/>\n",
       "<!-- 0x0 -->\n",
       "<g id=\"node1\" class=\"node\">\n",
       "<title>0x0</title>\n",
       "<g id=\"a_node1\"><a xlink:title=\"None\">\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"73.02\" cy=\"-234\" rx=\"62.54\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"73.02\" y=\"-228.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ &lt;lambda&gt;</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x1 -->\n",
       "<g id=\"node2\" class=\"node\">\n",
       "<title>0x1</title>\n",
       "<g id=\"a_node2\"><a xlink:title=\"{\n",
       "    'background.offset': 0.0,\n",
       "    'background.signal_length': 512,\n",
       "    'background.strength': 0.1\n",
       "}\">\n",
       "<ellipse fill=\"none\" stroke=\"black\" cx=\"73.02\" cy=\"-162\" rx=\"69.03\" ry=\"18\"/>\n",
       "<text text-anchor=\"middle\" x=\"73.02\" y=\"-156.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ background</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x0&#45;&gt;0x1 -->\n",
       "<g id=\"edge1\" class=\"edge\">\n",
       "<title>0x0-&gt;0x1</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M73.02,-215.7C73.02,-208.41 73.02,-199.73 73.02,-191.54\"/>\n",
       "<polygon fill=\"black\" stroke=\"black\" points=\"76.52,-191.62 73.02,-181.62 69.52,-191.62 76.52,-191.62\"/>\n",
       "</g>\n",
       "<!-- 0x2 -->\n",
       "<g id=\"node3\" class=\"node\">\n",
       "<title>0x2</title>\n",
       "<g id=\"a_node3\"><a xlink:title=\"{'maybe_ramp.height': 1.0, 'maybe_ramp.length': 64, 'maybe_ramp.p_apply': 0.8}\">\n",
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       "<text text-anchor=\"middle\" x=\"73.02\" y=\"-84.58\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ maybe_ramp</text>\n",
       "</a>\n",
       "</g>\n",
       "</g>\n",
       "<!-- 0x1&#45;&gt;0x2 -->\n",
       "<g id=\"edge2\" class=\"edge\">\n",
       "<title>0x1-&gt;0x2</title>\n",
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       "<!-- 0x3 -->\n",
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       "<title>0x3</title>\n",
       "<g id=\"a_node4\"><a xlink:title=\"{\n",
       "    'maybe_step.kernel_size': 1,\n",
       "    'maybe_step.length': 32,\n",
       "    'maybe_step.p_apply': 0.9\n",
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       "<text text-anchor=\"middle\" x=\"73.02\" y=\"-12.57\" font-family=\"Times New Roman,serif\" font-size=\"14.00\">➡️ maybe_step</text>\n",
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       "</g>\n",
       "<!-- 0x2&#45;&gt;0x3 -->\n",
       "<g id=\"edge3\" class=\"edge\">\n",
       "<title>0x2-&gt;0x3</title>\n",
       "<path fill=\"none\" stroke=\"black\" d=\"M73.02,-71.7C73.02,-64.41 73.02,-55.73 73.02,-47.54\"/>\n",
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       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def maybe_ramp(input: tuple[np.ndarray, list[str]], height: float, length: int, p_apply: float = 0.5) -> np.ndarray:  # noqa: D103, A002\n",
    "    signal, labels = input\n",
    "\n",
    "    if rng.uniform() < p_apply:\n",
    "        ramp_signal = np.zeros(len(signal))\n",
    "        ramp_start = rng.choice(range(len(signal) - length))\n",
    "        ramp_signal[ramp_start : ramp_start + length] = np.linspace(0.0, height, length)\n",
    "        signal += ramp_signal\n",
    "        labels.append(\"ramp\")\n",
    "\n",
    "    return signal, labels\n",
    "\n",
    "\n",
    "def maybe_step(input: tuple[np.ndarray, list[str]], length: int, kernel_size: int, p_apply: float = 0.5) -> np.ndarray:  # noqa: D103, A002\n",
    "    signal, labels = input\n",
    "\n",
    "    if rng.uniform() < p_apply:\n",
    "        step_signal = np.zeros(len(signal))\n",
    "        step_start = rng.choice(range(len(signal) - length))\n",
    "        step_signal[step_start : step_start + length] = 1.0\n",
    "        kernel = np.ones(kernel_size) / kernel_size\n",
    "        signal += np.convolve(step_signal, kernel, mode=\"same\")\n",
    "        labels.append(\"step\")\n",
    "\n",
    "    return signal, labels\n",
    "\n",
    "\n",
    "df = pd.DataFrame(\n",
    "    {\n",
    "        \"maybe_ramp.height\": 1.0,\n",
    "        \"maybe_ramp.length\": 64,\n",
    "        \"maybe_ramp.p_apply\": 0.8,\n",
    "        \"maybe_step.length\": 32,\n",
    "        \"maybe_step.kernel_size\": 1,\n",
    "        \"maybe_step.p_apply\": 0.9,\n",
    "    }\n",
    "    | transform_config,\n",
    ")\n",
    "\n",
    "# 🎲🌳 tree of transformations\n",
    "tree = SamplingTree(\n",
    "    [\n",
    "        lambda _: (None, []),\n",
    "        background,\n",
    "        maybe_ramp,\n",
    "        maybe_step,\n",
    "    ],\n",
    "    df,\n",
    ")\n",
    "\n",
    "tree.to_svg(output_path / \"multi_label_tree_3\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x, y = tree.sample()\n",
    "\n",
    "sns.lineplot(x).set_title(str(y));"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "streamgen-WTmPm9Y--py3.11",
   "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}