From 7130108a8dd59dccead21d683ab8e1ab5f22b107 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Braulio=20R=C3=ADos?= Date: Thu, 26 Oct 2023 11:50:09 -0300 Subject: [PATCH 1/9] Move graph mode section to the end of user guide --- docs/src/user_guide.ipynb | 2070 +++++++++++++++++++------------------ 1 file changed, 1037 insertions(+), 1033 deletions(-) diff --git a/docs/src/user_guide.ipynb b/docs/src/user_guide.ipynb index 34a55c8e4..d6550666a 100644 --- a/docs/src/user_guide.ipynb +++ b/docs/src/user_guide.ipynb @@ -209,1960 +209,1964 @@ { "attachments": {}, "cell_type": "markdown", - "id": "0dd8a9a5-19b4-4898-b5af-794dbd4ac1eb", - "metadata": {}, + "id": "b73d3011", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 + }, "source": [ - "## Eager mode vs Graph mode\n", "\n", - "Temporian has two execution modes: **eager** and **graph**. In eager mode, operators are applied immediately. This mode is useful for learning Temporian, for iterative and interactive development, and for lightweight/small data use cases where performance isn't a priority.\n", + "## Time units\n", "\n", - "In graph mode, operators are combined together into \"Temporian programs\" before being executed. Graph mode is more efficient and it consumes less memory. Temporian programs can be saved, inspected, and distributed by users.\n", + "In Temporian, times are always represented by a float64 value. Users have the freedom to choose the semantic to this value. For example, the time can be the number of nanoseconds since the start of the day, the number of cycles of a process, the number of years since the big bang, or the number of seconds since January 1, 1970, at 00:00:00 UTC, also known as Unix or POSIX time.\n", "\n", - "Migrating a Temporian program from eager to graph mode is easy and requires little work. Most of the time, adding a `@tp.compile` annotation is enough. Therefore, it is recommended to develop programs in eager mode and then to productize them in graph mode.\n", + "To ease the feature engineering of dates, Temporian contains a set of _calendar operators_. These operators specialize in creating features from dates and datetimes. For instance, the `EventSet.calendar_hour()` operator returns the hour of the date in the range `0-23`.\n", "\n", - "Next, we see a the same program written three times: First, in eager mode, then in graph mode using `@tp.compile`, and finally in graph mode without `@tp.compile`." + "Calendar operators require the time in their inputs to be Unix time, so applying them on non-Unix timestamps will raise errors. Temporian can sometimes automatically recognize if input timestamps correspond to Unix time (e.g. when an `EventSet` is created from a pandas DataFrame with a datetime column, or when passing a list of datetime objects as timestamps in `EventSet`'s constructor). If creating `EventSets` manually and passing floats directly to `timestamps`, you need to explicitly specify whether they correspond to Unix times or not via the `is_unix_timestamp` argument." ] }, { "cell_type": "code", "execution_count": null, - "id": "d5405837-c801-40e9-8b41-4d4dc901d429", + "id": "92ec9711", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.315244Z", - "iopub.status.busy": "2023-07-24T15:00:57.315083Z", - "iopub.status.idle": "2023-07-24T15:00:57.417894Z", - "shell.execute_reply": "2023-07-24T15:00:57.417481Z" - } + "iopub.execute_input": "2023-07-24T15:00:57.666655Z", + "iopub.status.busy": "2023-07-24T15:00:57.666536Z", + "iopub.status.idle": "2023-07-24T15:00:57.679313Z", + "shell.execute_reply": "2023-07-24T15:00:57.678950Z" + }, + "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "# Eager mode\n", - "#\n", - "# Note: This Temporian program contains three operators: two \"simple_moving_average\" and one \"tp.substract\" operators.\n", - "result = evset.simple_moving_average(window_length=0.5) - evset.simple_moving_average(window_length=1.0)\n", - "result.plot()" + "a_evset = tp.event_set(\n", + " timestamps=[\n", + " pd.to_datetime(\"Monday Mar 13 12:00:00 2023\", utc=True),\n", + " pd.to_datetime(\"Tuesday Mar 14 12:00:00 2023\", utc=True),\n", + " pd.to_datetime(\"Friday Mar 17 00:00:01 2023\", utc=True),\n", + " ],\n", + " features={\n", + " \"feature_1\": [1, 2, 3],\n", + " \"feature_2\": [\"a\", \"b\", \"c\"],\n", + " },\n", + ")\n", + "a_node = a_evset.node()\n", + "b_node = tp.glue(a_node, a_node.calendar_day_of_week())\n", + "b_node.run(a_evset)" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "9c854408-0804-4435-8d44-f521ef1b379e", + "attachments": {}, + "cell_type": "markdown", + "id": "7380c9c7", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.420139Z", - "iopub.status.busy": "2023-07-24T15:00:57.419972Z", - "iopub.status.idle": "2023-07-24T15:00:57.522730Z", - "shell.execute_reply": "2023-07-24T15:00:57.522286Z" - } + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 }, - "outputs": [], "source": [ - "# Graph mode with @tp.compile\n", "\n", - "@tp.compile\n", - "def my_function(x):\n", - " return x.simple_moving_average(window_length=0.5) - x.simple_moving_average(window_length=1.0)\n", + "Temporian accepts time inputs in various formats, including integer, float, Python date or datetime, NumPy datetime, and pandas datetime. Date and datetime objects are internally converted to floats as Unix time in seconds, compatible with the calendar operators.\n", "\n", - "result = my_function(evset)\n", - " \n", - "result.plot()" + "Operators can take _durations_ as input arguments. For example, the simple moving average operator takes a `window_length` argument. Temporian exposes several utility functions to help creating those duration arguments when using Unix timestamps:" ] }, { "cell_type": "code", "execution_count": null, - "id": "e4aa008f-aa8e-486f-a96c-854fb5b2bd83", + "id": "508c3f0c", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.525125Z", - "iopub.status.busy": "2023-07-24T15:00:57.524948Z", - "iopub.status.idle": "2023-07-24T15:00:57.629039Z", - "shell.execute_reply": "2023-07-24T15:00:57.628643Z" + "iopub.execute_input": "2023-07-24T15:00:57.681429Z", + "iopub.status.busy": "2023-07-24T15:00:57.681281Z", + "iopub.status.idle": "2023-07-24T15:00:57.684114Z", + "shell.execute_reply": "2023-07-24T15:00:57.683752Z" } }, "outputs": [], "source": [ - "# Graph model without @tp.compile\n", + "a = tp.input_node(features=[(\"feature_1\", tp.float64)])\n", "\n", - "input_node = tp.input_node([(\"value\", tp.float64)])\n", - "# Or input_node = tp.input_node(evset.schema.features)\n", + "# Define a 1-day moving average.\n", + "b = a.simple_moving_average(window_length=tp.duration.days(1))\n", "\n", - "result_node = input_node.simple_moving_average(window_length=0.5) - input_node.simple_moving_average(window_length=1.0)\n", - "result = tp.run(result_node, {input_node: evset}, verbose=1)\n", - " \n", - "result.plot()" + "# Equivalent.\n", + "b = a.simple_moving_average(window_length=24 * 60 * 60)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "a6a5a017-9dbe-4782-bc73-9b278ec622b0", - "metadata": {}, + "id": "6c99993c", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 + }, "source": [ - "## More on graph mode\n", - "\n", - "**Remark:** While you will likely use the graph mode with `@tp.compile` , it is useful for you to understand the graph model without `@tp.compile`.\n", - "\n", - "A Temporian program is a graph of [EventSetNodes][temporian.EventSetNode] connecting operators. A graph is executed with the function `tp.run(, )`.\n", "\n", - "\"eager\n", + "## Plotting\n", "\n", - "The `` can be specified as an `EventSetNode`, a list of `EventSetNodes`, or a dictionary of names to `EventSetNodes`, and the result of `tp.run()` will be of the same type. For example, if `` is a list of three `EventSetNodes`, the result will be a list of the three corresponding `EventSets`.\n", + "Data visualization is crucial for gaining insights into data and the system it represents. It also helps in detecting unexpected behavior and issues, making debugging and iterative development easier.\n", "\n", - "The `` can be specified as:\n", + "Temporian provides two plotting functions for data visualization: `evset.plot()` and `tp.plot()`.\n", "\n", - "- A dictionary of `EventSetNodes` to `EventSets`, or \n", - "- A dictionary of names to `EventSets`, or,\n", - "- A list of `EventSets`, or\n", - "- A single `EventSet`\n", + "The `evset.plot()` function is shorter to write and is used for displaying a single `EventSet`, while the `tp.plot()` function is used for displaying multiple `EventSets` together. This function is particularly useful when `EventSets` are indexed (see [Index, horizontal and vertical operators](#indexes-horizontal-and-vertical-operators)) or have different samplings (see [Sampling](#sampling)).\n", "\n", - "This lets Temporian know the `EventSetNodes` of the graph that each input `EventSet` corresponds to. If `` is a dictionary of names to `EventSets`, the names must match the names of `EventSetNodes` in the graph. If `` is a list or a single `EventSet`, the names of those `EventSets` must do the same. If we specify the inputs as a dictionary, we could skip passing a name to `a_evset`." + "Here's an example of using the `evset.plot()` function:" ] }, { "cell_type": "code", "execution_count": null, - "id": "dd4142a0-df47-4ca3-aafe-7a105b4f2deb", + "id": "cbf42f42", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.631693Z", - "iopub.status.busy": "2023-07-24T15:00:57.631532Z", - "iopub.status.idle": "2023-07-24T15:00:57.636094Z", - "shell.execute_reply": "2023-07-24T15:00:57.635717Z" + "iopub.execute_input": "2023-07-24T15:00:57.686202Z", + "iopub.status.busy": "2023-07-24T15:00:57.685957Z", + "iopub.status.idle": "2023-07-24T15:00:57.884670Z", + "shell.execute_reply": "2023-07-24T15:00:57.884262Z" } }, "outputs": [], "source": [ - "input_node = tp.input_node([(\"value\", tp.float64)])\n", - "result_1_node = input_node.simple_moving_average(window_length=0.5)\n", - "result_2_node = input_node.simple_moving_average(window_length=1.0)\n", - "result_3_node = result_1_node - result_2_node\n", - "\n", - "result = tp.run([result_1_node,result_2_node, result_3_node], {input_node: evset})\n", - " \n", - "print(result)" + "evset = tp.event_set(\n", + "\ttimestamps=[1, 2, 3, 4, 5],\n", + "\tfeatures={\n", + " \"feature_1\": [0.5, 0.6, 0.4, 0.4, 0.9],\n", + " \"feature_2\": [\"red\", \"blue\", \"red\", \"blue\", \"green\"]\n", + " }\n", + ")\n", + "evset.plot()" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "32dd49a8-8847-4df1-be17-b844b92f4211", - "metadata": {}, + "id": "39dc9a92", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 + }, "source": [ - "**Remarks:**\n", "\n", - "- It's important to distinguish between a `tp.EventSet`, such as `evset`, that contains data, and a `tp.EventSetNode`, like `input_node`, that connect operators together and compose the computation graph, but do not contain data.\n", - "- No computation is performed when defining the graph (i.e., when calling the operator functions). All computation is done during `tp.run()`.\n", - "- In `tp.run()`, the second argument defines a mapping between input `EventSetNodes` and `EventSets`. If all necessary input `EventSetNodes` are not fed, an error will be raised.\n", - "- In most cases you will only pass `EventSets` that correspond to the graph's input `EventSetNodes`, but Temporian also supports passing `EventSets` to intermediate `EventSetNodes` in the graph." + "By default, the plotting style is selected automatically based on the data.\n", + "\n", + "For example, uniformly sampled numerical features (i.e., time series) are plotted with a continuous line, while non-uniformly sampled values are plotted with markers. Those and other behaviors can be controlled via the function's arguments.\n", + "\n", + "Here's an example of using the `evset.plot()` function with options:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82552e04", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-24T15:00:57.887017Z", + "iopub.status.busy": "2023-07-24T15:00:57.886848Z", + "iopub.status.idle": "2023-07-24T15:00:58.113203Z", + "shell.execute_reply": "2023-07-24T15:00:58.112793Z" + } + }, + "outputs": [], + "source": [ + "figure = evset.plot(\n", + " style=\"marker\",\n", + " width_px=400,\n", + " min_time=2,\n", + " max_time=10,\n", + " return_fig=True,\n", + ")" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "c7e808b2-4091-447a-a6a5-66fcf5f037a9", - "metadata": {}, + "id": "37bd0475", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 + }, "source": [ - "The `@tp.compile` annotation takes a function inputing and outputing `tp.EventSetNode`, and automatically calls `tp.run` on the result of the function if a `tp.EventSet` is provided as input." + "\n", + "The plots are static images by default. However, interactive plotting can be very powerful. To enable interactive plotting, use `interactive=True`. Note that interactive plotting requires the `bokeh` Python library to be installed." ] }, { "cell_type": "code", "execution_count": null, - "id": "b0b0220b-9869-454c-8f3b-afeabaca06bc", + "id": "37feddd0", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.638508Z", - "iopub.status.busy": "2023-07-24T15:00:57.638232Z", - "iopub.status.idle": "2023-07-24T15:00:57.642016Z", - "shell.execute_reply": "2023-07-24T15:00:57.641623Z" + "iopub.execute_input": "2023-07-24T15:00:58.115589Z", + "iopub.status.busy": "2023-07-24T15:00:58.115426Z", + "iopub.status.idle": "2023-07-24T15:01:00.005021Z", + "shell.execute_reply": "2023-07-24T15:01:00.004601Z" } }, "outputs": [], "source": [ - "@tp.compile\n", - "def my_function(x : tp.types.EventSetOrNode) -> tp.types.EventSetOrNode:\n", - " return x.simple_moving_average(window_length=0.5)\n", - "\n", - "# Feeding an EventSet\n", - "input_evset = tp.event_set(timestamps=[1, 2, 3],features={\"value\": [5., 6., 7.]})\n", - "assert isinstance(my_function(input_evset), tp.EventSet)\n", + "!pip install bokeh -q\n", "\n", - "# Feeding an EventSetNode\n", - "input_node = tp.input_node([(\"value\", tp.float64)])\n", - "assert isinstance(my_function(input_node), tp.EventSetNode)" + "evset.plot(interactive=True)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "c0f02853-718f-4c9e-8945-b25e74b548ae", - "metadata": {}, + "id": "cf4d0c27", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 + }, "source": [ - "Importantly, variables in a `tp.compile` function are `EventSetNode` and not `EventSet`. Therefore, you cannot directly access the event set data.\n", "\n", - "In addition, the compiled function execution first generates the graph. The graph is then executed. In the next example, the compiled function generates a graph with 10 operators." + "## Feature naming\n", + "\n", + "Each feature is identified by a name, and the list of features is available through the `features` property of an `EventSetNode`." ] }, { "cell_type": "code", "execution_count": null, - "id": "945d8f39-18ad-44df-a4d2-9d8986e7825b", + "id": "38ff0e20", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.644118Z", - "iopub.status.busy": "2023-07-24T15:00:57.643976Z", - "iopub.status.idle": "2023-07-24T15:00:57.646561Z", - "shell.execute_reply": "2023-07-24T15:00:57.646187Z" + "iopub.execute_input": "2023-07-24T15:01:00.007393Z", + "iopub.status.busy": "2023-07-24T15:01:00.007171Z", + "iopub.status.idle": "2023-07-24T15:01:00.010463Z", + "shell.execute_reply": "2023-07-24T15:01:00.010135Z" } }, "outputs": [], "source": [ - "@tp.compile\n", - "def my_function(x : tp.EventSetNode) -> tp.EventSetNode:\n", - " for i in range(10):\n", - " x = x.simple_moving_average(window_length=i+1)\n", - " return x" + "events = tp.event_set(\n", + "\ttimestamps=[1,2,3,4,5],\n", + "\tfeatures={\n", + "\t \"feature_1\": [0.5, 0.6, 0.4, 0.4, 0.9],\n", + "\t \"feature_2\": [1.0, 2.0, 3.0, 2.0, 1.0]}\n", + " )\n", + "node = events.node()\n", + "print(node.features)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "4c46c4ee-a8ff-4b9b-9ced-acbee202d0a3", - "metadata": {}, + "id": "2cd29020", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 + }, "source": [ - "You can create a compiled function with a `if`. However, the condition of the `if` cannot depend on the EventSet data." + "\n", + "Most operators do not change the input feature's names." ] }, { "cell_type": "code", "execution_count": null, - "id": "941a5fc4-edfc-4fba-8bc8-22f3a7387e91", + "id": "838449d4", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.648616Z", - "iopub.status.busy": "2023-07-24T15:00:57.648475Z", - "iopub.status.idle": "2023-07-24T15:00:57.651921Z", - "shell.execute_reply": "2023-07-24T15:00:57.651456Z" + "iopub.execute_input": "2023-07-24T15:01:00.012599Z", + "iopub.status.busy": "2023-07-24T15:01:00.012475Z", + "iopub.status.idle": "2023-07-24T15:01:00.015736Z", + "shell.execute_reply": "2023-07-24T15:01:00.015395Z" } }, "outputs": [], "source": [ - "@tp.compile\n", - "def my_function(x : tp.types.EventSetOrNode, a:bool) -> tp.types.EventSetOrNode:\n", - " if a:\n", - " return x.rename(\"a_branch\")\n", - " else:\n", - " return x.rename(\"non_a_branch\")\n", - "\n", - "print(my_function(input_evset, a=True).schema.features)\n", - "\n", - "print(my_function(input_evset, a=False).schema.features)" + "node.moving_sum(window_length=10).features" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "3bf624e6-6b1c-4806-8024-8beebd30fc0b", - "metadata": {}, + "id": "b70cc298", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 + }, "source": [ - "If you want to create a program conditional on EventSet data, you can use `EventSet.filter()`." + "\n", + "Some operators combine two input features with different names, in which case the output name is also combined." ] }, { "cell_type": "code", "execution_count": null, - "id": "a9ab68f3-91a6-445e-99c2-7f2379185f21", + "id": "b10fdec3", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.654090Z", - "iopub.status.busy": "2023-07-24T15:00:57.653930Z", - "iopub.status.idle": "2023-07-24T15:00:57.658773Z", - "shell.execute_reply": "2023-07-24T15:00:57.658419Z" + "iopub.execute_input": "2023-07-24T15:01:00.017898Z", + "iopub.status.busy": "2023-07-24T15:01:00.017753Z", + "iopub.status.idle": "2023-07-24T15:01:00.021061Z", + "shell.execute_reply": "2023-07-24T15:01:00.020666Z" } }, "outputs": [], "source": [ - "@tp.compile\n", - "def my_function(x : tp.types.EventSetOrNode) -> tp.types.EventSetOrNode:\n", - " return x[\"value\"].filter(x[\"condition\"])\n", - "\n", - "my_function(tp.event_set(\n", - "\ttimestamps=[1,2,3],\n", - "\tfeatures={\n", - " \"value\": [10, 11, 12],\n", - " \"condition\":[True, True, False]}\n", - "))" + "result = node[\"feature_1\"] * node[\"feature_2\"]\n", + "result.features" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "c7ef8b32-a08d-46e2-aa8e-5cad3d5421e0", - "metadata": {}, + "id": "87731369", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 + }, "source": [ - "To simplify its usage when the graph contains a single output `EventSetNode`, `node.run(...)` is equivalent to `tp.run(node, ...)`." + "\n", + "The calendar operators don't depend on input features but on the timestamps, so the output feature name doesn't\n", + "relate to the input feature names." ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "afa9c5d2-1efe-440d-bf45-366c0b389b5a", - "metadata": {}, + "cell_type": "code", + "execution_count": null, + "id": "4182d3f8", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-24T15:01:00.023467Z", + "iopub.status.busy": "2023-07-24T15:01:00.023325Z", + "iopub.status.idle": "2023-07-24T15:01:00.026531Z", + "shell.execute_reply": "2023-07-24T15:01:00.026132Z" + } + }, + "outputs": [], "source": [ - "\n", - "\n", - "\n", - "**Warning:** It is more efficient to run multiple output `EventSetNodes` together with `tp.run()` than to run them separately with `node_1.run(...)`, `node_2.run(...)`, etc." + "date_events = tp.event_set(\n", + "\ttimestamps=[\"2020-02-15\", \"2020-06-20\"],\n", + "\tfeatures={\"some_feature\": [10, 20]}\n", + " )\n", + "date_node = date_events.node()\n", + "print(date_node.calendar_month().features)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "d78f320b", + "id": "61127d7f", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "Previously, we defined the input of the graph with `tp.input_node()`. This way of listing features manually and their respective data type is cumbersome.\n", "\n", - "If an `EventSet` is available (i.e., data is available) this step can be changed to use `evset.node()` instead, which will return an `EventSetNode` that is compatible with it. This is especially useful when creating `EventSets` from existing data, such as pandas DataFrames or CSV files." + "You can modify feature names using the `EventSet.rename()` and `EventSet.prefix()` operators. `EventSet.rename()` changes the name of features, while `EventSet.prefix()` adds a prefix in front of existing feature names. Note that they do not modify the content of the input `EventSet`, but return a new one with the modified feature names." ] }, { "cell_type": "code", "execution_count": null, - "id": "d4fc33b0", + "id": "bc36bc1f", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.661104Z", - "iopub.status.busy": "2023-07-24T15:00:57.660955Z", - "iopub.status.idle": "2023-07-24T15:00:57.664631Z", - "shell.execute_reply": "2023-07-24T15:00:57.664234Z" - }, - "lines_to_next_cell": 0 + "iopub.execute_input": "2023-07-24T15:01:00.029017Z", + "iopub.status.busy": "2023-07-24T15:01:00.028848Z", + "iopub.status.idle": "2023-07-24T15:01:00.031960Z", + "shell.execute_reply": "2023-07-24T15:01:00.031429Z" + } }, "outputs": [], "source": [ - "# Define an EventSet.\n", - "a_evset = tp.event_set(\n", - "\ttimestamps=[0, 1, 2],\n", - "\tfeatures={\n", - " \"feature_1\": [1.0, 2.0, 3.0],\n", - " \"feature_2\": [\"hello\", \"little\", \"dog\"],\n", - " \"feature_3\": [\"A\", \"A\", \"B\"],\n", - "\t}\n", - ")\n", - "\n", - "# The following three statements are (almost) equivalent.\n", - "a_node = tp.input_node(\n", - " features=[\n", - " (\"feature_1\", tp.float64),\n", - " (\"feature_2\", tp.str_),\n", - " ],\n", - " indexes=[(\"feature_3\", tp.str_)])\n", - "\n", - "a_node = tp.input_node(\n", - " features=a_evset.schema.features,\n", - " indexes=a_evset.schema.indexes\n", + "# Rename a single feature.\n", + "renamed_f1 = node[\"feature_1\"].rename(\"renamed_1\")\n", + "print(renamed_f1.features)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a44f6e7a", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-24T15:01:00.042574Z", + "iopub.status.busy": "2023-07-24T15:01:00.042259Z", + "iopub.status.idle": "2023-07-24T15:01:00.045691Z", + "shell.execute_reply": "2023-07-24T15:01:00.044933Z" + } + }, + "outputs": [], + "source": [ + "# Rename all features.\n", + "renamed_node = node.rename(\n", + " {\"feature_1\": \"renamed_1\", \"feature_2\": \"renamed_2\"}\n", ")\n", - " \n", - "a_node = a_evset.node()" + "print(renamed_node.features)" ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "b73d3011", + "cell_type": "code", + "execution_count": null, + "id": "af6103fc", "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 + "execution": { + "iopub.execute_input": "2023-07-24T15:01:00.048841Z", + "iopub.status.busy": "2023-07-24T15:01:00.048430Z", + "iopub.status.idle": "2023-07-24T15:01:00.051757Z", + "shell.execute_reply": "2023-07-24T15:01:00.051319Z" + } }, + "outputs": [], "source": [ - "\n", - "## Time units\n", - "\n", - "In Temporian, times are always represented by a float64 value. Users have the freedom to choose the semantic to this value. For example, the time can be the number of nanoseconds since the start of the day, the number of cycles of a process, the number of years since the big bang, or the number of seconds since January 1, 1970, at 00:00:00 UTC, also known as Unix or POSIX time.\n", - "\n", - "To ease the feature engineering of dates, Temporian contains a set of _calendar operators_. These operators specialize in creating features from dates and datetimes. For instance, the `EventSet.calendar_hour()` operator returns the hour of the date in the range `0-23`.\n", - "\n", - "Calendar operators require the time in their inputs to be Unix time, so applying them on non-Unix timestamps will raise errors. Temporian can sometimes automatically recognize if input timestamps correspond to Unix time (e.g. when an `EventSet` is created from a pandas DataFrame with a datetime column, or when passing a list of datetime objects as timestamps in `EventSet`'s constructor). If creating `EventSets` manually and passing floats directly to `timestamps`, you need to explicitly specify whether they correspond to Unix times or not via the `is_unix_timestamp` argument." + "# Prefix a single feature.\n", + "prefixed_f1 = node[\"feature_1\"].prefix(\"prefixed.\")\n", + "print(prefixed_f1.features)" ] }, { "cell_type": "code", "execution_count": null, - "id": "92ec9711", + "id": "3126cbed", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.666655Z", - "iopub.status.busy": "2023-07-24T15:00:57.666536Z", - "iopub.status.idle": "2023-07-24T15:00:57.679313Z", - "shell.execute_reply": "2023-07-24T15:00:57.678950Z" - }, - "lines_to_next_cell": 0 + "iopub.execute_input": "2023-07-24T15:01:00.053697Z", + "iopub.status.busy": "2023-07-24T15:01:00.053582Z", + "iopub.status.idle": "2023-07-24T15:01:00.056089Z", + "shell.execute_reply": "2023-07-24T15:01:00.055703Z" + } }, "outputs": [], "source": [ - "a_evset = tp.event_set(\n", - " timestamps=[\n", - " pd.to_datetime(\"Monday Mar 13 12:00:00 2023\", utc=True),\n", - " pd.to_datetime(\"Tuesday Mar 14 12:00:00 2023\", utc=True),\n", - " pd.to_datetime(\"Friday Mar 17 00:00:01 2023\", utc=True),\n", - " ],\n", - " features={\n", - " \"feature_1\": [1, 2, 3],\n", - " \"feature_2\": [\"a\", \"b\", \"c\"],\n", - " },\n", - ")\n", - "a_node = a_evset.node()\n", - "b_node = tp.glue(a_node, a_node.calendar_day_of_week())\n", - "b_node.run(a_evset)" + "# Prefix all features.\n", + "prefixed_node = node.prefix(\"prefixed.\")\n", + "print(prefixed_node.features)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "7380c9c7", + "id": "085e79c3", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ "\n", - "Temporian accepts time inputs in various formats, including integer, float, Python date or datetime, NumPy datetime, and pandas datetime. Date and datetime objects are internally converted to floats as Unix time in seconds, compatible with the calendar operators.\n", - "\n", - "Operators can take _durations_ as input arguments. For example, the simple moving average operator takes a `window_length` argument. Temporian exposes several utility functions to help creating those duration arguments when using Unix timestamps:" + "It is recommended to use `EventSet.rename()` and `EventSet.prefix()` to organize your data, and avoid duplicated feature names." ] }, { "cell_type": "code", "execution_count": null, - "id": "508c3f0c", + "id": "852abbdd", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.681429Z", - "iopub.status.busy": "2023-07-24T15:00:57.681281Z", - "iopub.status.idle": "2023-07-24T15:00:57.684114Z", - "shell.execute_reply": "2023-07-24T15:00:57.683752Z" + "iopub.execute_input": "2023-07-24T15:01:00.058375Z", + "iopub.status.busy": "2023-07-24T15:01:00.058188Z", + "iopub.status.idle": "2023-07-24T15:01:00.061323Z", + "shell.execute_reply": "2023-07-24T15:01:00.060844Z" } }, "outputs": [], "source": [ - "a = tp.input_node(features=[(\"feature_1\", tp.float64)])\n", - "\n", - "# Define a 1-day moving average.\n", - "b = a.simple_moving_average(window_length=tp.duration.days(1))\n", - "\n", - "# Equivalent.\n", - "b = a.simple_moving_average(window_length=24 * 60 * 60)" + "sma_7_node = node.simple_moving_average(tp.duration.days(7)).prefix(\"sma_7.\")\n", + "sma_14_node = node.simple_moving_average(tp.duration.days(14)).prefix(\"sma_14.\")" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "6c99993c", + "id": "ac25eb4a", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ "\n", - "## Plotting\n", - "\n", - "Data visualization is crucial for gaining insights into data and the system it represents. It also helps in detecting unexpected behavior and issues, making debugging and iterative development easier.\n", - "\n", - "Temporian provides two plotting functions for data visualization: `evset.plot()` and `tp.plot()`.\n", - "\n", - "The `evset.plot()` function is shorter to write and is used for displaying a single `EventSet`, while the `tp.plot()` function is used for displaying multiple `EventSets` together. This function is particularly useful when `EventSets` are indexed (see [Index, horizontal and vertical operators](#indexes-horizontal-and-vertical-operators)) or have different samplings (see [Sampling](#sampling)).\n", - "\n", - "Here's an example of using the `evset.plot()` function:" + "The `tp.glue()` operator can be used to concatenate different features into a single `EventSetNode`, but it will fail if two features with the same name are provided. The following pattern is commonly used in Temporian programs." ] }, { "cell_type": "code", "execution_count": null, - "id": "cbf42f42", + "id": "f7d85e33", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.686202Z", - "iopub.status.busy": "2023-07-24T15:00:57.685957Z", - "iopub.status.idle": "2023-07-24T15:00:57.884670Z", - "shell.execute_reply": "2023-07-24T15:00:57.884262Z" + "iopub.execute_input": "2023-07-24T15:01:00.063510Z", + "iopub.status.busy": "2023-07-24T15:01:00.063393Z", + "iopub.status.idle": "2023-07-24T15:01:00.067035Z", + "shell.execute_reply": "2023-07-24T15:01:00.066516Z" } }, "outputs": [], "source": [ - "evset = tp.event_set(\n", - "\ttimestamps=[1, 2, 3, 4, 5],\n", - "\tfeatures={\n", - " \"feature_1\": [0.5, 0.6, 0.4, 0.4, 0.9],\n", - " \"feature_2\": [\"red\", \"blue\", \"red\", \"blue\", \"green\"]\n", - " }\n", - ")\n", - "evset.plot()" + "result = tp.glue(\n", + " node.simple_moving_average(tp.duration.days(7)).prefix(\"sma_7.\"),\n", + " node.simple_moving_average(tp.duration.days(14)).prefix(\"sma_14.\"),\n", + ")" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "39dc9a92", + "id": "749df3cc", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ "\n", - "By default, the plotting style is selected automatically based on the data.\n", - "\n", - "For example, uniformly sampled numerical features (i.e., time series) are plotted with a continuous line, while non-uniformly sampled values are plotted with markers. Those and other behaviors can be controlled via the function's arguments.\n", + "## Casting\n", "\n", - "Here's an example of using the `evset.plot()` function with options:" + "Temporian is strict on feature data types (also called dtype). This means that often, you cannot perform operations between features of different types. For example, you cannot subtract a `tp.float32` and a `tp.float64`. Instead, you must manually cast the features to the same type before performing the operation." ] }, { "cell_type": "code", "execution_count": null, - "id": "82552e04", + "id": "e1276be1", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.887017Z", - "iopub.status.busy": "2023-07-24T15:00:57.886848Z", - "iopub.status.idle": "2023-07-24T15:00:58.113203Z", - "shell.execute_reply": "2023-07-24T15:00:58.112793Z" + "iopub.execute_input": "2023-07-24T15:01:00.069531Z", + "iopub.status.busy": "2023-07-24T15:01:00.069387Z", + "iopub.status.idle": "2023-07-24T15:01:00.072411Z", + "shell.execute_reply": "2023-07-24T15:01:00.071968Z" } }, "outputs": [], "source": [ - "figure = evset.plot(\n", - " style=\"marker\",\n", - " width_px=400,\n", - " min_time=2,\n", - " max_time=10,\n", - " return_fig=True,\n", - ")" + "node = tp.input_node(features=[(\"f1\", tp.float32), (\"f2\", tp.float64)])\n", + "added = node[\"f1\"].cast(tp.float64) + node[\"f2\"]" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "37bd0475", + "id": "d45a37bb", "metadata": { - "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ "\n", - "The plots are static images by default. However, interactive plotting can be very powerful. To enable interactive plotting, use `interactive=True`. Note that interactive plotting requires the `bokeh` Python library to be installed." + "Casting is especially useful to reduce memory usage. For example, if a feature only contains values between 0 and 10000, using `tp.int32` instead of `tp.int64` will halve memory usage. These optimizations are critical when working with large datasets.\n", + "\n", + "Casting can also be a necessary step before calling operators that only accept certain input data types.\n", + "\n", + "Note that in Python, the values `1.0` and `1` are respectively `float64` and `int64`.\n", + "\n", + "Temporian supports data type casting through the `EventSet.cast()` operator. Destination data types can be specified in three different ways:\n", + "\n", + "1. Single data type: converts all input features to the same destination data type.\n", + "\n", + " " ] }, { "cell_type": "code", "execution_count": null, - "id": "37feddd0", + "id": "3e3ee4c4", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:00:58.115589Z", - "iopub.status.busy": "2023-07-24T15:00:58.115426Z", - "iopub.status.idle": "2023-07-24T15:01:00.005021Z", - "shell.execute_reply": "2023-07-24T15:01:00.004601Z" + "iopub.execute_input": "2023-07-24T15:01:00.074589Z", + "iopub.status.busy": "2023-07-24T15:01:00.074466Z", + "iopub.status.idle": "2023-07-24T15:01:00.077932Z", + "shell.execute_reply": "2023-07-24T15:01:00.077413Z" + }, + "lines_to_next_cell": 0 + }, + "outputs": [], + "source": [ + "node.features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6c2444ba", + "metadata": { + "execution": { + "iopub.execute_input": "2023-07-24T15:01:00.080190Z", + "iopub.status.busy": "2023-07-24T15:01:00.079991Z", + "iopub.status.idle": "2023-07-24T15:01:00.082867Z", + "shell.execute_reply": "2023-07-24T15:01:00.082457Z" } }, "outputs": [], "source": [ - "!pip install bokeh -q\n", - "\n", - "evset.plot(interactive=True)" + "print(node.cast(tp.str_).features)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "cf4d0c27", + "id": "59c449d9", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ "\n", - "## Feature naming\n", - "\n", - "Each feature is identified by a name, and the list of features is available through the `features` property of an `EventSetNode`." + "2. Feature name to data type mapping: converts each feature (specified by name) to a specific data type." ] }, { "cell_type": "code", "execution_count": null, - "id": "38ff0e20", + "id": "05ee00a7", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.007393Z", - "iopub.status.busy": "2023-07-24T15:01:00.007171Z", - "iopub.status.idle": "2023-07-24T15:01:00.010463Z", - "shell.execute_reply": "2023-07-24T15:01:00.010135Z" + "iopub.execute_input": "2023-07-24T15:01:00.085010Z", + "iopub.status.busy": "2023-07-24T15:01:00.084841Z", + "iopub.status.idle": "2023-07-24T15:01:00.087364Z", + "shell.execute_reply": "2023-07-24T15:01:00.087026Z" } }, "outputs": [], "source": [ - "events = tp.event_set(\n", - "\ttimestamps=[1,2,3,4,5],\n", - "\tfeatures={\n", - "\t \"feature_1\": [0.5, 0.6, 0.4, 0.4, 0.9],\n", - "\t \"feature_2\": [1.0, 2.0, 3.0, 2.0, 1.0]}\n", - " )\n", - "node = events.node()\n", - "print(node.features)" + "print(node.cast({\"f1\": tp.str_, \"f2\": tp.int64}).features)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "2cd29020", + "id": "3f85a531", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ "\n", - "Most operators do not change the input feature's names." + "3. Data type to data type mapping: converts all features of a specific data type to another data type." ] }, { "cell_type": "code", "execution_count": null, - "id": "838449d4", + "id": "6deaff88", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.012599Z", - "iopub.status.busy": "2023-07-24T15:01:00.012475Z", - "iopub.status.idle": "2023-07-24T15:01:00.015736Z", - "shell.execute_reply": "2023-07-24T15:01:00.015395Z" + "iopub.execute_input": "2023-07-24T15:01:00.089508Z", + "iopub.status.busy": "2023-07-24T15:01:00.089353Z", + "iopub.status.idle": "2023-07-24T15:01:00.091835Z", + "shell.execute_reply": "2023-07-24T15:01:00.091498Z" } }, "outputs": [], "source": [ - "node.moving_sum(window_length=10).features" + "print(node.cast({tp.float32: tp.str_, tp.float64: tp.int64}).features)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "b70cc298", + "id": "915df6c5", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ "\n", - "Some operators combine two input features with different names, in which case the output name is also combined." + "Keep in mind that casting may fail when the graph is evaluated. For instance, attempting to cast `\"word\"` to `tp.float64` will result in an error. These errors cannot be caught prior to graph evaluation.\n", + "\n", + "## Arithmetic operators\n", + "\n", + "Arithmetic operators can be used between the features of an `EventSetNode`, to perform element-wise calculations.\n", + "\n", + "Common mathematical and bit operations are supported, such as addition (`+`), subtraction (`-`), product (`*`), division (`/`), floor division (`//`), modulo (`%`), comparisons (`>, >=, <, <=`), and bitwise operators (`&, |, ~`).\n", + "\n", + "These operators are applied index-wise and timestamp-wise, between features in the same position." ] }, { "cell_type": "code", "execution_count": null, - "id": "b10fdec3", + "id": "2c673b17", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.017898Z", - "iopub.status.busy": "2023-07-24T15:01:00.017753Z", - "iopub.status.idle": "2023-07-24T15:01:00.021061Z", - "shell.execute_reply": "2023-07-24T15:01:00.020666Z" - } + "iopub.execute_input": "2023-07-24T15:01:00.094177Z", + "iopub.status.busy": "2023-07-24T15:01:00.094010Z", + "iopub.status.idle": "2023-07-24T15:01:00.098574Z", + "shell.execute_reply": "2023-07-24T15:01:00.098140Z" + }, + "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "result = node[\"feature_1\"] * node[\"feature_2\"]\n", - "result.features" + "evset = tp.event_set(\n", + " timestamps=[1, 10],\n", + " features={\n", + " \"f1\": [0, 1],\n", + " \"f2\": [10.0, 20.0],\n", + " \"f3\": [100, 100],\n", + " \"f4\": [1000.0, 1000.0],\n", + " },\n", + ")\n", + "node = evset.node()\n", + "\n", + "node_added = node[[\"f1\", \"f2\"]] + node[[\"f3\", \"f4\"]]\n", + "\n", + "evset_added = node_added.run(evset)\n", + "print(evset_added)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "87731369", + "id": "03bd54cc", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "\n", - "The calendar operators don't depend on input features but on the timestamps, so the output feature name doesn't\n", - "relate to the input feature names." + "Note that features of type `int64` and `float64` are not mixed above, because otherwise the operation would fail without an explicit type cast." ] }, { - "cell_type": "code", - "execution_count": null, - "id": "4182d3f8", + "attachments": {}, + "cell_type": "markdown", + "id": "1675fa25", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.023467Z", - "iopub.status.busy": "2023-07-24T15:01:00.023325Z", - "iopub.status.idle": "2023-07-24T15:01:00.026531Z", - "shell.execute_reply": "2023-07-24T15:01:00.026132Z" - } + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 }, - "outputs": [], "source": [ - "date_events = tp.event_set(\n", - "\ttimestamps=[\"2020-02-15\", \"2020-06-20\"],\n", - "\tfeatures={\"some_feature\": [10, 20]}\n", - " )\n", - "date_node = date_events.node()\n", - "print(date_node.calendar_month().features)" + "```python\n", + ">>> node[\"f1\"] + node[\"f2\"] # Attempt to mix dtypes.\n", + "Traceback (most recent call last):\n", + " ...\n", + "ValueError: corresponding features should have the same dtype. ...\n", + "```" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "61127d7f", + "id": "7b50cf71", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ + "Refer to the [Casting](#casting) section for more on this.\n", "\n", - "You can modify feature names using the `EventSet.rename()` and `EventSet.prefix()` operators. `EventSet.rename()` changes the name of features, while `EventSet.prefix()` adds a prefix in front of existing feature names. Note that they do not modify the content of the input `EventSet`, but return a new one with the modified feature names." + "All the operators have an equivalent functional form. The example above using `+`, could be rewritten with `tp.add()`." ] }, { "cell_type": "code", "execution_count": null, - "id": "bc36bc1f", + "id": "3b350c4a", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.029017Z", - "iopub.status.busy": "2023-07-24T15:01:00.028848Z", - "iopub.status.idle": "2023-07-24T15:01:00.031960Z", - "shell.execute_reply": "2023-07-24T15:01:00.031429Z" + "iopub.execute_input": "2023-07-24T15:01:00.101025Z", + "iopub.status.busy": "2023-07-24T15:01:00.100848Z", + "iopub.status.idle": "2023-07-24T15:01:00.103444Z", + "shell.execute_reply": "2023-07-24T15:01:00.103080Z" } }, "outputs": [], "source": [ - "# Rename a single feature.\n", - "renamed_f1 = node[\"feature_1\"].rename(\"renamed_1\")\n", - "print(renamed_f1.features)" + "# Equivalent.\n", + "node_added = tp.add(node[[\"f1\", \"f2\"]], node[[\"f3\", \"f4\"]])" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "a44f6e7a", + "attachments": {}, + "cell_type": "markdown", + "id": "d65bd8de", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.042574Z", - "iopub.status.busy": "2023-07-24T15:01:00.042259Z", - "iopub.status.idle": "2023-07-24T15:01:00.045691Z", - "shell.execute_reply": "2023-07-24T15:01:00.044933Z" - } + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 }, - "outputs": [], "source": [ - "# Rename all features.\n", - "renamed_node = node.rename(\n", - " {\"feature_1\": \"renamed_1\", \"feature_2\": \"renamed_2\"}\n", - ")\n", - "print(renamed_node.features)" + "Other usual comparison and logic operators also work (except `==`, see below)." ] }, { "cell_type": "code", "execution_count": null, - "id": "af6103fc", + "id": "f1462eea", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.048841Z", - "iopub.status.busy": "2023-07-24T15:01:00.048430Z", - "iopub.status.idle": "2023-07-24T15:01:00.051757Z", - "shell.execute_reply": "2023-07-24T15:01:00.051319Z" - } - }, - "outputs": [], - "source": [ - "# Prefix a single feature.\n", - "prefixed_f1 = node[\"feature_1\"].prefix(\"prefixed.\")\n", - "print(prefixed_f1.features)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3126cbed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.053697Z", - "iopub.status.busy": "2023-07-24T15:01:00.053582Z", - "iopub.status.idle": "2023-07-24T15:01:00.056089Z", - "shell.execute_reply": "2023-07-24T15:01:00.055703Z" + "iopub.execute_input": "2023-07-24T15:01:00.105489Z", + "iopub.status.busy": "2023-07-24T15:01:00.105344Z", + "iopub.status.idle": "2023-07-24T15:01:00.108344Z", + "shell.execute_reply": "2023-07-24T15:01:00.107952Z" } }, "outputs": [], "source": [ - "# Prefix all features.\n", - "prefixed_node = node.prefix(\"prefixed.\")\n", - "print(prefixed_node.features)" + "is_greater = node[[\"f1\", \"f2\"]] > node[[\"f3\", \"f4\"]]\n", + "is_less_or_equal = node[[\"f1\", \"f2\"]] <= node[[\"f3\", \"f4\"]]\n", + "is_wrong = is_greater & is_less_or_equal" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "085e79c3", + "id": "9f01eee7", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "\n", - "It is recommended to use `EventSet.rename()` and `EventSet.prefix()` to organize your data, and avoid duplicated feature names." + "**Warning:** The Python equality operator (`==`) does not compute element-wise equality between features. Use the `evset.equal()` operator instead." ] }, { "cell_type": "code", "execution_count": null, - "id": "852abbdd", + "id": "c610a0d0", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.058375Z", - "iopub.status.busy": "2023-07-24T15:01:00.058188Z", - "iopub.status.idle": "2023-07-24T15:01:00.061323Z", - "shell.execute_reply": "2023-07-24T15:01:00.060844Z" + "iopub.execute_input": "2023-07-24T15:01:00.110338Z", + "iopub.status.busy": "2023-07-24T15:01:00.110212Z", + "iopub.status.idle": "2023-07-24T15:01:00.113649Z", + "shell.execute_reply": "2023-07-24T15:01:00.113316Z" } }, "outputs": [], "source": [ - "sma_7_node = node.simple_moving_average(tp.duration.days(7)).prefix(\"sma_7.\")\n", - "sma_14_node = node.simple_moving_average(tp.duration.days(14)).prefix(\"sma_14.\")" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "ac25eb4a", - "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 - }, - "source": [ - "\n", - "The `tp.glue()` operator can be used to concatenate different features into a single `EventSetNode`, but it will fail if two features with the same name are provided. The following pattern is commonly used in Temporian programs." + "# Works element-wise as expected\n", + "node[\"f1\"].equal(node[\"f3\"])" ] }, { "cell_type": "code", "execution_count": null, - "id": "f7d85e33", + "id": "abda404b", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.063510Z", - "iopub.status.busy": "2023-07-24T15:01:00.063393Z", - "iopub.status.idle": "2023-07-24T15:01:00.067035Z", - "shell.execute_reply": "2023-07-24T15:01:00.066516Z" + "iopub.execute_input": "2023-07-24T15:01:00.115541Z", + "iopub.status.busy": "2023-07-24T15:01:00.115402Z", + "iopub.status.idle": "2023-07-24T15:01:00.118082Z", + "shell.execute_reply": "2023-07-24T15:01:00.117723Z" } }, "outputs": [], "source": [ - "result = tp.glue(\n", - " node.simple_moving_average(tp.duration.days(7)).prefix(\"sma_7.\"),\n", - " node.simple_moving_average(tp.duration.days(14)).prefix(\"sma_14.\"),\n", - ")" + "# This is just a boolean\n", + "(node[\"f1\"] == node[\"f3\"])" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "749df3cc", + "id": "0fa4a2d1", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "\n", - "## Casting\n", - "\n", - "Temporian is strict on feature data types (also called dtype). This means that often, you cannot perform operations between features of different types. For example, you cannot subtract a `tp.float32` and a `tp.float64`. Instead, you must manually cast the features to the same type before performing the operation." + "All these operators act feature-wise, i.e. they perform index-feature-wise operations (for each feature in each index key). This implies that the input `EventSets` must have the same number of features." ] }, { - "cell_type": "code", - "execution_count": null, - "id": "e1276be1", + "attachments": {}, + "cell_type": "markdown", + "id": "0352b9de", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.069531Z", - "iopub.status.busy": "2023-07-24T15:01:00.069387Z", - "iopub.status.idle": "2023-07-24T15:01:00.072411Z", - "shell.execute_reply": "2023-07-24T15:01:00.071968Z" - } + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 }, - "outputs": [], "source": [ - "node = tp.input_node(features=[(\"f1\", tp.float32), (\"f2\", tp.float64)])\n", - "added = node[\"f1\"].cast(tp.float64) + node[\"f2\"]" + "```python\n", + "node[[\"f1\", \"f2\"]] + node[\"f3\"]\n", + "Traceback (most recent call last):\n", + " ...\n", + "ValueError: The left and right arguments should have the same number of features. ...\n", + "```" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "d45a37bb", + "id": "8dd1d7f4", "metadata": { + "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ "\n", - "Casting is especially useful to reduce memory usage. For example, if a feature only contains values between 0 and 10000, using `tp.int32` instead of `tp.int64` will halve memory usage. These optimizations are critical when working with large datasets.\n", - "\n", - "Casting can also be a necessary step before calling operators that only accept certain input data types.\n", - "\n", - "Note that in Python, the values `1.0` and `1` are respectively `float64` and `int64`.\n", - "\n", - "Temporian supports data type casting through the `EventSet.cast()` operator. Destination data types can be specified in three different ways:\n", - "\n", - "1. Single data type: converts all input features to the same destination data type.\n", - "\n", - " " + "The input `EventSets` must also have the same sampling and index." ] }, { - "cell_type": "code", - "execution_count": null, - "id": "3e3ee4c4", + "attachments": {}, + "cell_type": "markdown", + "id": "00990d1e", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.074589Z", - "iopub.status.busy": "2023-07-24T15:01:00.074466Z", - "iopub.status.idle": "2023-07-24T15:01:00.077932Z", - "shell.execute_reply": "2023-07-24T15:01:00.077413Z" - }, + "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, - "outputs": [], - "source": [ - "node.features" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c2444ba", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.080190Z", - "iopub.status.busy": "2023-07-24T15:01:00.079991Z", - "iopub.status.idle": "2023-07-24T15:01:00.082867Z", - "shell.execute_reply": "2023-07-24T15:01:00.082457Z" - } - }, - "outputs": [], "source": [ - "print(node.cast(tp.str_).features)" + "```python\n", + "sampling_1 = tp.event_set(\n", + " timestamps=[0, 1],\n", + " features={\"f1\": [1, 2]},\n", + ")\n", + "sampling_2 = tp.event_set(\n", + " timestamps=[1, 2],\n", + " features={\"f1\": [3, 4]},\n", + ")\n", + "sampling_1.node() + sampling_2.node()\n", + "Traceback (most recent call last):\n", + " ...\n", + "ValueError: Arguments should have the same sampling. ...\n", + "```" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "59c449d9", + "id": "217fa8f5", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ + "If you want to apply arithmetic operators on `EventSets` with different samplings, take a look at\n", + "[Sampling](#sampling) section.\n", "\n", - "2. Feature name to data type mapping: converts each feature (specified by name) to a specific data type." + "If you want to apply them on `EventSets` with different indexes, check the\n", + "[Vertical operators](#indexes-horizontal-and-vertical-operators) section.\n", + "\n", + "Operations involving scalars are applied index-feature-element-wise." ] }, { "cell_type": "code", "execution_count": null, - "id": "05ee00a7", + "id": "58fe7d8e", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.085010Z", - "iopub.status.busy": "2023-07-24T15:01:00.084841Z", - "iopub.status.idle": "2023-07-24T15:01:00.087364Z", - "shell.execute_reply": "2023-07-24T15:01:00.087026Z" - } + "iopub.execute_input": "2023-07-24T15:01:00.120397Z", + "iopub.status.busy": "2023-07-24T15:01:00.120245Z", + "iopub.status.idle": "2023-07-24T15:01:00.123150Z", + "shell.execute_reply": "2023-07-24T15:01:00.122799Z" + }, + "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "print(node.cast({\"f1\": tp.str_, \"f2\": tp.int64}).features)" + "node_scalar = node * 10\n", + "print(node_scalar.run(evset))" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "3f85a531", + "id": "a229408c", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ + "## Sampling\n", "\n", - "3. Data type to data type mapping: converts all features of a specific data type to another data type." + "Arithmetic operators, such as `tp.add()`, require their input arguments to have the same timestamps and [Index](#indexes-horizontal-and-vertical-operators). The unique combination of timestamps and indexes is called a _sampling_.\n", + "\n", + "\n", + "\n", + "For example, if `EventSetNodes` `a` and `b` have different samplings, `a[\"feature_1\"] + b[\"feature_2\"]` will fail.\n", + "\n", + "To use arithmetic operators on `EventSets` with different samplings, one of the `EventSets` needs to be resampled to the sampling of the other `EventSet`. Resampling is done with the `EventSet.resample()` operator.\n", + "\n", + "The `EventSet.resample()` operator takes two `EventSets` called `input` and `sampling`, and returns the resampling of the features of `input` according to the timestamps of `sampling` according to the following rules:\n", + "\n", + "If a timestamp is present in `input` but not in `sampling`, the timestamp is dropped.\n", + "If a timestamp is present in both `input` and `sampling`, the timestamp is kept.\n", + "If a timestamp is present in `sampling` but not in `input`, a new timestamp is created using the feature values from the _closest anterior_ (not the closest, as that could induce future leakage) timestamp of `input`. This rule is especially useful for events that represent measurements (see [Events and `EventSets`](#events-and-eventsets)).\n", + "\n", + "**Note:** Features in `sampling` are ignored. This also happens in some other operators that take a `sampling` argument of type `EventSetNode` - it indicates that only the sampling (a.k.a. the indexes and timestamps) of that `EventSetNode` are being used by that operator.\n", + "\n", + "Given this example:" ] }, { "cell_type": "code", "execution_count": null, - "id": "6deaff88", + "id": "087b6fd6", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.089508Z", - "iopub.status.busy": "2023-07-24T15:01:00.089353Z", - "iopub.status.idle": "2023-07-24T15:01:00.091835Z", - "shell.execute_reply": "2023-07-24T15:01:00.091498Z" - } + "iopub.execute_input": "2023-07-24T15:01:00.125315Z", + "iopub.status.busy": "2023-07-24T15:01:00.125163Z", + "iopub.status.idle": "2023-07-24T15:01:00.129661Z", + "shell.execute_reply": "2023-07-24T15:01:00.129338Z" + }, + "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "print(node.cast({tp.float32: tp.str_, tp.float64: tp.int64}).features)" + "evset = tp.event_set(\n", + " timestamps=[10, 20, 30],\n", + " features={\n", + " \"x\": [1.0, 2.0, 3.0],\n", + " },\n", + ")\n", + "node = evset.node()\n", + "sampling_evset = tp.event_set(\n", + " timestamps=[0, 9, 10, 11, 19, 20, 21],\n", + ")\n", + "sampling_node = sampling_evset.node()\n", + "resampled = node.resample(sampling=sampling_node)\n", + "resampled.run({node: evset, sampling_node: sampling_evset})" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "915df6c5", + "id": "11496f59", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ "\n", - "Keep in mind that casting may fail when the graph is evaluated. For instance, attempting to cast `\"word\"` to `tp.float64` will result in an error. These errors cannot be caught prior to graph evaluation.\n", + "The following would be the matching between the timestamps of `sampling` and `input`:\n", "\n", - "## Arithmetic operators\n", + "| `sampling` timestamp | 0 | 9 | 10 | 11 | 19 | 20 | 21 |\n", + "| ---------------------------- | --- | --- | --- | --- | --- | --- | --- |\n", + "| matching `input` timestamp | - | - | 10 | 10 | 10 | 20 | 20 |\n", + "| matching `\"x\"` feature value | NaN | NaN | 1 | 1 | 1 | 2 | 2 |\n", "\n", - "Arithmetic operators can be used between the features of an `EventSetNode`, to perform element-wise calculations.\n", + "If `sampling` contains a timestamp anterior to any timestamp in the `input` (like 0 and 9 in the example above), the feature of the sampled event will be missing. The representation of a missing value depends on its dtype:\n", "\n", - "Common mathematical and bit operations are supported, such as addition (`+`), subtraction (`-`), product (`*`), division (`/`), floor division (`//`), modulo (`%`), comparisons (`>, >=, <, <=`), and bitwise operators (`&, |, ~`).\n", + "float: `NaN`\n", + "integer: `0`\n", + "string: `\"\"`\n", "\n", - "These operators are applied index-wise and timestamp-wise, between features in the same position." + "Back to the example of the `tp.add()` operator, `a` and `b` with different sampling can be added as follows:" ] }, { "cell_type": "code", "execution_count": null, - "id": "2c673b17", + "id": "df3bbc7d", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.094177Z", - "iopub.status.busy": "2023-07-24T15:01:00.094010Z", - "iopub.status.idle": "2023-07-24T15:01:00.098574Z", - "shell.execute_reply": "2023-07-24T15:01:00.098140Z" + "iopub.execute_input": "2023-07-24T15:01:00.131774Z", + "iopub.status.busy": "2023-07-24T15:01:00.131623Z", + "iopub.status.idle": "2023-07-24T15:01:00.136202Z", + "shell.execute_reply": "2023-07-24T15:01:00.135874Z" }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "evset = tp.event_set(\n", - " timestamps=[1, 10],\n", - " features={\n", - " \"f1\": [0, 1],\n", - " \"f2\": [10.0, 20.0],\n", - " \"f3\": [100, 100],\n", - " \"f4\": [1000.0, 1000.0],\n", - " },\n", + "sampling_a = tp.event_set(\n", + " timestamps=[0, 1, 2],\n", + " features={\"f1\": [10, 20, 30]},\n", ")\n", - "node = evset.node()\n", - "\n", - "node_added = node[[\"f1\", \"f2\"]] + node[[\"f3\", \"f4\"]]\n", - "\n", - "evset_added = node_added.run(evset)\n", - "print(evset_added)" - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "03bd54cc", - "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 - }, - "source": [ - "Note that features of type `int64` and `float64` are not mixed above, because otherwise the operation would fail without an explicit type cast." + "sampling_b = tp.event_set(\n", + " timestamps=[1, 2, 3],\n", + " features={\"f1\": [5, 4, 3]},\n", + ")\n", + "a = sampling_a.node()\n", + "b = sampling_b.node()\n", + "result = a + b.resample(a)\n", + "result.run({a: sampling_a, b: sampling_b})" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "1675fa25", + "id": "56313ff2", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ + "\n", + "`EventSet.resample()` is critical to combine events from different, non-synchronized sources. For example, consider a system with two sensors, a thermometer for temperature and a manometer for pressure. The temperature sensor produces measurements every 1 to 10 minutes, while the pressure sensor returns measurements every second. Additionally assume that both sensors are not synchronized. Finally, assume that you need to combine the temperature and pressure measurements with the equation `temperature / pressure`.\n", + "\n", + "\n", + "\n", + "Since the temperature and pressure `EventSets` have different sampling, you will need to resample one of them. The pressure sensor has higher resolution. Therefore, resampling the temperature to the pressure yields higher resolution than resampling the pressure to the temperature.\n", + "\n", "```python\n", - ">>> node[\"f1\"] + node[\"f2\"] # Attempt to mix dtypes.\n", - "Traceback (most recent call last):\n", - " ...\n", - "ValueError: corresponding features should have the same dtype. ...\n", + "r = termometer[\"temperature\"].resample(manometer) / manometer[\"pressure\"]\n", "```" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "7b50cf71", + "id": "ae131e37", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "Refer to the [Casting](#casting) section for more on this.\n", "\n", - "All the operators have an equivalent functional form. The example above using `+`, could be rewritten with `tp.add()`." + "When handling non-uniform timestamps it is also common to have a common resampling source.\n", + "\n", + "```python\n", + "sampling_source = # Uniform timestamps every 10 seconds.\n", + "r = termometer[\"temperature\"].resample(sampling_source) / manometer[\"pressure\"].resample(sampling_source)\n", + "```\n", + "\n", + "Moving window operators, such as the `EventSet.simple_moving_average()` or `EventSet.moving_count()` operators, have an optional `sampling` argument. For example, the signature of the simple moving average operator is `EventSet.simple_moving_average(window_length: Duration, sampling: Optional[EventSet] = None)`. If `sampling` is not set, the result will maintain the sampling of the `input` argument. If `sampling` is set, the moving window will be sampled at each timestamp of `sampling` instead, and the result will have those new ones.\n", + "\n", + "```python\n", + "b = tp.simple_moving_average(input=a, window_length=10)\n", + "c = tp.simple_moving_average(input=a, window_length=10, sampling=d)\n", + "```\n", + "\n", + "Note that if planning to resample the result of a moving window operator, passing the `sampling` argument is both more efficient and more accurate than calling `.resample()` on the result.\n", + "\n", + "## Indexes, horizontal and vertical operators\n", + "\n", + "All operators presented so far work on a sequence of related events. For instance, the simple moving average operator computes the average of events within a specific time window. These types of operators are called _horizontal operators_.\n", + "\n", + "It is sometimes desirable for events in an `EventSet` not to interact with each other. For example, assume a dataset containing the sum of daily sales of a set of products. The objective is to compute the sum of weekly sales of each product independently. In this scenario, the weekly moving sum should be applied individually to each product. If not, you would compute the weekly sales of all the products together.\n", + "\n", + "To compute the weekly sales of individual products, you can define the `product` feature as the _index_." ] }, { "cell_type": "code", "execution_count": null, - "id": "3b350c4a", + "id": "54a71cae", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.101025Z", - "iopub.status.busy": "2023-07-24T15:01:00.100848Z", - "iopub.status.idle": "2023-07-24T15:01:00.103444Z", - "shell.execute_reply": "2023-07-24T15:01:00.103080Z" - } + "iopub.execute_input": "2023-07-24T15:01:00.138254Z", + "iopub.status.busy": "2023-07-24T15:01:00.138101Z", + "iopub.status.idle": "2023-07-24T15:01:00.141435Z", + "shell.execute_reply": "2023-07-24T15:01:00.141074Z" + }, + "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "# Equivalent.\n", - "node_added = tp.add(node[[\"f1\", \"f2\"]], node[[\"f3\", \"f4\"]])" + "daily_sales = tp.event_set(\n", + "\ttimestamps=[\"2020-01-01\", \"2020-01-01\", \"2020-01-02\", \"2020-01-02\"],\n", + "\tfeatures={\n", + " \"product\": [1, 2, 1, 2],\n", + " \"sale\": [100.0, 300.0, 90.0, 400.0],\n", + " },\n", + " indexes=[\"product\"]\n", + ")\n", + "print(daily_sales)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "d65bd8de", + "id": "40dacbb4", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "Other usual comparison and logic operators also work (except `==`, see below)." + "\n", + "The moving sum operator will then be applied independently to the events corresponding to each product." ] }, { "cell_type": "code", "execution_count": null, - "id": "f1462eea", + "id": "2c87a680", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.105489Z", - "iopub.status.busy": "2023-07-24T15:01:00.105344Z", - "iopub.status.idle": "2023-07-24T15:01:00.108344Z", - "shell.execute_reply": "2023-07-24T15:01:00.107952Z" - } + "iopub.execute_input": "2023-07-24T15:01:00.143537Z", + "iopub.status.busy": "2023-07-24T15:01:00.143412Z", + "iopub.status.idle": "2023-07-24T15:01:00.147227Z", + "shell.execute_reply": "2023-07-24T15:01:00.146886Z" + }, + "lines_to_next_cell": 2 }, "outputs": [], "source": [ - "is_greater = node[[\"f1\", \"f2\"]] > node[[\"f3\", \"f4\"]]\n", - "is_less_or_equal = node[[\"f1\", \"f2\"]] <= node[[\"f3\", \"f4\"]]\n", - "is_wrong = is_greater & is_less_or_equal" + "a = daily_sales.node()\n", + "\n", + "# Compute the moving sum of each index group (a.k.a. each product) individually.\n", + "b = a.moving_sum(window_length=tp.duration.weeks(1))\n", + "\n", + "b.run({a: daily_sales})" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "9f01eee7", + "id": "e096897d", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "**Warning:** The Python equality operator (`==`) does not compute element-wise equality between features. Use the `evset.equal()` operator instead." + "\n", + "Horizontal operators can be understood as operators that are applied independently on each index.\n", + "\n", + "Operators that modify an `EventSetNode`'s indexes are called _vertical operators_. The most important vertical operators are:\n", + "\n", + "- `EventSet.add_index()`: Add features to the index.\n", + "- `EventSet.drop_index()`: Remove features from the index, optionally keeping them as features.\n", + "- `EventSet.set_index()`: Changes the index.\n", + "- `EventSet.propagate()`: Expand indexes based on another `EventSet`’s indexes.\n", + "\n", + "By default, `EventSets` are _flat_, which means they have no index, and therefore all events are in a single global group.\n", + "\n", + "Also, keep in mind that only string and integer features can be used as indexes.\n", + "\n", + "`EventSets` can have multiple features as index. In the next example, assume our daily sale aggregates are also annotated with `store` data." ] }, { "cell_type": "code", "execution_count": null, - "id": "c610a0d0", + "id": "8badbebf", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.110338Z", - "iopub.status.busy": "2023-07-24T15:01:00.110212Z", - "iopub.status.idle": "2023-07-24T15:01:00.113649Z", - "shell.execute_reply": "2023-07-24T15:01:00.113316Z" + "iopub.execute_input": "2023-07-24T15:01:00.149178Z", + "iopub.status.busy": "2023-07-24T15:01:00.149039Z", + "iopub.status.idle": "2023-07-24T15:01:00.152148Z", + "shell.execute_reply": "2023-07-24T15:01:00.151798Z" } }, "outputs": [], "source": [ - "# Works element-wise as expected\n", - "node[\"f1\"].equal(node[\"f3\"])" + "daily_sales = tp.event_set(\n", + "\ttimestamps=[\"2020-01-01\", \"2020-01-01\", \"2020-01-02\", \"2020-01-02\"],\n", + "\tfeatures={\n", + " \"store\": [1, 1, 1, 2],\n", + " \"product\": [1, 2, 1, 2],\n", + " \"sale\": [100.0, 200.0, 110.0, 300.0],\n", + " },\n", + ")\n", + "print(daily_sales)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "69b3c03c", + "metadata": { + "cell_marker": "\"\"\"", + "lines_to_next_cell": 0 + }, + "source": [ + "\n", + "Since we haven't defined the `indexes` yet, `store` and `product` are just regular features above.\n", + "Let's add the `(product, store)` pair as the index." ] }, { "cell_type": "code", "execution_count": null, - "id": "abda404b", + "id": "479096c3", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.115541Z", - "iopub.status.busy": "2023-07-24T15:01:00.115402Z", - "iopub.status.idle": "2023-07-24T15:01:00.118082Z", - "shell.execute_reply": "2023-07-24T15:01:00.117723Z" - } + "iopub.execute_input": "2023-07-24T15:01:00.154125Z", + "iopub.status.busy": "2023-07-24T15:01:00.154007Z", + "iopub.status.idle": "2023-07-24T15:01:00.157566Z", + "shell.execute_reply": "2023-07-24T15:01:00.157243Z" + }, + "lines_to_next_cell": 2 }, "outputs": [], "source": [ - "# This is just a boolean\n", - "(node[\"f1\"] == node[\"f3\"])" + "a = daily_sales.node()\n", + "b = a.add_index([\"product\", \"store\"])\n", + "b.run({a: daily_sales})" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "0fa4a2d1", + "id": "c2dc98f6", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "All these operators act feature-wise, i.e. they perform index-feature-wise operations (for each feature in each index key). This implies that the input `EventSets` must have the same number of features." + "\n", + "The `moving_sum` operator can be used to calculate the weekly sum of sales\n", + "for each `(product, store)` pair." ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "0352b9de", + "cell_type": "code", + "execution_count": null, + "id": "4df0d8cf", "metadata": { - "cell_marker": "\"\"\"", + "execution": { + "iopub.execute_input": "2023-07-24T15:01:00.159586Z", + "iopub.status.busy": "2023-07-24T15:01:00.159442Z", + "iopub.status.idle": "2023-07-24T15:01:00.163136Z", + "shell.execute_reply": "2023-07-24T15:01:00.162800Z" + }, "lines_to_next_cell": 0 }, + "outputs": [], "source": [ - "```python\n", - "node[[\"f1\", \"f2\"]] + node[\"f3\"]\n", - "Traceback (most recent call last):\n", - " ...\n", - "ValueError: The left and right arguments should have the same number of features. ...\n", - "```" + "# Weekly sales by product and store\n", + "c = b[\"sale\"].moving_sum(window_length=tp.duration.weeks(1))\n", + "c.run({a: daily_sales})" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "8dd1d7f4", + "id": "2295d4ee", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ "\n", - "The input `EventSets` must also have the same sampling and index." + "If we want the weekly sum of sales per `store`, we can just drop the `product` index." ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "00990d1e", + "cell_type": "code", + "execution_count": null, + "id": "8d9448a2", "metadata": { - "cell_marker": "\"\"\"", + "execution": { + "iopub.execute_input": "2023-07-24T15:01:00.165204Z", + "iopub.status.busy": "2023-07-24T15:01:00.165078Z", + "iopub.status.idle": "2023-07-24T15:01:00.170459Z", + "shell.execute_reply": "2023-07-24T15:01:00.169929Z" + }, "lines_to_next_cell": 0 }, + "outputs": [], "source": [ - "```python\n", - "sampling_1 = tp.event_set(\n", - " timestamps=[0, 1],\n", - " features={\"f1\": [1, 2]},\n", - ")\n", - "sampling_2 = tp.event_set(\n", - " timestamps=[1, 2],\n", - " features={\"f1\": [3, 4]},\n", - ")\n", - "sampling_1.node() + sampling_2.node()\n", - "Traceback (most recent call last):\n", - " ...\n", - "ValueError: Arguments should have the same sampling. ...\n", - "```" + "# Weekly sales by store (including all products)\n", + "d = b.drop_index(\"product\")\n", + "e = d[\"sale\"].moving_sum(window_length=tp.duration.weeks(1))\n", + "e.run({a: daily_sales})" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "217fa8f5", + "id": "f67b3f0c", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "If you want to apply arithmetic operators on `EventSets` with different samplings, take a look at\n", - "[Sampling](#sampling) section.\n", "\n", - "If you want to apply them on `EventSets` with different indexes, check the\n", - "[Vertical operators](#indexes-horizontal-and-vertical-operators) section.\n", + "Finally, let's calculate the ratio of sales of each `(product, store)` pair compared to the whole `store` sales.\n", "\n", - "Operations involving scalars are applied index-feature-element-wise." + "Since `c` (weekly sales for each product and store) and `e` (weekly sales for each store) have different indexes, we cannot use `tp.divide` (or `/`) directly - we must first `propagate` `e` to the `[\"product\", \"store\"]` index." ] }, { "cell_type": "code", "execution_count": null, - "id": "58fe7d8e", + "id": "4e12a9e0", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.120397Z", - "iopub.status.busy": "2023-07-24T15:01:00.120245Z", - "iopub.status.idle": "2023-07-24T15:01:00.123150Z", - "shell.execute_reply": "2023-07-24T15:01:00.122799Z" + "iopub.execute_input": "2023-07-24T15:01:00.172674Z", + "iopub.status.busy": "2023-07-24T15:01:00.172528Z", + "iopub.status.idle": "2023-07-24T15:01:00.177142Z", + "shell.execute_reply": "2023-07-24T15:01:00.176616Z" }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "node_scalar = node * 10\n", - "print(node_scalar.run(evset))" + "# Copy the content of e (indexed by (store)) into each (store, product).\n", + "f = c / e.propagate(sampling=c, resample=True)\n", + "\n", + "# Equivalent.\n", + "f = c / e.propagate(sampling=c).resample(sampling=c)\n", + "print(f.run({a: daily_sales}))" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "a229408c", + "id": "e73da62c", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "## Sampling\n", - "\n", - "Arithmetic operators, such as `tp.add()`, require their input arguments to have the same timestamps and [Index](#indexes-horizontal-and-vertical-operators). The unique combination of timestamps and indexes is called a _sampling_.\n", - "\n", - "\n", "\n", - "For example, if `EventSetNodes` `a` and `b` have different samplings, `a[\"feature_1\"] + b[\"feature_2\"]` will fail.\n", + "The `EventSet.propagate()` operator expands the indexes of its `input` (`e` in this case) to match the indexes of its `sampling` by copying the content of `input` into each corresponding index group of `sampling`. Note that `sampling`'s indexes must be a superset of `input`'s indexes.\n", "\n", - "To use arithmetic operators on `EventSets` with different samplings, one of the `EventSets` needs to be resampled to the sampling of the other `EventSet`. Resampling is done with the `EventSet.resample()` operator.\n", + "## Future leakage\n", "\n", - "The `EventSet.resample()` operator takes two `EventSets` called `input` and `sampling`, and returns the resampling of the features of `input` according to the timestamps of `sampling` according to the following rules:\n", + "In supervised learning, [leakage]() is the use of data not available at serving time by a machine learning model. A common example of leakage is _label leakage_, which involves the invalid use of labels in the model input features. Leakage tends to bias model evaluation by making it appear much better than it is in reality. Unfortunately, leakage is often subtle, easy to inject, and challenging to detect.\n", "\n", - "If a timestamp is present in `input` but not in `sampling`, the timestamp is dropped.\n", - "If a timestamp is present in both `input` and `sampling`, the timestamp is kept.\n", - "If a timestamp is present in `sampling` but not in `input`, a new timestamp is created using the feature values from the _closest anterior_ (not the closest, as that could induce future leakage) timestamp of `input`. This rule is especially useful for events that represent measurements (see [Events and `EventSets`](#events-and-eventsets)).\n", + "Another type of leakage is future leakage, where a model uses data before it is available. Future leakage is particularly easy to create, as all feature data is ultimately available to the model, the problem being it being accessed at the wrong time.\n", "\n", - "**Note:** Features in `sampling` are ignored. This also happens in some other operators that take a `sampling` argument of type `EventSetNode` - it indicates that only the sampling (a.k.a. the indexes and timestamps) of that `EventSetNode` are being used by that operator.\n", + "To avoid future leakage, Temporian operators are guaranteed to not cause future leakage, except for the `EventSet.leak()` operator. This means that it is impossible to inadvertently add future leakage to a Temporian program.\n", "\n", - "Given this example:" + "`EventSet.leak()` can be useful for precomputing labels or evaluating machine learning models. However, its outputs shouldn’t be used as input features." ] }, { "cell_type": "code", "execution_count": null, - "id": "087b6fd6", + "id": "c07453fa", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.125315Z", - "iopub.status.busy": "2023-07-24T15:01:00.125163Z", - "iopub.status.idle": "2023-07-24T15:01:00.129661Z", - "shell.execute_reply": "2023-07-24T15:01:00.129338Z" - }, - "lines_to_next_cell": 0 + "iopub.execute_input": "2023-07-24T15:01:00.179407Z", + "iopub.status.busy": "2023-07-24T15:01:00.179261Z", + "iopub.status.idle": "2023-07-24T15:01:00.182867Z", + "shell.execute_reply": "2023-07-24T15:01:00.182049Z" + } }, "outputs": [], "source": [ - "evset = tp.event_set(\n", - " timestamps=[10, 20, 30],\n", - " features={\n", - " \"x\": [1.0, 2.0, 3.0],\n", - " },\n", - ")\n", - "node = evset.node()\n", - "sampling_evset = tp.event_set(\n", - " timestamps=[0, 9, 10, 11, 19, 20, 21],\n", - ")\n", - "sampling_node = sampling_evset.node()\n", - "resampled = node.resample(sampling=sampling_node)\n", - "resampled.run({node: evset, sampling_node: sampling_evset})" + "a = tp.input_node(features=[(\"feature_1\", tp.float32)])\n", + "b = a.moving_count(1)\n", + "c = b.leak(1).moving_count(2)" ] }, { - "attachments": {}, "cell_type": "markdown", - "id": "11496f59", - "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 - }, + "id": "d2e37ee5", + "metadata": {}, "source": [ + "In this example, `b` does not have a future leak, but `c` does because it depends on `EventSet.leak()`.\n", "\n", - "The following would be the matching between the timestamps of `sampling` and `input`:\n", - "\n", - "| `sampling` timestamp | 0 | 9 | 10 | 11 | 19 | 20 | 21 |\n", - "| ---------------------------- | --- | --- | --- | --- | --- | --- | --- |\n", - "| matching `input` timestamp | - | - | 10 | 10 | 10 | 20 | 20 |\n", - "| matching `\"x\"` feature value | NaN | NaN | 1 | 1 | 1 | 2 | 2 |\n", - "\n", - "If `sampling` contains a timestamp anterior to any timestamp in the `input` (like 0 and 9 in the example above), the feature of the sampled event will be missing. The representation of a missing value depends on its dtype:\n", - "\n", - "float: `NaN`\n", - "integer: `0`\n", - "string: `\"\"`\n", - "\n", - "Back to the example of the `tp.add()` operator, `a` and `b` with different sampling can be added as follows:" + "To check programmatically if an `EventSetNode` depends on `leak()`, we can use the `tp.has_leak()` function." ] }, { "cell_type": "code", "execution_count": null, - "id": "df3bbc7d", + "id": "ab15c133", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.131774Z", - "iopub.status.busy": "2023-07-24T15:01:00.131623Z", - "iopub.status.idle": "2023-07-24T15:01:00.136202Z", - "shell.execute_reply": "2023-07-24T15:01:00.135874Z" - }, - "lines_to_next_cell": 0 + "iopub.execute_input": "2023-07-24T15:01:00.185286Z", + "iopub.status.busy": "2023-07-24T15:01:00.185178Z", + "iopub.status.idle": "2023-07-24T15:01:00.187923Z", + "shell.execute_reply": "2023-07-24T15:01:00.187503Z" + } }, "outputs": [], "source": [ - "sampling_a = tp.event_set(\n", - " timestamps=[0, 1, 2],\n", - " features={\"f1\": [10, 20, 30]},\n", - ")\n", - "sampling_b = tp.event_set(\n", - " timestamps=[1, 2, 3],\n", - " features={\"f1\": [5, 4, 3]},\n", - ")\n", - "a = sampling_a.node()\n", - "b = sampling_b.node()\n", - "result = a + b.resample(a)\n", - "result.run({a: sampling_a, b: sampling_b})" + "print(tp.has_leak(b))" ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "56313ff2", + "cell_type": "code", + "execution_count": null, + "id": "cd455fc1", "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 + "execution": { + "iopub.execute_input": "2023-07-24T15:01:00.190006Z", + "iopub.status.busy": "2023-07-24T15:01:00.189857Z", + "iopub.status.idle": "2023-07-24T15:01:00.192310Z", + "shell.execute_reply": "2023-07-24T15:01:00.191954Z" + } }, + "outputs": [], "source": [ - "\n", - "`EventSet.resample()` is critical to combine events from different, non-synchronized sources. For example, consider a system with two sensors, a thermometer for temperature and a manometer for pressure. The temperature sensor produces measurements every 1 to 10 minutes, while the pressure sensor returns measurements every second. Additionally assume that both sensors are not synchronized. Finally, assume that you need to combine the temperature and pressure measurements with the equation `temperature / pressure`.\n", - "\n", - "\n", - "\n", - "Since the temperature and pressure `EventSets` have different sampling, you will need to resample one of them. The pressure sensor has higher resolution. Therefore, resampling the temperature to the pressure yields higher resolution than resampling the pressure to the temperature.\n", - "\n", - "```python\n", - "r = termometer[\"temperature\"].resample(manometer) / manometer[\"pressure\"]\n", - "```" + "print(tp.has_leak(c))" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "ae131e37", + "id": "8d973547", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ + "By using `tp.has_leak()`, we can programmatically identify future leakage and modify our code accordingly.\n", "\n", - "When handling non-uniform timestamps it is also common to have a common resampling source.\n", - "\n", - "```python\n", - "sampling_source = # Uniform timestamps every 10 seconds.\n", - "r = termometer[\"temperature\"].resample(sampling_source) / manometer[\"pressure\"].resample(sampling_source)\n", - "```\n", - "\n", - "Moving window operators, such as the `EventSet.simple_moving_average()` or `EventSet.moving_count()` operators, have an optional `sampling` argument. For example, the signature of the simple moving average operator is `EventSet.simple_moving_average(window_length: Duration, sampling: Optional[EventSet] = None)`. If `sampling` is not set, the result will maintain the sampling of the `input` argument. If `sampling` is set, the moving window will be sampled at each timestamp of `sampling` instead, and the result will have those new ones.\n", - "\n", - "```python\n", - "b = tp.simple_moving_average(input=a, window_length=10)\n", - "c = tp.simple_moving_average(input=a, window_length=10, sampling=d)\n", - "```\n", - "\n", - "Note that if planning to resample the result of a moving window operator, passing the `sampling` argument is both more efficient and more accurate than calling `.resample()` on the result.\n", - "\n", - "## Indexes, horizontal and vertical operators\n", - "\n", - "All operators presented so far work on a sequence of related events. For instance, the simple moving average operator computes the average of events within a specific time window. These types of operators are called _horizontal operators_.\n", - "\n", - "It is sometimes desirable for events in an `EventSet` not to interact with each other. For example, assume a dataset containing the sum of daily sales of a set of products. The objective is to compute the sum of weekly sales of each product independently. In this scenario, the weekly moving sum should be applied individually to each product. If not, you would compute the weekly sales of all the products together.\n", + "## Accessing `EventSet` data\n", "\n", - "To compute the weekly sales of individual products, you can define the `product` feature as the _index_." + "`EventSet` data can be accessed using their `data` attribute. Temporian internally relies on NumPy, which means that the data access functions always return NumPy arrays." ] }, { "cell_type": "code", "execution_count": null, - "id": "54a71cae", + "id": "7da63117", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.138254Z", - "iopub.status.busy": "2023-07-24T15:01:00.138101Z", - "iopub.status.idle": "2023-07-24T15:01:00.141435Z", - "shell.execute_reply": "2023-07-24T15:01:00.141074Z" - }, - "lines_to_next_cell": 0 + "iopub.execute_input": "2023-07-24T15:01:00.194430Z", + "iopub.status.busy": "2023-07-24T15:01:00.194277Z", + "iopub.status.idle": "2023-07-24T15:01:00.198829Z", + "shell.execute_reply": "2023-07-24T15:01:00.198424Z" + } }, "outputs": [], "source": [ - "daily_sales = tp.event_set(\n", - "\ttimestamps=[\"2020-01-01\", \"2020-01-01\", \"2020-01-02\", \"2020-01-02\"],\n", + "evset = tp.event_set(\n", + "\ttimestamps=[1, 2, 3, 5, 6],\n", "\tfeatures={\n", - " \"product\": [1, 2, 1, 2],\n", - " \"sale\": [100.0, 300.0, 90.0, 400.0],\n", - " },\n", - " indexes=[\"product\"]\n", + " \"f1\": [0.1, 0.2, 0.3, 1.1, 1.2],\n", + " \"f2\": [\"red\", \"red\", \"red\", \"blue\", \"blue\"],\n", + "\t},\n", + "\tindexes=[\"f2\"],\n", ")\n", - "print(daily_sales)" + "\n", + "# Access the data for the index group `f2=red`.\n", + "evset.get_index_value((\"red\",))" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "40dacbb4", + "id": "eddfa88e", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ + "\n", + "\n", + "## Import and export data\n", + "\n", + "`EventSets` can be read from and saved to csv files via the `tp.from_csv()` and `tp.to_csv()` functions.\n", + "\n", + "```python\n", + "evset = tp.from_csv( # Read EventSet from a .csv file.\n", + " path=\"path/to/file.csv\",\n", + " timestamps=\"timestamp\",\n", + " indexes=[\"product_id\"],\n", + ")\n", + "\n", + "tp.to_csv(evset, path=\"path/to/file.csv\") # Save EventSet to a .csv file.\n", + "```\n", + "\n", + "Converting `EventSet` data to and from pandas DataFrames is also easily done via `tp.to_pandas()` and `tp.from_pandas()`." ] }, { "cell_type": "code", "execution_count": null, - "id": "8badbebf", + "id": "da239494", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.149178Z", - "iopub.status.busy": "2023-07-24T15:01:00.149039Z", - "iopub.status.idle": "2023-07-24T15:01:00.152148Z", - "shell.execute_reply": "2023-07-24T15:01:00.151798Z" + "iopub.execute_input": "2023-07-24T15:01:00.200932Z", + "iopub.status.busy": "2023-07-24T15:01:00.200760Z", + "iopub.status.idle": "2023-07-24T15:01:00.212641Z", + "shell.execute_reply": "2023-07-24T15:01:00.212298Z" } }, "outputs": [], "source": [ - "daily_sales = tp.event_set(\n", - "\ttimestamps=[\"2020-01-01\", \"2020-01-01\", \"2020-01-02\", \"2020-01-02\"],\n", - "\tfeatures={\n", - " \"store\": [1, 1, 1, 2],\n", - " \"product\": [1, 2, 1, 2],\n", - " \"sale\": [100.0, 200.0, 110.0, 300.0],\n", - " },\n", - ")\n", - "print(daily_sales)" + "df = pd.DataFrame({\n", + " \"timestamp\": [1, 2, 3, 5, 6],\n", + " \"f1\": [0.1, 0.2, 0.3, 1.1, 1.2],\n", + " \"f2\": [\"red\", \"red\", \"red\", \"blue\", \"blue\"],\n", + "})\n", + "\n", + "# Create EventSet from DataFrame.\n", + "evset = tp.from_pandas(df)\n", + "\n", + "# Convert EventSet to DataFrame.\n", + "df = tp.to_pandas(evset)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "69b3c03c", + "id": "64cb7ff2-945d-461d-ba4b-b5ac1e127f85", "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 + "jp-MarkdownHeadingCollapsed": true }, "source": [ + "## Eager mode vs Graph mode\n", "\n", - "Since we haven't defined the `indexes` yet, `store` and `product` are just regular features above.\n", - "Let's add the `(product, store)` pair as the index." + "Temporian has two execution modes: **eager** and **graph**. In eager mode, operators are applied immediately. This mode is useful for learning Temporian, for iterative and interactive development, and for lightweight/small data use cases where performance isn't a priority.\n", + "\n", + "In graph mode, operators are combined together into \"Temporian programs\" before being executed. Graph mode is more efficient and it consumes less memory. Temporian programs can be saved, inspected, and distributed by users.\n", + "\n", + "Migrating a Temporian program from eager to graph mode is easy and requires little work. Most of the time, adding a `@tp.compile` annotation is enough. Therefore, it is recommended to develop programs in eager mode and then to productize them in graph mode.\n", + "\n", + "Next, we see a the same program written three times: First, in eager mode, then in graph mode using `@tp.compile`, and finally in graph mode without `@tp.compile`." ] }, { "cell_type": "code", "execution_count": null, - "id": "479096c3", + "id": "d5405837-c801-40e9-8b41-4d4dc901d429", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.154125Z", - "iopub.status.busy": "2023-07-24T15:01:00.154007Z", - "iopub.status.idle": "2023-07-24T15:01:00.157566Z", - "shell.execute_reply": "2023-07-24T15:01:00.157243Z" - }, - "lines_to_next_cell": 2 + "iopub.execute_input": "2023-07-24T15:00:57.315244Z", + "iopub.status.busy": "2023-07-24T15:00:57.315083Z", + "iopub.status.idle": "2023-07-24T15:00:57.417894Z", + "shell.execute_reply": "2023-07-24T15:00:57.417481Z" + } }, "outputs": [], "source": [ - "a = daily_sales.node()\n", - "b = a.add_index([\"product\", \"store\"])\n", - "b.run({a: daily_sales})" + "# Eager mode\n", + "#\n", + "# Note: This Temporian program contains three operators: two \"simple_moving_average\" and one \"tp.substract\" operators.\n", + "result = evset.simple_moving_average(window_length=0.5) - evset.simple_moving_average(window_length=1.0)\n", + "result.plot()" ] }, { - "attachments": {}, - "cell_type": "markdown", - "id": "c2dc98f6", + "cell_type": "code", + "execution_count": null, + "id": "9c854408-0804-4435-8d44-f521ef1b379e", "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 + "execution": { + "iopub.execute_input": "2023-07-24T15:00:57.420139Z", + "iopub.status.busy": "2023-07-24T15:00:57.419972Z", + "iopub.status.idle": "2023-07-24T15:00:57.522730Z", + "shell.execute_reply": "2023-07-24T15:00:57.522286Z" + } }, + "outputs": [], "source": [ + "# Graph mode with @tp.compile\n", "\n", - "The `moving_sum` operator can be used to calculate the weekly sum of sales\n", - "for each `(product, store)` pair." + "@tp.compile\n", + "def my_function(x):\n", + " return x.simple_moving_average(window_length=0.5) - x.simple_moving_average(window_length=1.0)\n", + "\n", + "result = my_function(evset)\n", + " \n", + "result.plot()" ] }, { "cell_type": "code", "execution_count": null, - "id": "4df0d8cf", + "id": "e4aa008f-aa8e-486f-a96c-854fb5b2bd83", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.159586Z", - "iopub.status.busy": "2023-07-24T15:01:00.159442Z", - "iopub.status.idle": "2023-07-24T15:01:00.163136Z", - "shell.execute_reply": "2023-07-24T15:01:00.162800Z" - }, - "lines_to_next_cell": 0 + "iopub.execute_input": "2023-07-24T15:00:57.525125Z", + "iopub.status.busy": "2023-07-24T15:00:57.524948Z", + "iopub.status.idle": "2023-07-24T15:00:57.629039Z", + "shell.execute_reply": "2023-07-24T15:00:57.628643Z" + } }, "outputs": [], "source": [ - "# Weekly sales by product and store\n", - "c = b[\"sale\"].moving_sum(window_length=tp.duration.weeks(1))\n", - "c.run({a: daily_sales})" + "# Graph model without @tp.compile\n", + "\n", + "input_node = tp.input_node([(\"value\", tp.float64)])\n", + "# Or input_node = tp.input_node(evset.schema.features)\n", + "\n", + "result_node = input_node.simple_moving_average(window_length=0.5) - input_node.simple_moving_average(window_length=1.0)\n", + "result = tp.run(result_node, {input_node: evset}, verbose=1)\n", + " \n", + "result.plot()" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "2295d4ee", + "id": "6f961cee-c813-46b8-bf49-cecd95916b31", "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 + "jp-MarkdownHeadingCollapsed": true }, "source": [ + "## More on graph mode\n", "\n", - "If we want the weekly sum of sales per `store`, we can just drop the `product` index." + "**Remark:** While you will likely use the graph mode with `@tp.compile` , it is useful for you to understand the graph model without `@tp.compile`.\n", + "\n", + "A Temporian program is a graph of [EventSetNodes][temporian.EventSetNode] connecting operators. A graph is executed with the function `tp.run(, )`.\n", + "\n", + "\"eager\n", + "\n", + "The `` can be specified as an `EventSetNode`, a list of `EventSetNodes`, or a dictionary of names to `EventSetNodes`, and the result of `tp.run()` will be of the same type. For example, if `` is a list of three `EventSetNodes`, the result will be a list of the three corresponding `EventSets`.\n", + "\n", + "The `` can be specified as:\n", + "\n", + "- A dictionary of `EventSetNodes` to `EventSets`, or \n", + "- A dictionary of names to `EventSets`, or,\n", + "- A list of `EventSets`, or\n", + "- A single `EventSet`\n", + "\n", + "This lets Temporian know the `EventSetNodes` of the graph that each input `EventSet` corresponds to. If `` is a dictionary of names to `EventSets`, the names must match the names of `EventSetNodes` in the graph. If `` is a list or a single `EventSet`, the names of those `EventSets` must do the same. If we specify the inputs as a dictionary, we could skip passing a name to `a_evset`." ] }, { "cell_type": "code", "execution_count": null, - "id": "8d9448a2", + "id": "dd4142a0-df47-4ca3-aafe-7a105b4f2deb", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.165204Z", - "iopub.status.busy": "2023-07-24T15:01:00.165078Z", - "iopub.status.idle": "2023-07-24T15:01:00.170459Z", - "shell.execute_reply": "2023-07-24T15:01:00.169929Z" - }, - "lines_to_next_cell": 0 + "iopub.execute_input": "2023-07-24T15:00:57.631693Z", + "iopub.status.busy": "2023-07-24T15:00:57.631532Z", + "iopub.status.idle": "2023-07-24T15:00:57.636094Z", + "shell.execute_reply": "2023-07-24T15:00:57.635717Z" + } }, "outputs": [], "source": [ - "# Weekly sales by store (including all products)\n", - "d = b.drop_index(\"product\")\n", - "e = d[\"sale\"].moving_sum(window_length=tp.duration.weeks(1))\n", - "e.run({a: daily_sales})" + "input_node = tp.input_node([(\"value\", tp.float64)])\n", + "result_1_node = input_node.simple_moving_average(window_length=0.5)\n", + "result_2_node = input_node.simple_moving_average(window_length=1.0)\n", + "result_3_node = result_1_node - result_2_node\n", + "\n", + "result = tp.run([result_1_node,result_2_node, result_3_node], {input_node: evset})\n", + " \n", + "print(result)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "f67b3f0c", - "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 - }, + "id": "bca4258f-cdb2-4b2b-992d-4c2937a2868f", + "metadata": {}, "source": [ + "**Remarks:**\n", "\n", - "Finally, let's calculate the ratio of sales of each `(product, store)` pair compared to the whole `store` sales.\n", - "\n", - "Since `c` (weekly sales for each product and store) and `e` (weekly sales for each store) have different indexes, we cannot use `tp.divide` (or `/`) directly - we must first `propagate` `e` to the `[\"product\", \"store\"]` index." + "- It's important to distinguish between a `tp.EventSet`, such as `evset`, that contains data, and a `tp.EventSetNode`, like `input_node`, that connect operators together and compose the computation graph, but do not contain data.\n", + "- No computation is performed when defining the graph (i.e., when calling the operator functions). All computation is done during `tp.run()`.\n", + "- In `tp.run()`, the second argument defines a mapping between input `EventSetNodes` and `EventSets`. If all necessary input `EventSetNodes` are not fed, an error will be raised.\n", + "- In most cases you will only pass `EventSets` that correspond to the graph's input `EventSetNodes`, but Temporian also supports passing `EventSets` to intermediate `EventSetNodes` in the graph." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "5b445f9c-725b-47b1-bd8c-fc0502b99016", + "metadata": {}, + "source": [ + "The `@tp.compile` annotation takes a function inputing and outputing `tp.EventSetNode`, and automatically calls `tp.run` on the result of the function if a `tp.EventSet` is provided as input." ] }, { "cell_type": "code", "execution_count": null, - "id": "4e12a9e0", + "id": "b0b0220b-9869-454c-8f3b-afeabaca06bc", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.172674Z", - "iopub.status.busy": "2023-07-24T15:01:00.172528Z", - "iopub.status.idle": "2023-07-24T15:01:00.177142Z", - "shell.execute_reply": "2023-07-24T15:01:00.176616Z" - }, - "lines_to_next_cell": 0 + "iopub.execute_input": "2023-07-24T15:00:57.638508Z", + "iopub.status.busy": "2023-07-24T15:00:57.638232Z", + "iopub.status.idle": "2023-07-24T15:00:57.642016Z", + "shell.execute_reply": "2023-07-24T15:00:57.641623Z" + } }, "outputs": [], "source": [ - "# Copy the content of e (indexed by (store)) into each (store, product).\n", - "f = c / e.propagate(sampling=c, resample=True)\n", + "@tp.compile\n", + "def my_function(x : tp.types.EventSetOrNode) -> tp.types.EventSetOrNode:\n", + " return x.simple_moving_average(window_length=0.5)\n", "\n", - "# Equivalent.\n", - "f = c / e.propagate(sampling=c).resample(sampling=c)\n", - "print(f.run({a: daily_sales}))" + "# Feeding an EventSet\n", + "input_evset = tp.event_set(timestamps=[1, 2, 3],features={\"value\": [5., 6., 7.]})\n", + "assert isinstance(my_function(input_evset), tp.EventSet)\n", + "\n", + "# Feeding an EventSetNode\n", + "input_node = tp.input_node([(\"value\", tp.float64)])\n", + "assert isinstance(my_function(input_node), tp.EventSetNode)" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "e73da62c", - "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 - }, + "id": "282ec163-1d0e-442a-bb18-ac961010b28e", + "metadata": {}, "source": [ + "Importantly, variables in a `tp.compile` function are `EventSetNode` and not `EventSet`. Therefore, you cannot directly access the event set data.\n", "\n", - "The `EventSet.propagate()` operator expands the indexes of its `input` (`e` in this case) to match the indexes of its `sampling` by copying the content of `input` into each corresponding index group of `sampling`. Note that `sampling`'s indexes must be a superset of `input`'s indexes.\n", - "\n", - "## Future leakage\n", - "\n", - "In supervised learning, [leakage]() is the use of data not available at serving time by a machine learning model. A common example of leakage is _label leakage_, which involves the invalid use of labels in the model input features. Leakage tends to bias model evaluation by making it appear much better than it is in reality. Unfortunately, leakage is often subtle, easy to inject, and challenging to detect.\n", - "\n", - "Another type of leakage is future leakage, where a model uses data before it is available. Future leakage is particularly easy to create, as all feature data is ultimately available to the model, the problem being it being accessed at the wrong time.\n", - "\n", - "To avoid future leakage, Temporian operators are guaranteed to not cause future leakage, except for the `EventSet.leak()` operator. This means that it is impossible to inadvertently add future leakage to a Temporian program.\n", - "\n", - "`EventSet.leak()` can be useful for precomputing labels or evaluating machine learning models. However, its outputs shouldn’t be used as input features." + "In addition, the compiled function execution first generates the graph. The graph is then executed. In the next example, the compiled function generates a graph with 10 operators." ] }, { "cell_type": "code", "execution_count": null, - "id": "c07453fa", + "id": "945d8f39-18ad-44df-a4d2-9d8986e7825b", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.179407Z", - "iopub.status.busy": "2023-07-24T15:01:00.179261Z", - "iopub.status.idle": "2023-07-24T15:01:00.182867Z", - "shell.execute_reply": "2023-07-24T15:01:00.182049Z" + "iopub.execute_input": "2023-07-24T15:00:57.644118Z", + "iopub.status.busy": "2023-07-24T15:00:57.643976Z", + "iopub.status.idle": "2023-07-24T15:00:57.646561Z", + "shell.execute_reply": "2023-07-24T15:00:57.646187Z" } }, "outputs": [], "source": [ - "a = tp.input_node(features=[(\"feature_1\", tp.float32)])\n", - "b = a.moving_count(1)\n", - "c = b.leak(1).moving_count(2)" + "@tp.compile\n", + "def my_function(x : tp.EventSetNode) -> tp.EventSetNode:\n", + " for i in range(10):\n", + " x = x.simple_moving_average(window_length=i+1)\n", + " return x" ] }, { + "attachments": {}, "cell_type": "markdown", - "id": "d2e37ee5", + "id": "660e2930-38b5-4eaa-af3e-97b97b1a422c", "metadata": {}, "source": [ - "In this example, `b` does not have a future leak, but `c` does because it depends on `EventSet.leak()`.\n", - "\n", - "To check programmatically if an `EventSetNode` depends on `leak()`, we can use the `tp.has_leak()` function." + "You can create a compiled function with a `if`. However, the condition of the `if` cannot depend on the EventSet data." ] }, { "cell_type": "code", "execution_count": null, - "id": "ab15c133", + "id": "941a5fc4-edfc-4fba-8bc8-22f3a7387e91", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.185286Z", - "iopub.status.busy": "2023-07-24T15:01:00.185178Z", - "iopub.status.idle": "2023-07-24T15:01:00.187923Z", - "shell.execute_reply": "2023-07-24T15:01:00.187503Z" + "iopub.execute_input": "2023-07-24T15:00:57.648616Z", + "iopub.status.busy": "2023-07-24T15:00:57.648475Z", + "iopub.status.idle": "2023-07-24T15:00:57.651921Z", + "shell.execute_reply": "2023-07-24T15:00:57.651456Z" } }, "outputs": [], "source": [ - "print(tp.has_leak(b))" + "@tp.compile\n", + "def my_function(x : tp.types.EventSetOrNode, a:bool) -> tp.types.EventSetOrNode:\n", + " if a:\n", + " return x.rename(\"a_branch\")\n", + " else:\n", + " return x.rename(\"non_a_branch\")\n", + "\n", + "print(my_function(input_evset, a=True).schema.features)\n", + "\n", + "print(my_function(input_evset, a=False).schema.features)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "57eff095-9188-4add-b904-7679ed86a2de", + "metadata": {}, + "source": [ + "If you want to create a program conditional on EventSet data, you can use `EventSet.filter()`." ] }, { "cell_type": "code", "execution_count": null, - "id": "cd455fc1", + "id": "a9ab68f3-91a6-445e-99c2-7f2379185f21", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.190006Z", - "iopub.status.busy": "2023-07-24T15:01:00.189857Z", - "iopub.status.idle": "2023-07-24T15:01:00.192310Z", - "shell.execute_reply": "2023-07-24T15:01:00.191954Z" + "iopub.execute_input": "2023-07-24T15:00:57.654090Z", + "iopub.status.busy": "2023-07-24T15:00:57.653930Z", + "iopub.status.idle": "2023-07-24T15:00:57.658773Z", + "shell.execute_reply": "2023-07-24T15:00:57.658419Z" } }, "outputs": [], "source": [ - "print(tp.has_leak(c))" + "@tp.compile\n", + "def my_function(x : tp.types.EventSetOrNode) -> tp.types.EventSetOrNode:\n", + " return x[\"value\"].filter(x[\"condition\"])\n", + "\n", + "my_function(tp.event_set(\n", + "\ttimestamps=[1,2,3],\n", + "\tfeatures={\n", + " \"value\": [10, 11, 12],\n", + " \"condition\":[True, True, False]}\n", + "))" ] }, { "attachments": {}, "cell_type": "markdown", - "id": "8d973547", - "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 - }, + "id": "80ebc42e-4cc4-4928-b815-af353ae41a11", + "metadata": {}, "source": [ - "By using `tp.has_leak()`, we can programmatically identify future leakage and modify our code accordingly.\n", - "\n", - "## Accessing `EventSet` data\n", - "\n", - "`EventSet` data can be accessed using their `data` attribute. Temporian internally relies on NumPy, which means that the data access functions always return NumPy arrays." + "To simplify its usage when the graph contains a single output `EventSetNode`, `node.run(...)` is equivalent to `tp.run(node, ...)`." ] }, { - "cell_type": "code", - "execution_count": null, - "id": "7da63117", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.194430Z", - "iopub.status.busy": "2023-07-24T15:01:00.194277Z", - "iopub.status.idle": "2023-07-24T15:01:00.198829Z", - "shell.execute_reply": "2023-07-24T15:01:00.198424Z" - } - }, - "outputs": [], + "attachments": {}, + "cell_type": "markdown", + "id": "79ed1db9-43cf-4d8d-8d19-c6594565b71e", + "metadata": {}, "source": [ - "evset = tp.event_set(\n", - "\ttimestamps=[1, 2, 3, 5, 6],\n", - "\tfeatures={\n", - " \"f1\": [0.1, 0.2, 0.3, 1.1, 1.2],\n", - " \"f2\": [\"red\", \"red\", \"red\", \"blue\", \"blue\"],\n", - "\t},\n", - "\tindexes=[\"f2\"],\n", - ")\n", "\n", - "# Access the data for the index group `f2=red`.\n", - "evset.get_index_value((\"red\",))" + "\n", + "\n", + "**Warning:** It is more efficient to run multiple output `EventSetNodes` together with `tp.run()` than to run them separately with `node_1.run(...)`, `node_2.run(...)`, etc." ] }, { "attachments": {}, "cell_type": "markdown", - "id": "eddfa88e", + "id": "0da789f5-a0dd-4b7b-8887-b5a9c2801678", "metadata": { "cell_marker": "\"\"\"", "lines_to_next_cell": 0 }, "source": [ - "\n", - "\n", - "## Import and export data\n", - "\n", - "`EventSets` can be read from and saved to csv files via the `tp.from_csv()` and `tp.to_csv()` functions.\n", - "\n", - "```python\n", - "evset = tp.from_csv( # Read EventSet from a .csv file.\n", - " path=\"path/to/file.csv\",\n", - " timestamps=\"timestamp\",\n", - " indexes=[\"product_id\"],\n", - ")\n", - "\n", - "tp.to_csv(evset, path=\"path/to/file.csv\") # Save EventSet to a .csv file.\n", - "```\n", + "Previously, we defined the input of the graph with `tp.input_node()`. This way of listing features manually and their respective data type is cumbersome.\n", "\n", - "Converting `EventSet` data to and from pandas DataFrames is also easily done via `tp.to_pandas()` and `tp.from_pandas()`." + "If an `EventSet` is available (i.e., data is available) this step can be changed to use `evset.node()` instead, which will return an `EventSetNode` that is compatible with it. This is especially useful when creating `EventSets` from existing data, such as pandas DataFrames or CSV files." ] }, { "cell_type": "code", "execution_count": null, - "id": "da239494", + "id": "d4fc33b0", "metadata": { "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.200932Z", - "iopub.status.busy": "2023-07-24T15:01:00.200760Z", - "iopub.status.idle": "2023-07-24T15:01:00.212641Z", - "shell.execute_reply": "2023-07-24T15:01:00.212298Z" - } + "iopub.execute_input": "2023-07-24T15:00:57.661104Z", + "iopub.status.busy": "2023-07-24T15:00:57.660955Z", + "iopub.status.idle": "2023-07-24T15:00:57.664631Z", + "shell.execute_reply": "2023-07-24T15:00:57.664234Z" + }, + "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "df = pd.DataFrame({\n", - " \"timestamp\": [1, 2, 3, 5, 6],\n", - " \"f1\": [0.1, 0.2, 0.3, 1.1, 1.2],\n", - " \"f2\": [\"red\", \"red\", \"red\", \"blue\", \"blue\"],\n", - "})\n", + "# Define an EventSet.\n", + "a_evset = tp.event_set(\n", + "\ttimestamps=[0, 1, 2],\n", + "\tfeatures={\n", + " \"feature_1\": [1.0, 2.0, 3.0],\n", + " \"feature_2\": [\"hello\", \"little\", \"dog\"],\n", + " \"feature_3\": [\"A\", \"A\", \"B\"],\n", + "\t}\n", + ")\n", "\n", - "# Create EventSet from DataFrame.\n", - "evset = tp.from_pandas(df)\n", + "# The following three statements are (almost) equivalent.\n", + "a_node = tp.input_node(\n", + " features=[\n", + " (\"feature_1\", tp.float64),\n", + " (\"feature_2\", tp.str_),\n", + " ],\n", + " indexes=[(\"feature_3\", tp.str_)])\n", "\n", - "# Convert EventSet to DataFrame.\n", - "df = tp.to_pandas(evset)" + "a_node = tp.input_node(\n", + " features=a_evset.schema.features,\n", + " indexes=a_evset.schema.indexes\n", + ")\n", + " \n", + "a_node = a_evset.node()" ] }, { @@ -2239,7 +2243,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.10.13" } }, "nbformat": 4, From a815629b03f9452c2d64f389cb56f6a6ac5b6acf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Braulio=20R=C3=ADos?= Date: Thu, 26 Oct 2023 17:46:30 -0300 Subject: [PATCH 2/9] Move user guide to use eager mode --- docs/src/user_guide.ipynb | 801 ++++++++++++-------------------------- 1 file changed, 244 insertions(+), 557 deletions(-) diff --git a/docs/src/user_guide.ipynb b/docs/src/user_guide.ipynb index d6550666a..29a7e7054 100644 --- a/docs/src/user_guide.ipynb +++ b/docs/src/user_guide.ipynb @@ -8,7 +8,7 @@ "source": [ "# User Guide\n", "\n", - "This is a complete tour of Temporian's capabilities. For a brief introduction to how the library works, please refer to [3 minutes to Temporian](./3_minutes).\n" + "This is a complete tour of Temporian's capabilities. For a quick hands-on overview, make sure to check the [Getting started guide](./getting_started)." ] }, { @@ -60,10 +60,10 @@ "**Remarks:**\n", "\n", "- All values for a given feature are of the same data type. For instance, `feature_1` is float64 while `feature_2` is a string.\n", - "- Many operators interpret the value NaN (for _not a number_) as missing.\n", + "- Operators handle the value NaN (for _not a number_) as missing (e.g., it's ignored in moving averages).\n", "- Timestamps are not necessarily uniformly sampled.\n", "- The same timestamp can be repeated.\n", - "- The events withing an EventSet are sampled synchronously. However, different EventSets might be sampled differently.\n", + "- Timestamps can be floating point numbers (in seconds) that don't represent a datetime.\n", "\n", "In the next code examples, variables with names like `evset` refer to an `EventSet`.\n", "\n", @@ -111,7 +111,7 @@ "metadata": {}, "outputs": [], "source": [ - "print(evset)" + "evset" ] }, { @@ -130,14 +130,7 @@ "cell_type": "code", "execution_count": null, "id": "e3f29b64", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:56.491002Z", - "iopub.status.busy": "2023-07-24T15:00:56.490848Z", - "iopub.status.idle": "2023-07-24T15:00:57.122544Z", - "shell.execute_reply": "2023-07-24T15:00:57.122043Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "evset.plot()" @@ -169,25 +162,16 @@ "id": "e7a88b45-bef7-46ac-88b8-f376cc14ee88", "metadata": {}, "source": [ - "## Operators and eager mode\n", + "## Operators\n", "\n", - "Processing operations are performed by **Operators**. For instance, the `EventSet.simple_moving_average()` operator computes the [simple moving average](https://en.wikipedia.org/wiki/Moving_average) of each feature in an `EventSet`.\n", - "\n", - "The list of all operators is available in the [API Reference](../reference/)." + "`EventSets` are transformed using **Operators**. For instance, the `EventSet.simple_moving_average()` operator computes the [simple moving average](https://en.wikipedia.org/wiki/Moving_average) of each feature." ] }, { "cell_type": "code", "execution_count": null, "id": "f0c059c3-1412-47d5-8f21-8c034f16a551", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.125248Z", - "iopub.status.busy": "2023-07-24T15:00:57.125018Z", - "iopub.status.idle": "2023-07-24T15:00:57.312917Z", - "shell.execute_reply": "2023-07-24T15:00:57.312518Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Create an event set with a random walk\n", @@ -199,13 +183,24 @@ "\tfeatures={\"value\": random_walk}\n", ")\n", "\n", - "# Compute a simple moving average\n", + "# Compute a simple moving average (1 second, equivalent to tp.duration.seconds(1))\n", "result = evset.simple_moving_average(window_length=1)\n", "\n", "# Plot the results\n", "tp.plot([evset, result]) " ] }, + { + "cell_type": "markdown", + "id": "df79875e-7802-4316-94da-9923650c01b5", + "metadata": {}, + "source": [ + "By default, all durations in Temporian are given in **seconds**. So, `window_length=1` means 1 second of duration.\n", + "See the next section to see how to specify other time units.\n", + "\n", + "The list of all operators is available in the [API Reference](../reference/)." + ] + }, { "attachments": {}, "cell_type": "markdown", @@ -215,14 +210,12 @@ "lines_to_next_cell": 0 }, "source": [ - "\n", "## Time units\n", "\n", - "In Temporian, times are always represented by a float64 value. Users have the freedom to choose the semantic to this value. For example, the time can be the number of nanoseconds since the start of the day, the number of cycles of a process, the number of years since the big bang, or the number of seconds since January 1, 1970, at 00:00:00 UTC, also known as Unix or POSIX time.\n", - "\n", - "To ease the feature engineering of dates, Temporian contains a set of _calendar operators_. These operators specialize in creating features from dates and datetimes. For instance, the `EventSet.calendar_hour()` operator returns the hour of the date in the range `0-23`.\n", + "### Timestamps and datetimes\n", + "In Temporian, timestamps are always represented by a `float64` value in **seconds**.\n", "\n", - "Calendar operators require the time in their inputs to be Unix time, so applying them on non-Unix timestamps will raise errors. Temporian can sometimes automatically recognize if input timestamps correspond to Unix time (e.g. when an `EventSet` is created from a pandas DataFrame with a datetime column, or when passing a list of datetime objects as timestamps in `EventSet`'s constructor). If creating `EventSets` manually and passing floats directly to `timestamps`, you need to explicitly specify whether they correspond to Unix times or not via the `is_unix_timestamp` argument." + "When datetimes are provided as the timestamps, Temporian internally converts them to the number of seconds since January 1, 1970, at 00:00:00 UTC, also known as Unix or POSIX time. But the printing and plotting functions will still show datetimes, which are easier to interpret by humans." ] }, { @@ -230,30 +223,51 @@ "execution_count": null, "id": "92ec9711", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.666655Z", - "iopub.status.busy": "2023-07-24T15:00:57.666536Z", - "iopub.status.idle": "2023-07-24T15:00:57.679313Z", - "shell.execute_reply": "2023-07-24T15:00:57.678950Z" - }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ + "from datetime import datetime\n", + "\n", "a_evset = tp.event_set(\n", " timestamps=[\n", - " pd.to_datetime(\"Monday Mar 13 12:00:00 2023\", utc=True),\n", - " pd.to_datetime(\"Tuesday Mar 14 12:00:00 2023\", utc=True),\n", - " pd.to_datetime(\"Friday Mar 17 00:00:01 2023\", utc=True),\n", + " # strings are interpreted as datetimes\n", + " \"2023-03-13\",\n", + " \"2023-03-14 13:30:05\",\n", + "\n", + " # python and pandas datetimes are ok too :)\n", + " datetime(2023, 4, 22, 12, 00),\n", + " pd.to_datetime(\"Tue, Mar 14 12:00:00 2023\", utc=True),\n", " ],\n", " features={\n", - " \"feature_1\": [1, 2, 3],\n", - " \"feature_2\": [\"a\", \"b\", \"c\"],\n", + " \"feature_1\": [1, 2, 3, 4],\n", + " \"feature_2\": [\"a\", \"b\", \"c\", \"d\"],\n", " },\n", ")\n", - "a_node = a_evset.node()\n", - "b_node = tp.glue(a_node, a_node.calendar_day_of_week())\n", - "b_node.run(a_evset)" + "a_evset" + ] + }, + { + "cell_type": "markdown", + "id": "e23789b4-3b14-47ee-ad49-3980e9d111c7", + "metadata": {}, + "source": [ + "If you specify floating point numbers as the timestamps (which might be useful to handle signals such as ECG, audio, etc.), then the _calendar operators_ like `EventSet.calendar_hour()` or `EventSet.calendar_day_of_month()` are disabled, they will raise an error unless you indicate that these numbers represent datetimes, by setting `is_unix_timestamp=True` explicitly." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4869f529-350b-43a5-af07-fded47ccad54", + "metadata": {}, + "outputs": [], + "source": [ + "a_evset = tp.event_set(\n", + " timestamps=[0, 1, 2],\n", + " is_unix_timestamp=True\n", + ")\n", + "\n", + "tp.glue(a_evset.calendar_year(), a_evset.calendar_hour(), a_evset.calendar_second())" ] }, { @@ -265,33 +279,33 @@ "lines_to_next_cell": 0 }, "source": [ + "### Durations\n", "\n", - "Temporian accepts time inputs in various formats, including integer, float, Python date or datetime, NumPy datetime, and pandas datetime. Date and datetime objects are internally converted to floats as Unix time in seconds, compatible with the calendar operators.\n", - "\n", - "Operators can take _durations_ as input arguments. For example, the simple moving average operator takes a `window_length` argument. Temporian exposes several utility functions to help creating those duration arguments when using Unix timestamps:" + "Operators can take _durations_ as input arguments, which are always in seconds, as mentioned above. This is compatible with datetime-like timestamps, as well as floating point timestamps that don't represent a datetime." ] }, { "cell_type": "code", "execution_count": null, - "id": "508c3f0c", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.681429Z", - "iopub.status.busy": "2023-07-24T15:00:57.681281Z", - "iopub.status.idle": "2023-07-24T15:00:57.684114Z", - "shell.execute_reply": "2023-07-24T15:00:57.683752Z" - } - }, + "id": "899fee67-01d0-4680-a223-7b7b144a46ca", + "metadata": {}, "outputs": [], "source": [ - "a = tp.input_node(features=[(\"feature_1\", tp.float64)])\n", + "a = tp.event_set(timestamps=[\"2023-03-13\", \"2023-03-14\", \"2023-03-15\"])\n", "\n", - "# Define a 1-day moving average.\n", - "b = a.simple_moving_average(window_length=tp.duration.days(1))\n", + "# Number of seconds in 2 days:\n", + "a.moving_count(window_length=2 * 24 * 60 * 60)\n", "\n", - "# Equivalent.\n", - "b = a.simple_moving_average(window_length=24 * 60 * 60)" + "# Better option:\n", + "a.moving_count(window_length=tp.duration.days(2))" + ] + }, + { + "cell_type": "markdown", + "id": "374ce207-f369-446a-9b6d-fd5d25939321", + "metadata": {}, + "source": [ + "**Note:** all window operators work with left-open intervals, so for each `t` in the sampling, events in `(t - window_length, t]` will be considered (i.e., the exact time `t - window_length` is not included). That is why the moving window at `t=2023-03-15` doesn't include the event at `t=2023-03-13`, when using a window length of 2 days." ] }, { @@ -319,14 +333,7 @@ "cell_type": "code", "execution_count": null, "id": "cbf42f42", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.686202Z", - "iopub.status.busy": "2023-07-24T15:00:57.685957Z", - "iopub.status.idle": "2023-07-24T15:00:57.884670Z", - "shell.execute_reply": "2023-07-24T15:00:57.884262Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "evset = tp.event_set(\n", @@ -360,14 +367,7 @@ "cell_type": "code", "execution_count": null, "id": "82552e04", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.887017Z", - "iopub.status.busy": "2023-07-24T15:00:57.886848Z", - "iopub.status.idle": "2023-07-24T15:00:58.113203Z", - "shell.execute_reply": "2023-07-24T15:00:58.112793Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "figure = evset.plot(\n", @@ -396,19 +396,12 @@ "cell_type": "code", "execution_count": null, "id": "37feddd0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:58.115589Z", - "iopub.status.busy": "2023-07-24T15:00:58.115426Z", - "iopub.status.idle": "2023-07-24T15:01:00.005021Z", - "shell.execute_reply": "2023-07-24T15:01:00.004601Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "!pip install bokeh -q\n", "\n", - "evset.plot(interactive=True)" + "evset.plot(interactive=True, width_px=500)" ] }, { @@ -423,31 +416,23 @@ "\n", "## Feature naming\n", "\n", - "Each feature is identified by a name, and the list of features is available through the `features` property of an `EventSetNode`." + "Each feature is identified by a name, and the list of features is available through the `schema.feature_names()` method of an `EventSet`." ] }, { "cell_type": "code", "execution_count": null, "id": "38ff0e20", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.007393Z", - "iopub.status.busy": "2023-07-24T15:01:00.007171Z", - "iopub.status.idle": "2023-07-24T15:01:00.010463Z", - "shell.execute_reply": "2023-07-24T15:01:00.010135Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "events = tp.event_set(\n", - "\ttimestamps=[1,2,3,4,5],\n", + "evset = tp.event_set(\n", + "\ttimestamps=[0],\n", "\tfeatures={\n", - "\t \"feature_1\": [0.5, 0.6, 0.4, 0.4, 0.9],\n", - "\t \"feature_2\": [1.0, 2.0, 3.0, 2.0, 1.0]}\n", + "\t \"feature_1\": [0.1],\n", + "\t \"feature_2\": [0.2]}\n", " )\n", - "node = events.node()\n", - "print(node.features)" + "print(evset.schema.feature_names())" ] }, { @@ -467,17 +452,10 @@ "cell_type": "code", "execution_count": null, "id": "838449d4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.012599Z", - "iopub.status.busy": "2023-07-24T15:01:00.012475Z", - "iopub.status.idle": "2023-07-24T15:01:00.015736Z", - "shell.execute_reply": "2023-07-24T15:01:00.015395Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "node.moving_sum(window_length=10).features" + "evset.moving_sum(window_length=10).schema.feature_names()" ] }, { @@ -497,18 +475,10 @@ "cell_type": "code", "execution_count": null, "id": "b10fdec3", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.017898Z", - "iopub.status.busy": "2023-07-24T15:01:00.017753Z", - "iopub.status.idle": "2023-07-24T15:01:00.021061Z", - "shell.execute_reply": "2023-07-24T15:01:00.020666Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "result = node[\"feature_1\"] * node[\"feature_2\"]\n", - "result.features" + "(evset[\"feature_1\"] * evset[\"feature_2\"]).schema.feature_names()" ] }, { @@ -521,7 +491,7 @@ }, "source": [ "\n", - "The calendar operators don't depend on input features but on the timestamps, so the output feature name doesn't\n", + "The calendar operators don't depend on input features but only on the timestamps, so the output feature name doesn't\n", "relate to the input feature names." ] }, @@ -529,22 +499,14 @@ "cell_type": "code", "execution_count": null, "id": "4182d3f8", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.023467Z", - "iopub.status.busy": "2023-07-24T15:01:00.023325Z", - "iopub.status.idle": "2023-07-24T15:01:00.026531Z", - "shell.execute_reply": "2023-07-24T15:01:00.026132Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "date_events = tp.event_set(\n", "\ttimestamps=[\"2020-02-15\", \"2020-06-20\"],\n", "\tfeatures={\"some_feature\": [10, 20]}\n", " )\n", - "date_node = date_events.node()\n", - "print(date_node.calendar_month().features)" + "print(date_events.calendar_month().schema.feature_names())" ] }, { @@ -564,78 +526,33 @@ "cell_type": "code", "execution_count": null, "id": "bc36bc1f", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.029017Z", - "iopub.status.busy": "2023-07-24T15:01:00.028848Z", - "iopub.status.idle": "2023-07-24T15:01:00.031960Z", - "shell.execute_reply": "2023-07-24T15:01:00.031429Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Rename a single feature.\n", - "renamed_f1 = node[\"feature_1\"].rename(\"renamed_1\")\n", - "print(renamed_f1.features)" + "evset[\"feature_1\"].rename(\"renamed_1\")" ] }, { "cell_type": "code", "execution_count": null, "id": "a44f6e7a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.042574Z", - "iopub.status.busy": "2023-07-24T15:01:00.042259Z", - "iopub.status.idle": "2023-07-24T15:01:00.045691Z", - "shell.execute_reply": "2023-07-24T15:01:00.044933Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Rename all features.\n", - "renamed_node = node.rename(\n", - " {\"feature_1\": \"renamed_1\", \"feature_2\": \"renamed_2\"}\n", - ")\n", - "print(renamed_node.features)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "af6103fc", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.048841Z", - "iopub.status.busy": "2023-07-24T15:01:00.048430Z", - "iopub.status.idle": "2023-07-24T15:01:00.051757Z", - "shell.execute_reply": "2023-07-24T15:01:00.051319Z" - } - }, - "outputs": [], - "source": [ - "# Prefix a single feature.\n", - "prefixed_f1 = node[\"feature_1\"].prefix(\"prefixed.\")\n", - "print(prefixed_f1.features)" + "evset.rename({\"feature_1\": \"renamed_1\", \"feature_2\": \"renamed_2\"})" ] }, { "cell_type": "code", "execution_count": null, "id": "3126cbed", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.053697Z", - "iopub.status.busy": "2023-07-24T15:01:00.053582Z", - "iopub.status.idle": "2023-07-24T15:01:00.056089Z", - "shell.execute_reply": "2023-07-24T15:01:00.055703Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Prefix all features.\n", - "prefixed_node = node.prefix(\"prefixed.\")\n", - "print(prefixed_node.features)" + "evset.prefix(\"prefixed.\")" ] }, { @@ -655,18 +572,11 @@ "cell_type": "code", "execution_count": null, "id": "852abbdd", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.058375Z", - "iopub.status.busy": "2023-07-24T15:01:00.058188Z", - "iopub.status.idle": "2023-07-24T15:01:00.061323Z", - "shell.execute_reply": "2023-07-24T15:01:00.060844Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "sma_7_node = node.simple_moving_average(tp.duration.days(7)).prefix(\"sma_7.\")\n", - "sma_14_node = node.simple_moving_average(tp.duration.days(14)).prefix(\"sma_14.\")" + "sma_7_node = evset.simple_moving_average(tp.duration.days(7)).prefix(\"sma_7.\")\n", + "sma_14_node = evset.simple_moving_average(tp.duration.days(14)).prefix(\"sma_14.\")" ] }, { @@ -679,26 +589,19 @@ }, "source": [ "\n", - "The `tp.glue()` operator can be used to concatenate different features into a single `EventSetNode`, but it will fail if two features with the same name are provided. The following pattern is commonly used in Temporian programs." + "The `tp.glue()` operator can be used to concatenate different features into a single `EventSet`, but it will fail if two features with the same name are provided. The following pattern is commonly used in Temporian programs to avoid errors:" ] }, { "cell_type": "code", "execution_count": null, "id": "f7d85e33", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.063510Z", - "iopub.status.busy": "2023-07-24T15:01:00.063393Z", - "iopub.status.idle": "2023-07-24T15:01:00.067035Z", - "shell.execute_reply": "2023-07-24T15:01:00.066516Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "result = tp.glue(\n", - " node.simple_moving_average(tp.duration.days(7)).prefix(\"sma_7.\"),\n", - " node.simple_moving_average(tp.duration.days(14)).prefix(\"sma_14.\"),\n", + " evset.simple_moving_average(tp.duration.days(7)).prefix(\"sma_7.\"),\n", + " evset.simple_moving_average(tp.duration.days(14)).prefix(\"sma_14.\"),\n", ")" ] }, @@ -714,25 +617,23 @@ "\n", "## Casting\n", "\n", - "Temporian is strict on feature data types (also called dtype). This means that often, you cannot perform operations between features of different types. For example, you cannot subtract a `tp.float32` and a `tp.float64`. Instead, you must manually cast the features to the same type before performing the operation." + "Temporian is strict on feature data types (also called dtype). This means that often, you cannot perform operations between features of different types. For example, you cannot subtract a `tp.float64` and a `tp.int64`. Instead, you must manually cast the features to the same type before performing the operation." ] }, { "cell_type": "code", "execution_count": null, "id": "e1276be1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.069531Z", - "iopub.status.busy": "2023-07-24T15:01:00.069387Z", - "iopub.status.idle": "2023-07-24T15:01:00.072411Z", - "shell.execute_reply": "2023-07-24T15:01:00.071968Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "node = tp.input_node(features=[(\"f1\", tp.float32), (\"f2\", tp.float64)])\n", - "added = node[\"f1\"].cast(tp.float64) + node[\"f2\"]" + "evset = tp.event_set(timestamps=[0, 1],\n", + " features={'f1': [0.5, 1.1],\n", + " 'f2': [1, 2]\n", + " }\n", + " )\n", + "# Can't add float64 and int64, explicit cast required:\n", + "result = evset['f1'] + evset['f2'].cast(float)" ] }, { @@ -743,53 +644,25 @@ "lines_to_next_cell": 0 }, "source": [ + "Using python's `float` above is equivalent to using `tp.float64`, like using `int` is equivalent to `tp.int64`.\n", "\n", "Casting is especially useful to reduce memory usage. For example, if a feature only contains values between 0 and 10000, using `tp.int32` instead of `tp.int64` will halve memory usage. These optimizations are critical when working with large datasets.\n", "\n", "Casting can also be a necessary step before calling operators that only accept certain input data types.\n", "\n", - "Note that in Python, the values `1.0` and `1` are respectively `float64` and `int64`.\n", - "\n", - "Temporian supports data type casting through the `EventSet.cast()` operator. Destination data types can be specified in three different ways:\n", + "When using `EventSet.cast()`, destination data types can be specified in three different ways:\n", "\n", - "1. Single data type: converts all input features to the same destination data type.\n", - "\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3e3ee4c4", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.074589Z", - "iopub.status.busy": "2023-07-24T15:01:00.074466Z", - "iopub.status.idle": "2023-07-24T15:01:00.077932Z", - "shell.execute_reply": "2023-07-24T15:01:00.077413Z" - }, - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "node.features" + "1. Single data type: converts all input features to the same destination data type.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "6c2444ba", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.080190Z", - "iopub.status.busy": "2023-07-24T15:01:00.079991Z", - "iopub.status.idle": "2023-07-24T15:01:00.082867Z", - "shell.execute_reply": "2023-07-24T15:01:00.082457Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "print(node.cast(tp.str_).features)" + "numbers_to_str = evset.cast(str)" ] }, { @@ -809,17 +682,10 @@ "cell_type": "code", "execution_count": null, "id": "05ee00a7", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.085010Z", - "iopub.status.busy": "2023-07-24T15:01:00.084841Z", - "iopub.status.idle": "2023-07-24T15:01:00.087364Z", - "shell.execute_reply": "2023-07-24T15:01:00.087026Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "print(node.cast({\"f1\": tp.str_, \"f2\": tp.int64}).features)" + "evset.cast({\"f1\": str, \"f2\": tp.int32})" ] }, { @@ -839,17 +705,10 @@ "cell_type": "code", "execution_count": null, "id": "6deaff88", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.089508Z", - "iopub.status.busy": "2023-07-24T15:01:00.089353Z", - "iopub.status.idle": "2023-07-24T15:01:00.091835Z", - "shell.execute_reply": "2023-07-24T15:01:00.091498Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "print(node.cast({tp.float32: tp.str_, tp.float64: tp.int64}).features)" + "half_precision = evset.cast({float: tp.float32, int: tp.int32})" ] }, { @@ -878,12 +737,6 @@ "execution_count": null, "id": "2c673b17", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.094177Z", - "iopub.status.busy": "2023-07-24T15:01:00.094010Z", - "iopub.status.idle": "2023-07-24T15:01:00.098574Z", - "shell.execute_reply": "2023-07-24T15:01:00.098140Z" - }, "lines_to_next_cell": 0 }, "outputs": [], @@ -897,12 +750,7 @@ " \"f4\": [1000.0, 1000.0],\n", " },\n", ")\n", - "node = evset.node()\n", - "\n", - "node_added = node[[\"f1\", \"f2\"]] + node[[\"f3\", \"f4\"]]\n", - "\n", - "evset_added = node_added.run(evset)\n", - "print(evset_added)" + "evset[[\"f1\", \"f2\"]] + evset[[\"f3\", \"f4\"]]" ] }, { @@ -914,7 +762,7 @@ "lines_to_next_cell": 0 }, "source": [ - "Note that features of type `int64` and `float64` are not mixed above, because otherwise the operation would fail without an explicit type cast." + "Note that we're adding both `int64` and both `float64` features, otherwise the operation would fail without an explicit cast (see the previous casting section)." ] }, { @@ -927,7 +775,7 @@ }, "source": [ "```python\n", - ">>> node[\"f1\"] + node[\"f2\"] # Attempt to mix dtypes.\n", + ">>> evset[\"f1\"] + evset[\"f2\"] # Attempt to mix dtypes int and float.\n", "Traceback (most recent call last):\n", " ...\n", "ValueError: corresponding features should have the same dtype. ...\n", @@ -943,27 +791,7 @@ "lines_to_next_cell": 0 }, "source": [ - "Refer to the [Casting](#casting) section for more on this.\n", - "\n", - "All the operators have an equivalent functional form. The example above using `+`, could be rewritten with `tp.add()`." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3b350c4a", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.101025Z", - "iopub.status.busy": "2023-07-24T15:01:00.100848Z", - "iopub.status.idle": "2023-07-24T15:01:00.103444Z", - "shell.execute_reply": "2023-07-24T15:01:00.103080Z" - } - }, - "outputs": [], - "source": [ - "# Equivalent.\n", - "node_added = tp.add(node[[\"f1\", \"f2\"]], node[[\"f3\", \"f4\"]])" + "Refer to the [Casting](#casting) section to learn how to cast features and solve this issue." ] }, { @@ -975,25 +803,20 @@ "lines_to_next_cell": 0 }, "source": [ - "Other usual comparison and logic operators also work (except `==`, see below)." + "### Comparisons\n", + "\n", + "The usual comparison and logic operators perform element-wise comparison (except `==`, see below)." ] }, { "cell_type": "code", "execution_count": null, "id": "f1462eea", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.105489Z", - "iopub.status.busy": "2023-07-24T15:01:00.105344Z", - "iopub.status.idle": "2023-07-24T15:01:00.108344Z", - "shell.execute_reply": "2023-07-24T15:01:00.107952Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "is_greater = node[[\"f1\", \"f2\"]] > node[[\"f3\", \"f4\"]]\n", - "is_less_or_equal = node[[\"f1\", \"f2\"]] <= node[[\"f3\", \"f4\"]]\n", + "is_greater = evset[[\"f1\", \"f2\"]] > evset[[\"f3\", \"f4\"]]\n", + "is_less_or_equal = evset[[\"f1\", \"f2\"]] <= evset[[\"f3\", \"f4\"]]\n", "is_wrong = is_greater & is_less_or_equal" ] }, @@ -1006,43 +829,39 @@ "lines_to_next_cell": 0 }, "source": [ + "The results are `EventSets` with boolean features, which can be used with other operators such as `filter()` or `where()`.\n", + "\n", "**Warning:** The Python equality operator (`==`) does not compute element-wise equality between features. Use the `evset.equal()` operator instead." ] }, { "cell_type": "code", "execution_count": null, - "id": "c610a0d0", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.110338Z", - "iopub.status.busy": "2023-07-24T15:01:00.110212Z", - "iopub.status.idle": "2023-07-24T15:01:00.113649Z", - "shell.execute_reply": "2023-07-24T15:01:00.113316Z" - } - }, + "id": "c67262d9-0a72-41ed-b020-12d95c5d2879", + "metadata": {}, "outputs": [], "source": [ - "# Works element-wise as expected\n", - "node[\"f1\"].equal(node[\"f3\"])" + "# This is NOT event-wise comparison, just a python boolean\n", + "evset[\"f1\"] == evset[\"f2\"]" + ] + }, + { + "cell_type": "markdown", + "id": "d65cbeb6-8de3-4824-8bca-815d88055a45", + "metadata": {}, + "source": [ + "For this particular comparison to work event-wise, use the `EventSet.equal()` method instead:" ] }, { "cell_type": "code", "execution_count": null, - "id": "abda404b", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.115541Z", - "iopub.status.busy": "2023-07-24T15:01:00.115402Z", - "iopub.status.idle": "2023-07-24T15:01:00.118082Z", - "shell.execute_reply": "2023-07-24T15:01:00.117723Z" - } - }, + "id": "c610a0d0", + "metadata": {}, "outputs": [], "source": [ - "# This is just a boolean\n", - "(node[\"f1\"] == node[\"f3\"])" + "# Works element-wise as expected\n", + "evset[\"f1\"].equal(evset[\"f3\"])" ] }, { @@ -1057,23 +876,6 @@ "All these operators act feature-wise, i.e. they perform index-feature-wise operations (for each feature in each index key). This implies that the input `EventSets` must have the same number of features." ] }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "0352b9de", - "metadata": { - "cell_marker": "\"\"\"", - "lines_to_next_cell": 0 - }, - "source": [ - "```python\n", - "node[[\"f1\", \"f2\"]] + node[\"f3\"]\n", - "Traceback (most recent call last):\n", - " ...\n", - "ValueError: The left and right arguments should have the same number of features. ...\n", - "```" - ] - }, { "attachments": {}, "cell_type": "markdown", @@ -1084,7 +886,7 @@ }, "source": [ "\n", - "The input `EventSets` must also have the same sampling and index." + "The input `EventSets` must also have the same sampling and index. Otherwise, Temporian raises an error:" ] }, { @@ -1097,19 +899,38 @@ }, "source": [ "```python\n", - "sampling_1 = tp.event_set(\n", - " timestamps=[0, 1],\n", - " features={\"f1\": [1, 2]},\n", - ")\n", - "sampling_2 = tp.event_set(\n", - " timestamps=[1, 2],\n", - " features={\"f1\": [3, 4]},\n", - ")\n", - "sampling_1.node() + sampling_2.node()\n", + ">>> sampling_1 = tp.event_set(\n", + "... timestamps=[0, 1],\n", + "... features={\"f1\": [1, 2]},\n", + "... )\n", + ">>> sampling_2 = tp.event_set(\n", + "... timestamps=[1, 2],\n", + "... features={\"f1\": [3, 4]},\n", + "... )\n", + ">>> sampling_1 + sampling_2\n", "Traceback (most recent call last):\n", " ...\n", "ValueError: Arguments should have the same sampling. ...\n", - "```" + "\n", + "```\n", + "\n", + "If you want to create two `EventSets` associated to the same sampling source (same timestamps and indexes), use the `same_sampling_as` argument:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1415ede1-3fbc-43d7-94e6-dd5d5ec93bbb", + "metadata": {}, + "outputs": [], + "source": [ + "evset_2 = tp.event_set(\n", + " timestamps=[1, 10],\n", + " features={'f': [1, 2]},\n", + " same_sampling_as=evset\n", + ")\n", + "# Now evset and evset_2 have the same sampling source\n", + "result = evset['f1'] + evset_2['f']" ] }, { @@ -1135,18 +956,11 @@ "execution_count": null, "id": "58fe7d8e", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.120397Z", - "iopub.status.busy": "2023-07-24T15:01:00.120245Z", - "iopub.status.idle": "2023-07-24T15:01:00.123150Z", - "shell.execute_reply": "2023-07-24T15:01:00.122799Z" - }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "node_scalar = node * 10\n", - "print(node_scalar.run(evset))" + "evset * 10" ] }, { @@ -1160,21 +974,16 @@ "source": [ "## Sampling\n", "\n", - "Arithmetic operators, such as `tp.add()`, require their input arguments to have the same timestamps and [Index](#indexes-horizontal-and-vertical-operators). The unique combination of timestamps and indexes is called a _sampling_.\n", - "\n", - "\n", - "\n", - "For example, if `EventSetNodes` `a` and `b` have different samplings, `a[\"feature_1\"] + b[\"feature_2\"]` will fail.\n", + "Arithmetic operators, require their input arguments to have the same timestamps and [Index](#indexes-horizontal-and-vertical-operators), as seen in the previous section. The unique combination of timestamps and indexes is called a _sampling_.\n", "\n", "To use arithmetic operators on `EventSets` with different samplings, one of the `EventSets` needs to be resampled to the sampling of the other `EventSet`. Resampling is done with the `EventSet.resample()` operator.\n", "\n", - "The `EventSet.resample()` operator takes two `EventSets` called `input` and `sampling`, and returns the resampling of the features of `input` according to the timestamps of `sampling` according to the following rules:\n", + "The `EventSet.resample(new_sampling)` operator returns the resampling of the features of the calling `EventSet` with the timestamps of `new_sampling` according to the following rules:\n", "\n", - "If a timestamp is present in `input` but not in `sampling`, the timestamp is dropped.\n", - "If a timestamp is present in both `input` and `sampling`, the timestamp is kept.\n", - "If a timestamp is present in `sampling` but not in `input`, a new timestamp is created using the feature values from the _closest anterior_ (not the closest, as that could induce future leakage) timestamp of `input`. This rule is especially useful for events that represent measurements (see [Events and `EventSets`](#events-and-eventsets)).\n", + "1. For each timestamp in `new_sampling`, use the feature values from the _closest_ event in `EventSet` that is previous or has exactly equal timestamp (future events are ignored as that would induce future leakage). This rule is especially useful for events that represent measurements (see [Events and `EventSets`](#events-and-eventsets)).\n", + "2. If there's no previous event for some timestamps in `new_sampling`, fill with a missing value, which depends on the feature type (`NaN` will be used for `float` features, `0` for `int` features, and `''` for `str` features).\n", "\n", - "**Note:** Features in `sampling` are ignored. This also happens in some other operators that take a `sampling` argument of type `EventSetNode` - it indicates that only the sampling (a.k.a. the indexes and timestamps) of that `EventSetNode` are being used by that operator.\n", + "**Note:** Features in `sampling` are ignored. This also happens in some other operators that take a `sampling` argument of type `EventSet`.\n", "\n", "Given this example:" ] @@ -1184,12 +993,6 @@ "execution_count": null, "id": "087b6fd6", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.125315Z", - "iopub.status.busy": "2023-07-24T15:01:00.125163Z", - "iopub.status.idle": "2023-07-24T15:01:00.129661Z", - "shell.execute_reply": "2023-07-24T15:01:00.129338Z" - }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1200,13 +1003,10 @@ " \"x\": [1.0, 2.0, 3.0],\n", " },\n", ")\n", - "node = evset.node()\n", "sampling_evset = tp.event_set(\n", " timestamps=[0, 9, 10, 11, 19, 20, 21],\n", ")\n", - "sampling_node = sampling_evset.node()\n", - "resampled = node.resample(sampling=sampling_node)\n", - "resampled.run({node: evset, sampling_node: sampling_evset})" + "resampled = evset.resample(sampling=sampling_evset)" ] }, { @@ -1219,20 +1019,14 @@ }, "source": [ "\n", - "The following would be the matching between the timestamps of `sampling` and `input`:\n", + "The following would be the matching between the timestamps of `sampling_evset` and `evset`:\n", "\n", "| `sampling` timestamp | 0 | 9 | 10 | 11 | 19 | 20 | 21 |\n", "| ---------------------------- | --- | --- | --- | --- | --- | --- | --- |\n", - "| matching `input` timestamp | - | - | 10 | 10 | 10 | 20 | 20 |\n", - "| matching `\"x\"` feature value | NaN | NaN | 1 | 1 | 1 | 2 | 2 |\n", - "\n", - "If `sampling` contains a timestamp anterior to any timestamp in the `input` (like 0 and 9 in the example above), the feature of the sampled event will be missing. The representation of a missing value depends on its dtype:\n", + "| previous `evset` timestamp | - | - | 10 | 10 | 10 | 20 | 20 |\n", + "| value for `\"x\"` | NaN | NaN | 1 | 1 | 1 | 2 | 2 |\n", "\n", - "float: `NaN`\n", - "integer: `0`\n", - "string: `\"\"`\n", - "\n", - "Back to the example of the `tp.add()` operator, `a` and `b` with different sampling can be added as follows:" + "Back to the example of the arithmetics operators, `a` and `b` with different sampling can be added as follows:" ] }, { @@ -1240,28 +1034,19 @@ "execution_count": null, "id": "df3bbc7d", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.131774Z", - "iopub.status.busy": "2023-07-24T15:01:00.131623Z", - "iopub.status.idle": "2023-07-24T15:01:00.136202Z", - "shell.execute_reply": "2023-07-24T15:01:00.135874Z" - }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "sampling_a = tp.event_set(\n", + "a = tp.event_set(\n", " timestamps=[0, 1, 2],\n", " features={\"f1\": [10, 20, 30]},\n", ")\n", - "sampling_b = tp.event_set(\n", + "b = tp.event_set(\n", " timestamps=[1, 2, 3],\n", " features={\"f1\": [5, 4, 3]},\n", ")\n", - "a = sampling_a.node()\n", - "b = sampling_b.node()\n", - "result = a + b.resample(a)\n", - "result.run({a: sampling_a, b: sampling_b})" + "result = a + b.resample(a)" ] }, { @@ -1302,18 +1087,18 @@ "r = termometer[\"temperature\"].resample(sampling_source) / manometer[\"pressure\"].resample(sampling_source)\n", "```\n", "\n", - "Moving window operators, such as the `EventSet.simple_moving_average()` or `EventSet.moving_count()` operators, have an optional `sampling` argument. For example, the signature of the simple moving average operator is `EventSet.simple_moving_average(window_length: Duration, sampling: Optional[EventSet] = None)`. If `sampling` is not set, the result will maintain the sampling of the `input` argument. If `sampling` is set, the moving window will be sampled at each timestamp of `sampling` instead, and the result will have those new ones.\n", + "Moving window operators, such as the `EventSet.simple_moving_average()` or `EventSet.moving_count()` operators, have an optional `sampling` argument. For example, the signature of the simple moving average operator is `EventSet.simple_moving_average(window_length: Duration, sampling: Optional[EventSet] = None)`. If `sampling` is not set, the result will maintain the sampling of the original `EventSet`. If `sampling` is set, the moving window will be sampled at each timestamp of `sampling` instead, and the result will have those new ones.\n", "\n", "```python\n", - "b = tp.simple_moving_average(input=a, window_length=10)\n", - "c = tp.simple_moving_average(input=a, window_length=10, sampling=d)\n", + "a.simple_moving_average(window_length=10)\n", + "a.simple_moving_average(window_length=10, sampling=d)\n", "```\n", "\n", "Note that if planning to resample the result of a moving window operator, passing the `sampling` argument is both more efficient and more accurate than calling `.resample()` on the result.\n", "\n", "## Indexes, horizontal and vertical operators\n", "\n", - "All operators presented so far work on a sequence of related events. For instance, the simple moving average operator computes the average of events within a specific time window. These types of operators are called _horizontal operators_.\n", + "All operators presented so far work on a sequence of related events. For instance, the simple moving average operator computes the average of events within a specific time window. These types of operators are called _horizontal operators_ (which makes sense if we think of events as advancing in time from left to right).\n", "\n", "It is sometimes desirable for events in an `EventSet` not to interact with each other. For example, assume a dataset containing the sum of daily sales of a set of products. The objective is to compute the sum of weekly sales of each product independently. In this scenario, the weekly moving sum should be applied individually to each product. If not, you would compute the weekly sales of all the products together.\n", "\n", @@ -1325,12 +1110,6 @@ "execution_count": null, "id": "54a71cae", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.138254Z", - "iopub.status.busy": "2023-07-24T15:01:00.138101Z", - "iopub.status.idle": "2023-07-24T15:01:00.141435Z", - "shell.execute_reply": "2023-07-24T15:01:00.141074Z" - }, "lines_to_next_cell": 0 }, "outputs": [], @@ -1343,7 +1122,7 @@ " },\n", " indexes=[\"product\"]\n", ")\n", - "print(daily_sales)" + "daily_sales.plot()" ] }, { @@ -1364,22 +1143,12 @@ "execution_count": null, "id": "2c87a680", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.143537Z", - "iopub.status.busy": "2023-07-24T15:01:00.143412Z", - "iopub.status.idle": "2023-07-24T15:01:00.147227Z", - "shell.execute_reply": "2023-07-24T15:01:00.146886Z" - }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ - "a = daily_sales.node()\n", - "\n", "# Compute the moving sum of each index group (a.k.a. each product) individually.\n", - "b = a.moving_sum(window_length=tp.duration.weeks(1))\n", - "\n", - "b.run({a: daily_sales})" + "b = a.moving_sum(window_length=tp.duration.weeks(1))" ] }, { @@ -1394,7 +1163,7 @@ "\n", "Horizontal operators can be understood as operators that are applied independently on each index.\n", "\n", - "Operators that modify an `EventSetNode`'s indexes are called _vertical operators_. The most important vertical operators are:\n", + "Operators that modify an `EventSet`'s indexes are called _vertical operators_ (note that they affect the vertical layout of the plots above). The most important vertical operators are:\n", "\n", "- `EventSet.add_index()`: Add features to the index.\n", "- `EventSet.drop_index()`: Remove features from the index, optionally keeping them as features.\n", @@ -1412,14 +1181,7 @@ "cell_type": "code", "execution_count": null, "id": "8badbebf", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.149178Z", - "iopub.status.busy": "2023-07-24T15:01:00.149039Z", - "iopub.status.idle": "2023-07-24T15:01:00.152148Z", - "shell.execute_reply": "2023-07-24T15:01:00.151798Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "daily_sales = tp.event_set(\n", @@ -1429,8 +1191,7 @@ " \"product\": [1, 2, 1, 2],\n", " \"sale\": [100.0, 200.0, 110.0, 300.0],\n", " },\n", - ")\n", - "print(daily_sales)" + ")" ] }, { @@ -1452,19 +1213,12 @@ "execution_count": null, "id": "479096c3", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.154125Z", - "iopub.status.busy": "2023-07-24T15:01:00.154007Z", - "iopub.status.idle": "2023-07-24T15:01:00.157566Z", - "shell.execute_reply": "2023-07-24T15:01:00.157243Z" - }, "lines_to_next_cell": 2 }, "outputs": [], "source": [ - "a = daily_sales.node()\n", - "b = a.add_index([\"product\", \"store\"])\n", - "b.run({a: daily_sales})" + "sales_per_product_store = daily_sales.add_index([\"product\", \"store\"])\n", + "sales_per_product_store" ] }, { @@ -1486,19 +1240,12 @@ "execution_count": null, "id": "4df0d8cf", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.159586Z", - "iopub.status.busy": "2023-07-24T15:01:00.159442Z", - "iopub.status.idle": "2023-07-24T15:01:00.163136Z", - "shell.execute_reply": "2023-07-24T15:01:00.162800Z" - }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ "# Weekly sales by product and store\n", - "c = b[\"sale\"].moving_sum(window_length=tp.duration.weeks(1))\n", - "c.run({a: daily_sales})" + "per_product_weekly = sales_per_product_store.moving_sum(window_length=tp.duration.weeks(1))" ] }, { @@ -1519,20 +1266,12 @@ "execution_count": null, "id": "8d9448a2", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.165204Z", - "iopub.status.busy": "2023-07-24T15:01:00.165078Z", - "iopub.status.idle": "2023-07-24T15:01:00.170459Z", - "shell.execute_reply": "2023-07-24T15:01:00.169929Z" - }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ "# Weekly sales by store (including all products)\n", - "d = b.drop_index(\"product\")\n", - "e = d[\"sale\"].moving_sum(window_length=tp.duration.weeks(1))\n", - "e.run({a: daily_sales})" + "per_store_weekly = per_week.drop_index(\"product\", keep=False).moving_sum(tp.duration.weeks(1))" ] }, { @@ -1555,22 +1294,12 @@ "execution_count": null, "id": "4e12a9e0", "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.172674Z", - "iopub.status.busy": "2023-07-24T15:01:00.172528Z", - "iopub.status.idle": "2023-07-24T15:01:00.177142Z", - "shell.execute_reply": "2023-07-24T15:01:00.176616Z" - }, "lines_to_next_cell": 0 }, "outputs": [], "source": [ - "# Copy the content of e (indexed by (store)) into each (store, product).\n", - "f = c / e.propagate(sampling=c, resample=True)\n", - "\n", - "# Equivalent.\n", - "f = c / e.propagate(sampling=c).resample(sampling=c)\n", - "print(f.run({a: daily_sales}))" + "# Copy the content of per_store_weekly (indexed by (store)) into each (store, product).\n", + "f = per_product_weekly / per_store_weekly.propagate(sampling=per_product_weekly, resample=True)" ] }, { @@ -1583,7 +1312,7 @@ }, "source": [ "\n", - "The `EventSet.propagate()` operator expands the indexes of its `input` (`e` in this case) to match the indexes of its `sampling` by copying the content of `input` into each corresponding index group of `sampling`. Note that `sampling`'s indexes must be a superset of `input`'s indexes.\n", + "The `EventSet.propagate()` operator expands the indexes of its input to match the indexes of its `sampling` by copying the contents of the former into each corresponding index group of `sampling`. Note that `sampling`'s indexes must be a superset of `input`'s indexes.\n", "\n", "## Future leakage\n", "\n", @@ -1600,19 +1329,12 @@ "cell_type": "code", "execution_count": null, "id": "c07453fa", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.179407Z", - "iopub.status.busy": "2023-07-24T15:01:00.179261Z", - "iopub.status.idle": "2023-07-24T15:01:00.182867Z", - "shell.execute_reply": "2023-07-24T15:01:00.182049Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "a = tp.input_node(features=[(\"feature_1\", tp.float32)])\n", - "b = a.moving_count(1)\n", - "c = b.leak(1).moving_count(2)" + "a = tp.event_set(timestamps=[10, 20])\n", + "b = a.leak(1)\n", + "b" ] }, { @@ -1620,43 +1342,29 @@ "id": "d2e37ee5", "metadata": {}, "source": [ - "In this example, `b` does not have a future leak, but `c` does because it depends on `EventSet.leak()`.\n", + "In this example, `b` has a future leakage because it depends on `EventSet.leak()`.\n", "\n", - "To check programmatically if an `EventSetNode` depends on `leak()`, we can use the `tp.has_leak()` function." + "To check programmatically if an `EventSet` depends on `leak()`, we can use the `tp.has_leak()` function." ] }, { "cell_type": "code", "execution_count": null, "id": "ab15c133", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.185286Z", - "iopub.status.busy": "2023-07-24T15:01:00.185178Z", - "iopub.status.idle": "2023-07-24T15:01:00.187923Z", - "shell.execute_reply": "2023-07-24T15:01:00.187503Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "print(tp.has_leak(b))" + "print(tp.has_leak(a.node()))" ] }, { "cell_type": "code", "execution_count": null, "id": "cd455fc1", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.190006Z", - "iopub.status.busy": "2023-07-24T15:01:00.189857Z", - "iopub.status.idle": "2023-07-24T15:01:00.192310Z", - "shell.execute_reply": "2023-07-24T15:01:00.191954Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "print(tp.has_leak(c))" + "print(tp.has_leak(b.node()))" ] }, { @@ -1768,14 +1476,7 @@ "cell_type": "code", "execution_count": null, "id": "da239494", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.200932Z", - "iopub.status.busy": "2023-07-24T15:01:00.200760Z", - "iopub.status.idle": "2023-07-24T15:01:00.212641Z", - "shell.execute_reply": "2023-07-24T15:01:00.212298Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "df = pd.DataFrame({\n", @@ -1795,39 +1496,30 @@ "attachments": {}, "cell_type": "markdown", "id": "64cb7ff2-945d-461d-ba4b-b5ac1e127f85", - "metadata": { - "jp-MarkdownHeadingCollapsed": true - }, + "metadata": {}, "source": [ "## Eager mode vs Graph mode\n", "\n", - "Temporian has two execution modes: **eager** and **graph**. In eager mode, operators are applied immediately. This mode is useful for learning Temporian, for iterative and interactive development, and for lightweight/small data use cases where performance isn't a priority.\n", + "Temporian has two execution modes: **eager** and **graph**. In eager mode (default), operators are applied immediately. This mode is useful for learning Temporian, for iterative and interactive development, and for lightweight/small data use cases where performance isn't a priority.\n", "\n", "In graph mode, operators are combined together into \"Temporian programs\" before being executed. Graph mode is more efficient and it consumes less memory. Temporian programs can be saved, inspected, and distributed by users.\n", "\n", "Migrating a Temporian program from eager to graph mode is easy and requires little work. Most of the time, adding a `@tp.compile` annotation is enough. Therefore, it is recommended to develop programs in eager mode and then to productize them in graph mode.\n", "\n", - "Next, we see a the same program written three times: First, in eager mode, then in graph mode using `@tp.compile`, and finally in graph mode without `@tp.compile`." + "Below, we see the same program written three times: First, in eager mode, then in graph mode using `@tp.compile`, and finally in graph mode without `@tp.compile`." ] }, { "cell_type": "code", "execution_count": null, "id": "d5405837-c801-40e9-8b41-4d4dc901d429", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.315244Z", - "iopub.status.busy": "2023-07-24T15:00:57.315083Z", - "iopub.status.idle": "2023-07-24T15:00:57.417894Z", - "shell.execute_reply": "2023-07-24T15:00:57.417481Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Eager mode\n", "#\n", "# Note: This Temporian program contains three operators: two \"simple_moving_average\" and one \"tp.substract\" operators.\n", - "result = evset.simple_moving_average(window_length=0.5) - evset.simple_moving_average(window_length=1.0)\n", + "result = evset['f1'].simple_moving_average(window_length=2) - evset['f1'].simple_moving_average(window_length=4)\n", "result.plot()" ] }, @@ -1835,21 +1527,17 @@ "cell_type": "code", "execution_count": null, "id": "9c854408-0804-4435-8d44-f521ef1b379e", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.420139Z", - "iopub.status.busy": "2023-07-24T15:00:57.419972Z", - "iopub.status.idle": "2023-07-24T15:00:57.522730Z", - "shell.execute_reply": "2023-07-24T15:00:57.522286Z" - } - }, + "metadata": {}, "outputs": [], "source": [ "# Graph mode with @tp.compile\n", "\n", "@tp.compile\n", "def my_function(x):\n", - " return x.simple_moving_average(window_length=0.5) - x.simple_moving_average(window_length=1.0)\n", + " f1 = x['f1']\n", + " sma_2 = f1.simple_moving_average(window_length=2)\n", + " sma_4 = f1.simple_moving_average(window_length=4)\n", + " return sma_2 - sma_4\n", "\n", "result = my_function(evset)\n", " \n", @@ -1860,22 +1548,21 @@ "cell_type": "code", "execution_count": null, "id": "e4aa008f-aa8e-486f-a96c-854fb5b2bd83", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:00:57.525125Z", - "iopub.status.busy": "2023-07-24T15:00:57.524948Z", - "iopub.status.idle": "2023-07-24T15:00:57.629039Z", - "shell.execute_reply": "2023-07-24T15:00:57.628643Z" - } - }, + "metadata": {}, "outputs": [], "source": [ - "# Graph model without @tp.compile\n", + "# Graph mode without @tp.compile\n", "\n", - "input_node = tp.input_node([(\"value\", tp.float64)])\n", - "# Or input_node = tp.input_node(evset.schema.features)\n", + "# Input can be specified before having the actual data\n", + "# input_node = tp.input_node([(\"f1\", tp.float64), (\"f2\", tp.str_)])\n", + "\n", + "# Since we already have the evset, create a proper node for it\n", + "input_node = evset.node()\n", "\n", - "result_node = input_node.simple_moving_average(window_length=0.5) - input_node.simple_moving_average(window_length=1.0)\n", + "f1_node = input_node['f1']\n", + "sma_2_node = f1_node.simple_moving_average(window_length=2)\n", + "sma_4_node = f1_node.simple_moving_average(window_length=4)\n", + "result_node = sma_2_node - sma_4_node\n", "result = tp.run(result_node, {input_node: evset}, verbose=1)\n", " \n", "result.plot()" From fdc78d67b93abf656628f928a4c741c95eb058bf Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Braulio=20R=C3=ADos?= Date: Thu, 26 Oct 2023 18:42:31 -0300 Subject: [PATCH 3/9] Remove 3_minutes, merged into getting_started --- .github/workflows/test_notebooks.yaml | 2 +- README.md | 6 +- docs/mkdocs.yml | 2 +- docs/src/3_minutes.md | 69 ----- .../src/{tutorials => }/getting_started.ipynb | 260 ++++++++++++++++-- docs/src/tutorials/index.md | 3 +- docs/src/tutorials/temporian_with_beam.ipynb | 2 +- 7 files changed, 242 insertions(+), 102 deletions(-) delete mode 100644 docs/src/3_minutes.md rename docs/src/{tutorials => }/getting_started.ipynb (57%) diff --git a/.github/workflows/test_notebooks.yaml b/.github/workflows/test_notebooks.yaml index a22ca7efd..449d1c8fd 100644 --- a/.github/workflows/test_notebooks.yaml +++ b/.github/workflows/test_notebooks.yaml @@ -11,11 +11,11 @@ jobs: # * can be used and all .ipynb files in that dir will be tested sequentially path: - user_guide + - getting_started - recipes/* - tutorials/anomaly_detection_supervised - tutorials/anomaly_detection_unsupervised - tutorials/bank_fraud_detection_with_tfdf - - tutorials/getting_started - tutorials/heart_rate_analysis - tutorials/loan_outcomes_prediction - tutorials/m5_competition diff --git a/README.md b/README.md index a81795be4..8e5b86d57 100644 --- a/README.md +++ b/README.md @@ -91,17 +91,17 @@ Check the [Getting Started tutorial](https://temporian.readthedocs.io/en/stable/ ## Next steps -New users should refer to the [3 minutes to Temporian](https://temporian.readthedocs.io/en/stable/3_minutes/) page, which provides a +New users should refer to the [Getting Started](https://temporian.readthedocs.io/en/stable/getting_started/) guide, which provides a quick overview of the key concepts and operations of Temporian. -After reading the 3 minute guide, visit the [User Guide](https://temporian.readthedocs.io/en/stable/user_guide/) for a deep dive into +After that, visit the [User Guide](https://temporian.readthedocs.io/en/stable/user_guide/) for a deep dive into the major concepts, operators, conventions, and practices of Temporian. For a hands-on learning experience, work through the [Tutorials](https://temporian.readthedocs.io/en/stable/tutorials/) or refer to the [API reference](https://temporian.readthedocs.io/en/stable/reference/). ## Documentation -The documentation 📚 is available at [temporian.readthedocs.io](https://temporian.readthedocs.io/en/stable/). The [3 minutes to Temporian ⏰️](https://temporian.readthedocs.io/en/stable/3_minutes/) is the best way to start. +The documentation 📚 is available at [temporian.readthedocs.io](https://temporian.readthedocs.io/en/stable/). The [Getting Started guide](https://temporian.readthedocs.io/en/stable/getting_started/) is the best way to start. ## Contributing diff --git a/docs/mkdocs.yml b/docs/mkdocs.yml index 4b367aa99..465485c58 100644 --- a/docs/mkdocs.yml +++ b/docs/mkdocs.yml @@ -53,7 +53,7 @@ extra_css: # Navigation bar nav: - Home: index.md - - 3 minutes to Temporian: 3_minutes.md + - Getting Started: getting_started.ipynb - User Guide: user_guide.ipynb - Recipes: recipes/ - Tutorials: tutorials/ diff --git a/docs/src/3_minutes.md b/docs/src/3_minutes.md deleted file mode 100644 index ad6ba7897..000000000 --- a/docs/src/3_minutes.md +++ /dev/null @@ -1,69 +0,0 @@ -# 3 minutes to Temporian - -This is a _very_ quick introduction to how Temporian works. For a complete tour of its capabilities, please refer to the [User Guide](../user_guide). - -## Events and EventSets - -The most basic unit of data in Temporian is an **event**. An event consists of a timestamp and a set of feature values. - -Events are not handled individually. Instead, events are grouped together into an **[`EventSet`][temporian.EventSet]**. - -[`EventSets`][temporian.EventSet] are the main data structure in Temporian, and represent **[multivariate and multi-index time sequences](../user_guide/#what-is-temporal-data)**. Let's break that down: - -- "multivariate" indicates that each event in the time sequence holds several feature values. -- "multi-index" indicates that the events can represent hierarchical data, and be therefore grouped by one or more of their features' values. -- "sequence" indicates that the events are not necessarily sampled at a uniform rate (in which case we would call it a time "series"). - -You can create an [`EventSet`][temporian.EventSet] from a pandas DataFrame, NumPy arrays, CSV files, and more. Here is an example of an [`EventSet`][temporian.EventSet] containing four events and three features: - -```python ->>> evset = tp.event_set( -... timestamps=["2023-02-04", "2023-02-06", "2023-02-07", "2023-02-07"], -... features={ -... "feature_1": [0.5, 0.6, np.nan, 0.9], -... "feature_2": ["red", "blue", "red", "blue"], -... "feature_3": [10.0, -1.0, 5.0, 5.0], -... }, -... indexes=["feature_2"], -... ) - -``` - -An [`EventSet`][temporian.EventSet] can hold one or several time sequences, depending on what its **[index](../user_guide/#index-horizontal-and-vertical-operators)** is. - -If the [`EventSet`][temporian.EventSet] has no index, it will hold a single multivariate time sequence, which means that all events will be considered part of the same group and will interact with each other when operators are applied to the [`EventSet`][temporian.EventSet]. - -If the [`EventSet`][temporian.EventSet] has one (or many) indexes, its events will be grouped by their indexes' values, so it will hold one multivariate time sequence for each unique value (or unique combination of values) of its indexes, and most operators applied to the [`EventSet`][temporian.EventSet] will be applied to each time sequence independently. - -## Operators - -Processing operations are performed by **operators**. For instance, the [`EventSet.simple_moving_average()`][temporian.EventSet.simple_moving_average] operator computes the [simple moving average](https://en.wikipedia.org/wiki/Moving_average) of each feature in an [`EventSet`][temporian.EventSet]. - -The list of all available operators is available in the [API Reference](../reference). - -```python ->>> # Compute the 2-day simple moving average of the EventSet defined above ->>> sma = evset.simple_moving_average(window_length=tp.duration.days(2)) - ->>> # Remove index to get a flat EventSet ->>> reindexed = sma.drop_index() - ->>> # Subtract feature_1 from feature_3 ->>> sub = reindexed["feature_3"] - reindexed["feature_1"] - ->>> # Plot the resulting EventSet ->>> sub.plot() - -``` - -## Graph mode - -Temporian works in **eager mode** out of the box, which means that when you call an operator on an [`EventSet`][temporian.EventSet] you get back the result of that operation immediately as a new [`EventSet`][temporian.EventSet]. - -Eager execution is easy to grasp, and fits most small data use cases. However, for big data, **graph mode** allows Temporian to perform optimizations on the computation graph that is defined when operators are applied on [`EventSets`][temporian.EventSet]. Graph mode also enables the serialization of Temporian programs, for later use in other platforms or distributed compute environments. - -To learn how graph mode works, check out **[Eager mode vs Graph mode](./user_guide.ipynb#eager-mode-vs-graph-mode)** in the User Guide. - -🥳 Congratulations! You're all set to write your first pieces of Temporian code. - -For a more in-depth look at Temporian's capabilities, please check out the [User Guide](../user_guide) or some of the use cases in the [Tutorials](../tutorials) section. diff --git a/docs/src/tutorials/getting_started.ipynb b/docs/src/getting_started.ipynb similarity index 57% rename from docs/src/tutorials/getting_started.ipynb rename to docs/src/getting_started.ipynb index f5bafb2a8..231af61d3 100644 --- a/docs/src/tutorials/getting_started.ipynb +++ b/docs/src/getting_started.ipynb @@ -8,7 +8,17 @@ "source": [ "# Getting Started with Temporian\n", "\n", - "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/temporian/blob/last-release/docs/src/tutorials/getting_started.ipynb)" + "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/temporian/blob/last-release/docs/src/tutorials/getting_started.ipynb)\n", + "\n", + "This guide will introduce you to the basics of Temporian, including:\n", + "- What is an **EventSet** and how to create one from scratch.\n", + "- Visualizing input/output data using **EventSet.plot()** and interactive plots.\n", + "- Converting back and forth between EventSets and pandas **DataFrames**.\n", + "- Transforming the EventSets by using **operators**.\n", + "- How operators work when using **indexes**.\n", + "- Commonly used operations like **glue**, **resample**, **lag**, moving windows and arithmetics.\n", + "\n", + "If you're interested in a topic that is not included here, we provide links to other parts of the documentation on the final section, to continue learning." ] }, { @@ -40,7 +50,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "c71dc843", "metadata": {}, "outputs": [], @@ -51,13 +61,221 @@ "import numpy as np" ] }, + { + "cell_type": "markdown", + "id": "5e9bf8e3-07fd-4e4d-bdb8-d32a48a366c7", + "metadata": {}, + "source": [ + "## Part 1: Events and EventSets\n", + "\n", + "The most basic unit of data in Temporian is an **event**. An event consists of a timestamp and a set of feature values.\n", + "\n", + "Events are not handled individually. Instead, events are grouped together into an **[`EventSet`][temporian.EventSet]**.\n", + "\n", + "[`EventSets`][temporian.EventSet] are the main data structure in Temporian, and represent **[multivariate and multi-index time sequences](../user_guide/#what-is-temporal-data)**. Let's break that down:\n", + "\n", + "- \"multivariate\" indicates that each event in the time sequence holds several feature values.\n", + "- \"multi-index\" indicates that the events can represent hierarchical data, and be therefore grouped by one or more of their features' values.\n", + "- \"sequence\" indicates that the events are not necessarily sampled at a uniform rate (in which case we would call it a time \"series\").\n", + "\n", + "You can create an [`EventSet`][temporian.EventSet] from a pandas DataFrame, NumPy arrays, CSV files, and more. Here is an example of an [`EventSet`][temporian.EventSet] containing four events and three features (one of which is used as an `index`):" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "46ebad7f-f4b4-4850-bc12-8552e55a3f6b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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In the third part we'll learn how to use them and why they can be useful." ] }, { @@ -347,7 +562,7 @@ "id": "de46f604-8d28-4d56-a83d-a13f1073b6b8", "metadata": {}, "source": [ - "## Part 3: Using an index\n", + "## Part 3: Using indexes\n", "This is the final important concept to get from this introduction.\n", "\n", "Indexes are useful to handle multiple signals in parallel (as mentioned at the top of this notebook).\n", @@ -464,25 +679,18 @@ "\n", "To keep it short and concise, there are interesting concepts that were not mentioned above:\n", "\n", - "- You might as well use **datetimes** to specify the timestamps. Learn more about it on the [**Time Units** section of the User Guide](https://temporian.readthedocs.io/en/latest/user_guide/#time-units). There are many [**calendar operators**](https://temporian.readthedocs.io/en/stable/reference/temporian/operators/calendar/calendar_day_of_month/) available when working with date timestamps.\n", - "- Temporian can handle **non-uniform samplings** just as easily (non-equal distance between event timestamps). Read more about the data representation on the **[User Guide's introduction](https://temporian.readthedocs.io/en/latest/user_guide/)** or check the [**sampling** section](https://temporian.readthedocs.io/en/latest/user_guide/#sampling).\n", - "- Temporian is **strict on the feature types** when applying operations, to avoid potentially silent errors or memory issues. Check the [User Guide's **casting** section](https://temporian.readthedocs.io/en/latest/user_guide/#casting) section to learn how to tackle those cases.\n", - "- We only used moving average here, but there are a bunch of other [**moving window**](https://temporian.readthedocs.io/en/stable/reference/temporian/operators/window/moving_count/) operators, frequently useful for time sequences manipulation.\n", + "- Check the [**Time Units** section of the User Guide](https://temporian.readthedocs.io/en/latest/user_guide/#time-units). There are many [**calendar operators**](https://temporian.readthedocs.io/en/stable/reference/temporian/operators/calendar/calendar_day_of_month/) available when working with datetimes.\n", + "- To combine or operate with events from different sampling sources (potentially non-uniform samplings) check the [**sampling** section of the User Guide](https://temporian.readthedocs.io/en/stable/user_guide/#sampling).\n", + "- Temporian is **strict on the feature data types** when applying operations, to avoid potentially silent errors or memory issues. Check the [User Guide's **casting** section](https://temporian.readthedocs.io/en/latest/user_guide/#casting) section to learn how to tackle those cases.\n", "\n", "### Next Steps\n", - "- The [**Recipes**](https://temporian.readthedocs.io/en/latest/recipes/) are short and self-contained examples showing how to use Temporian in typical use cases.\n", - "- Try the more advanced [**tutorials**](https://temporian.readthedocs.io/en/latest/tutorials/) to continue learning by example about all these topics and more!\n", + "- The [**Recipes**](https://temporian.readthedocs.io/en/stable/recipes/) are short and self-contained examples showing how to use Temporian in typical use cases.\n", + "- Try the more advanced [**tutorials**](https://temporian.readthedocs.io/en/stable/tutorials/) to continue learning by example about all these topics and more!\n", + "- Learn how Temporian is **ready for production**, using [**graph mode**](https://temporian.readthedocs.io/en/stable/user_guide/#eager-mode-vs-graph-mode) or [Apache Beam](https://temporian.readthedocs.io/en/stable/tutorials/temporian_with_beam/).\n", + "\n", "- We could only cover a small fraction of **[all available operators](https://temporian.readthedocs.io/en/stable/reference/temporian/operators/add_index/)**.\n", - "- We put a lot of ❤️ in the **[User Guide](https://temporian.readthedocs.io/en/latest/user_guide/)**, so make sure to check it out 🙂." + "- We put a lot of ❤️ in the **[User Guide](https://temporian.readthedocs.io/en/stable/user_guide/)**, so make sure to check it out 🙂." ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "73b77f3e-dcdf-4be0-9c15-c625cb4e297e", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { @@ -501,7 +709,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.4" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/docs/src/tutorials/index.md b/docs/src/tutorials/index.md index f1aa7839b..2c68a6aee 100644 --- a/docs/src/tutorials/index.md +++ b/docs/src/tutorials/index.md @@ -2,7 +2,8 @@ A collection of tutorials that will help you see how Temporian can be used in practice in a wide range of scenarios. -- [Getting Started](getting_started.ipynb): Basic usage of the library, including the concept of index. +Make sure to check the [Getting Started guide](../getting_started.ipynb) before to learn about the basic usage of the library, including the concepts of sampling and indexes. + - [Heart rate analysis](heart_rate_analysis.ipynb): Load, visualize and preprocess medical data to detect heartbeats and compute heart rate and heart rate variability from raw ECG signals. - [Loan outcomes prediction](loan_outcomes_prediction.ipynb): Use Temporian to prepare data to predict outcomes for finished loans. - [M5 Competition](m5_competition.ipynb): Feature engineering and model training on the M5 Makridakis Forecasting Competitions. diff --git a/docs/src/tutorials/temporian_with_beam.ipynb b/docs/src/tutorials/temporian_with_beam.ipynb index 107648aa2..6976bfaff 100644 --- a/docs/src/tutorials/temporian_with_beam.ipynb +++ b/docs/src/tutorials/temporian_with_beam.ipynb @@ -11,7 +11,7 @@ "\n", "**WARNING:** Temporian with Apache Beam is experimental. The API might change, some optimizations are not implemented, and some operators are not available.\n", "\n", - "The reader is assumed to be familiar with Temporian in-process execution. Please read the [3 Minutes to Temporian](../3_minutes), or the [User Guide](../user_guide) before.\n" + "The reader is assumed to be familiar with Temporian in-process execution. Please read the [Getting Started](../getting_started) or the [User Guide](../user_guide) before.\n" ] }, { From a2e00ae5158ffdc3a250d1635e24d01d8b0a37d2 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Braulio=20R=C3=ADos?= Date: Thu, 26 Oct 2023 19:06:25 -0300 Subject: [PATCH 4/9] Fixes in user_guide --- docs/src/user_guide.ipynb | 2369 ++++++++++++++++++++++++++++++++++--- 1 file changed, 2214 insertions(+), 155 deletions(-) diff --git a/docs/src/user_guide.ipynb b/docs/src/user_guide.ipynb index 29a7e7054..b10f4cccd 100644 --- a/docs/src/user_guide.ipynb +++ b/docs/src/user_guide.ipynb @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1d23b4c0", "metadata": {}, "outputs": [], @@ -106,10 +106,154 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "17c712f6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "evset.plot()" ] @@ -169,10 +324,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "f0c059c3-1412-47d5-8f21-8c034f16a551", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", 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\n", + "
\n" + ], + "text/plain": [ + "indexes: []\n", + "features: [('count', int32)]\n", + "events:\n", + " (3 events):\n", + " timestamps: ['2023-03-13T00:00:00' '2023-03-14T00:00:00' '2023-03-15T00:00:00']\n", + " 'count': [1 2 2]\n", + "memory usage: 0.5 kB" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "a = tp.event_set(timestamps=[\"2023-03-13\", \"2023-03-14\", \"2023-03-15\"])\n", "\n", @@ -331,10 +842,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "cbf42f42", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "evset = tp.event_set(\n", "\ttimestamps=[1, 2, 3, 4, 5],\n", @@ -365,10 +887,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "82552e04", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "figure = evset.plot(\n", " style=\"marker\",\n", @@ -394,10 +927,351 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "37feddd0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n", + "\u001b[0m" + ] + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " const force = true;\n", + "\n", + " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", + " root._bokeh_onload_callbacks = [];\n", + " root._bokeh_is_loading = undefined;\n", + " }\n", + "\n", + "const JS_MIME_TYPE = 'application/javascript';\n", + " const HTML_MIME_TYPE = 'text/html';\n", + " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " const CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " const script = document.createElement(\"script\");\n", + " node.appendChild(script);\n", + " }\n", + "\n", + " /**\n", + " * Handle when an output is cleared or removed\n", + " */\n", + " function handleClearOutput(event, handle) {\n", + " const cell = handle.cell;\n", + "\n", + " const id = cell.output_area._bokeh_element_id;\n", + " const server_id = cell.output_area._bokeh_server_id;\n", + " // Clean up Bokeh references\n", + " if (id != null && id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + "\n", + " if (server_id !== undefined) {\n", + " // Clean up Bokeh references\n", + " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", + " cell.notebook.kernel.execute(cmd_clean, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " const id = msg.content.text.trim();\n", + " if (id in Bokeh.index) {\n", + " Bokeh.index[id].model.document.clear();\n", + " delete Bokeh.index[id];\n", + " }\n", + " }\n", + " }\n", + " });\n", + " // Destroy server and session\n", + " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd_destroy);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " const output_area = handle.output_area;\n", + " const output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", + "\n", + " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", + " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", + " // store reference to embed id on output_area\n", + " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", + " }\n", + " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", + " const bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " const script_attrs = bk_div.children[0].attributes;\n", + " for (let i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", + " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", + " }\n", + " // store reference to server id on output_area\n", + " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", + " }\n", + " }\n", + "\n", + " function register_renderer(events, OutputArea) {\n", + "\n", + " function append_mime(data, metadata, element) {\n", + " // create a DOM node to render to\n", + " const toinsert = this.create_output_subarea(\n", + " metadata,\n", + " CLASS_NAME,\n", + " EXEC_MIME_TYPE\n", + " );\n", + " this.keyboard_manager.register_events(toinsert);\n", + " // Render to node\n", + " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", + " render(props, toinsert[toinsert.length - 1]);\n", + " element.append(toinsert);\n", + " return toinsert\n", + " }\n", + "\n", + " /* Handle when an output is cleared or removed */\n", + " events.on('clear_output.CodeCell', handleClearOutput);\n", + " events.on('delete.Cell', handleClearOutput);\n", + "\n", + " /* Handle when a new output is added */\n", + " events.on('output_added.OutputArea', handleAddOutput);\n", + "\n", + " /**\n", + " * Register the mime type and append_mime function with output_area\n", + " */\n", + " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", + " /* Is output safe? */\n", + " safe: true,\n", + " /* Index of renderer in `output_area.display_order` */\n", + " index: 0\n", + " });\n", + " }\n", + "\n", + " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", + " if (root.Jupyter !== undefined) {\n", + " const events = require('base/js/events');\n", + " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", + "\n", + " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", + " register_renderer(events, OutputArea);\n", + " }\n", + " }\n", + " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", + " root._bokeh_timeout = Date.now() + 5000;\n", + " root._bokeh_failed_load = false;\n", + " }\n", + "\n", + " const NB_LOAD_WARNING = {'data': {'text/html':\n", + " \"
\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"
    \\n\"+\n", + " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", + " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", + " \"
\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(null);\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " const docs_json = {\"44c6b85f-9b8f-4b65-b24c-9efd2b909138\":{\"version\":\"3.1.1\",\"title\":\"Bokeh 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if (cb_obj == p1_x_range) {\\n const start = p1_x_range.start;\\n const end = p1_x_range.end;\\n \\n p2_x_range.start = start;\\n p2_x_range.end = end;\\n \\n }\\n \\n if (cb_obj == p2_x_range) {\\n const start = p2_x_range.start;\\n const end = p2_x_range.end;\\n \\n p1_x_range.start = start;\\n p1_x_range.end = end;\\n \\n }\\n 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+ " const render_items = [{\"docid\":\"44c6b85f-9b8f-4b65-b24c-9efd2b909138\",\"roots\":{\"p1165\":\"e485e37c-f869-44fc-879c-ab34ea07fbda\"},\"root_ids\":[\"p1165\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1165" + } + }, + "output_type": "display_data" + } + ], "source": [ "!pip install bokeh -q\n", "\n", @@ -421,10 +1295,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "38ff0e20", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['feature_1', 'feature_2']\n" + ] + } + ], "source": [ "evset = tp.event_set(\n", "\ttimestamps=[0],\n", @@ -450,10 +1332,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "838449d4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['feature_1', 'feature_2']" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "evset.moving_sum(window_length=10).schema.feature_names()" ] @@ -473,10 +1366,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "b10fdec3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "['mult_feature_1_feature_2']" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "(evset[\"feature_1\"] * evset[\"feature_2\"]).schema.feature_names()" ] @@ -497,10 +1401,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "4182d3f8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['calendar_month']\n" + ] + } + ], "source": [ "date_events = tp.event_set(\n", "\ttimestamps=[\"2020-02-15\", \"2020-06-20\"],\n", @@ -524,10 +1436,87 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "bc36bc1f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "daily_sales = tp.event_set(\n", "\ttimestamps=[\"2020-01-01\", \"2020-01-01\", \"2020-01-02\", \"2020-01-02\"],\n", @@ -1140,7 +2744,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "2c87a680", "metadata": { "lines_to_next_cell": 2 @@ -1179,7 +2783,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "8badbebf", "metadata": {}, "outputs": [], @@ -1210,12 +2814,176 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "479096c3", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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\n" + ], + "text/plain": [ + "indexes: [('product', int64), ('store', int64)]\n", + "features: [('sale', float64)]\n", + "events:\n", + " product=1 store=1 (2 events):\n", + " timestamps: ['2020-01-01T00:00:00' '2020-01-02T00:00:00']\n", + " 'sale': [100. 110.]\n", + " product=2 store=1 (1 events):\n", + " timestamps: ['2020-01-01T00:00:00']\n", + " 'sale': [200.]\n", + " product=2 store=2 (1 events):\n", + " timestamps: ['2020-01-02T00:00:00']\n", + " 'sale': [300.]\n", + "memory usage: 1.2 kB" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "sales_per_product_store = daily_sales.add_index([\"product\", \"store\"])\n", "sales_per_product_store" @@ -1237,7 +3005,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "id": "4df0d8cf", "metadata": { "lines_to_next_cell": 0 @@ -1263,7 +3031,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "8d9448a2", "metadata": { "lines_to_next_cell": 0 @@ -1271,7 +3039,7 @@ "outputs": [], "source": [ "# Weekly sales by store (including all products)\n", - "per_store_weekly = per_week.drop_index(\"product\", keep=False).moving_sum(tp.duration.weeks(1))" + "per_store_weekly = per_product_weekly.drop_index(\"product\", keep=False).moving_sum(tp.duration.weeks(1))" ] }, { @@ -1291,7 +3059,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "4e12a9e0", "metadata": { "lines_to_next_cell": 0 @@ -1327,10 +3095,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "c07453fa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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\n" + ], + "text/plain": [ + "indexes: []\n", + "features: []\n", + "events:\n", + " (2 events):\n", + " timestamps: [ 9. 19.]\n", + " \n", + "memory usage: 416 B" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "a = tp.event_set(timestamps=[10, 20])\n", "b = a.leak(1)\n", @@ -1349,20 +3190,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "ab15c133", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], "source": [ "print(tp.has_leak(a.node()))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "cd455fc1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n" + ] + } + ], "source": [ "print(tp.has_leak(b.node()))" ] @@ -1385,17 +3242,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "7da63117", - "metadata": { - "execution": { - "iopub.execute_input": "2023-07-24T15:01:00.194430Z", - "iopub.status.busy": "2023-07-24T15:01:00.194277Z", - "iopub.status.idle": "2023-07-24T15:01:00.198829Z", - "shell.execute_reply": "2023-07-24T15:01:00.198424Z" + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "IndexData(features=[array([0.1, 0.2, 0.3])], timestamps=array([1., 2., 3.]))" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" } - }, - "outputs": [], + ], "source": [ "evset = tp.event_set(\n", "\ttimestamps=[1, 2, 3, 5, 6],\n", @@ -1474,10 +3335,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "da239494", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "WARNING:root:Feature \"f2\" is an array of numpy.object_ and will be casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n" + ] + } + ], "source": [ "df = pd.DataFrame({\n", " \"timestamp\": [1, 2, 3, 5, 6],\n", @@ -1511,10 +3380,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "d5405837-c801-40e9-8b41-4d4dc901d429", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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7sKtFz9RfmW+++QY9e/a0egxphdrwUhteasNLbTipCy+14aU2XJqaDEREOAA0b3Px+9mYXbuaPqf+gkst6IHzF9QT+9FPGnmpDS+14aU2vNSGk7rwUhteasPl4oX7xW1YF/Rt0eZF/eW08Yl/CRFnzpyxegQJQG14qQ0vteGlNpzUhZfa8FIbXnZr0+6Leocj9H/SIS3Fx8dbPYIEoDa81IaX2vBSG07qwktteKkNL7u1afdFvdjTyZMnrR5BAlAbXmrDS214qQ0ndeGlNrzUhpfd2ujwezGlrq7O6hEkALXhpTa81IaX2nBSF15qw0tteNmtjemXtLtYbW0t9u3bB4fDgYyMDHTt2tW/7bXXXmuv2YRIQkKC1SNIAGrDS214qQ0vteGkLrzUhpfa8LJbmzY/U//hhx8iOTkZDz/8MObOnYsBAwZg+/bt/u3p6entOqBwqKiosHoECUBteKkNL7XhpTac1IWX2vBSG152a9PmZ+ofeeQRbNmyBddffz0A4LPPPsP999+PgwcPtvtwwsPn81k9ggSgNrzUhpfa8FIbTurCS214qQ0vu7Vp8zP1ERER/gU9AIwaNQodOnRo16GET3JystUjSABqw0tteKkNL7XhpC681IaX2vCyW5s2L+onTpyI1157DYZhwDAMrF69GhMnTrwaswkRt9tt9QgSgNrwUhteasNLbTipCy+14aU2vOzWxvTh99deey0cDgcMw0B1dTXy8/MBAA0NDejRoweef/75qzakWK+xsdHqESQAteGlNrzUhpfacFIXXmrDS2142a2NwzD5GnSlpaWX3B4qVxD0er2Ijo5GdXU1nE6n1eOEjPr6ekRFRVk9hrRCbXipDS+14aU2nNSFl9rwUhteodLG7NrV9OH3CQkJl7yJvR07dszqESQAteGlNrzUhpfacFIXXmrDS2142a1Nm69+n5SUBIfD0eL9dvuLkeaampqsHkECUBteasNLbXipDSd14aU2vNSGl93amD78/oKioiL/7+vr6/HGG2+gV69eWLBggan7HzlyBDNnzsTJkycRHR2N1157DUOHDm22T0lJCXJzc7Fv3z4kJSVh//79/m3/+te/cOutt2LQoEH+933yySfo3Lmzqc+vw++vTENDAzp16mT1GNIKteGlNrzUhpfacFIXXmrDS214hUqbdj/8/oKhQ4f6b8OHD8dLL72Ed9991/T98/PzMXv2bBw+fBjz5s1Dbm5ui32cTieeffZZrF27ttWPMWjQIOzfv99/M7uglyt36NAhq0eQANSGl9rwUhteasNJXXipDS+14WW3Nm1e1P9fp06dQkVFhal9q6qqsGfPHuTk5AAApk6dCo/Hg+Li4mb79ezZE2PHjkXXrl2/73giIiIiIiIittXmc+ozMzP959Q3NjbC7XbjV7/6lan7ejwexMbGomPH85/W4XDA5XLB7XZj4MCBpmc4evQosrKy0KFDB9x333148MEHA+7r8/ng8/n8b3u9XtOfR/7r4tMdhIva8FIbXmrDS204qQsvteGlNrzs1qbNi/qXXnrJ//sOHToAAMaOHdtuA11OVlYWTpw4gejoaJw4cQK33XYbevfujXvuuafV/RcvXoyFCxcGbT67OnToEIYNG2b1GNIKteGlNrzUhpfacFIXXmrDS2142a1Nmw+/X7x4MdLT0zF8+HDk5OQgJycHTz31lKn7xsfHo7y8HI2NjQAAwzDgdrvhcrlMf36n04no6GgAQFxcHH7605+isLAw4P7z589HdXW1/+bxeEx/LhERERERERFmbV7UV1ZWokePHnjnnXcwZcoUHD58GJs2bTJ13759+yIrKwtr1qwBAGzYsAFxcXFtOvS+vLzc/xIENTU12Lp1KzIzMwPuHxkZCafT2ewmbWe3Q1TsRG14qQ0vteGlNpzUhZfa8FIbXnZr0+ZFfUNDAwBg586dmDBhAq655hr/OfJmFBQUoKCgAKmpqViyZAlWrVoFAMjLy8OWLVsAAGfPnkVcXBymTZuGL7/8EnFxcZg/fz6A8z8IuO6665Ceno7s7GxMmDAB9913X1v/GNJGdrtCpJ2oDS+14aU2vNSGk7rwUhteasPLbm3afE79sGHDcOutt+Krr77C888/j7Nnz7bp/oMGDcInn3zS4v2vvvqq//ddunTBiRMnWr3/3LlzMXfu3LYNLd9bRMT3fqEEuUrUhpfa8FIbXmrDSV14qQ0vteFltzYOwzCMttyhvr4e//jHP5Ceno6kpCR8/fXXOHjwICZNmnS1ZmxXXq8X0dHRqK6u1qH4bVBfX4+oqCirx5BWqA0vteGlNrzUhpO68FIbXmrDK1TamF27tvlHFFFRUbjzzjuRlJQEAOjfv3/ILOjlyhUXF1s9ggSgNrzUhpfa8FIbTurCS214qQ0vu7Wx13EHctW05boJElxqw0tteKkNL7XhpC681IaX2vCyWxst6sWUtrzsoASX2vBSG15qw0ttOKkLL7XhpTa87NZGi3ox5dixY1aPIAGoDS+14aU2vNSGk7rwUhteasPLbm20qBdTIiMjrR5BAlAbXmrDS214qQ0ndeGlNrzUhpfd2mhRL6bExMRYPYIEoDa81IaX2vBSG07qwktteKkNL7u10aJeTCktLbV6BAlAbXipDacqbz0WbfkcVd56q0eRVuhxw0ldeKkNL7XhZbc2WtSLKZ07d7Z6BAlAbXipDaeqGh/WHaxGVY3P6lGkFXrccFIXXmrDS2142a2NFvViSu/eva0eQQJQG15qI9J2etxwUhdeasNLbXjZrY0W9WKKx+OxegQJQG14qQ0nw2j+q3DR44aTuvBSG15qw8tubbSoF1O6detm9QgSgNrwUhs+J8/48MzbRQCAZ94uwskzOgSfjR43nNSFl9rwUhtedmujRb2Y4nQ6rR5BAlAbXmrDZduBckxYugPHT9ZiwqBe539dugPbDpRbPZpcRI8bTurCS214qQ0vu7XRol5MKSsrs3oECUBteKkNl0OVNchO7oX3H70RvxjZDe8/eiOyk3vhcGWN1aPJRfS44aQuvNSGl9rwslubjlYPIKHBbj/NshO14aU2XB4Zn4KICAcA4KzTid7dIvGXnOFoatLJ9Uz0uOGkLrzUhpfa8LJbm6A/U3/kyBGMHj0aqampGDlyJIqKilrdb+XKlUhJScGAAQMwa9YsNDQ0mNpmJ1Xeerz4z8MUr6XcpUsXq0eQABjaMH2tMmFoI/91YUEPNG9z8fvFenrccFIXXmrDS2142a1N0Bf1+fn5mD17Ng4fPox58+YhNze3xT7Hjx/HggULUFhYiOLiYlRWVmLFihWX3WY3VTU+LPvwCMVrKVdUVFg9ggTA0Ibpa5UJQxtpndrwUhtO6sJLbXipDS+7tQnqor6qqgp79uxBTk4OAGDq1KnweDwoLi5utt/69esxefJkxMTEwOFwYM6cOVi3bt1lt8nVc+2111o9ggSgNrzUhpfa8FIbTurCS214qQ0vu7UJ6qLe4/EgNjYWHTueP5Xf4XDA5XLB7XY328/tdiMhIcH/dmJion+fS21rjc/ng9frbXYLFUyvpdypUyerR5AAGNowfa0yYWgjrVMbXmrDSV14qQ0vteFltza2v/r94sWLER0d7b/Fx8dbPZIpbK+lXFVVZennl8CsbsP2tcrE6jYSmNrwUhtO6sJLbXipDS+7tQnqoj4+Ph7l5eVobGwEABiGAbfbDZfL1Ww/l8uF0tJS/9slJSX+fS61rTXz589HdXW1/+bxeNrzj3RVXPxayj9Ji6V4LeVevXpZ9rnl0qxsw/i1ykSPG15qw0ttOKkLL7XhpTa87NYmqIv6vn37IisrC2vWrAEAbNiwAXFxcRg4cGCz/aZOnYotW7agoqIChmFg+fLlmDFjxmW3tSYyMhJOp7PZjd3Fr6X8p//J0mspCy19rYqIiIiIWCvor1NfUFCA3NxcLFq0CE6nE6tWrQIA5OXlYfLkyZg8eTKSk5OxcOFCjBkzBgAwbtw45OfnA8Alt5lh/O9Jv8zn1v985A8QEREDNPng9fpwDYDnJqegqcmwbO7S0lJ07drVks8tl2ZlG8avVSZ63PBSG15qw0ldeKkNL7XhFSptLnw/bVzmwlUO43J72MyJEydC5rx6ERERERERCW8ejwdxcXEBt4fdor6pqQllZWXo3r07HA6H1eOEBK/Xi/j4eHg8npA4fSGcqA0vteGlNrzUhpO68FIbXmrDK5TaGIaBmpoa9OvXDxERgc+cD/rh91aLiIi45E85JLBQuSZBOFIbXmrDS214qQ0ndeGlNrzUhleotImOjr7sPrZ/STsRERERERERu9KiXkRERERERCREaVEvlxUZGYmnn34akZGRVo8i/4fa8FIbXmrDS204qQsvteGlNrzs2CbsLpQnIiIiIiIiYhd6pl5EREREREQkRGlRLyIiIiIiIhKitKgXERERERERCVFa1AsA4OGHH0ZiYiIcDgf279/f6j4fffQRRo0ahSFDhmDo0KF4/PHH0dTUFNxBw5CZNk1NTXjssccwZMgQpKWl4eabb0ZxcXFwBw1D9fX1uPPOO5Gamor09HRMmDDhsn/vubm5cDgcOH36dHCGDGMTJ05EWloaMjIycMMNN2Dfvn2t7rdy5UqkpKRgwIABmDVrFhoaGoI8afgx2+bgwYMYN24cBg8ejMGDB2Pjxo1BnjQ8rVq1Cg6HA5s3b26x7eDBg7jxxhvxwx/+EMOGDcPPf/5z1NXVBX/IMHWpNgDw3HPPYciQIcjIyEB2djY+++yz4A4YhhITEzFo0CBkZGQgIyMDb731VsB9DcPALbfcgh49egRvwDDm8/kwd+5cpKSk4LrrrkNOTk6r+9ni+wBDxDCMHTt2GB6Px0hISDD27dvX6j579+41jh49ahiGYdTV1RljxowxVq1aFbwhw5SZNps2bTJGjRplnDt3zjAMw/jd735nTJs2LYhThqe6ujpj27ZtRlNTk2EYhvHKK68YN910U8D9N2zYYOTl5RkAjG+//TY4Q4axi/+ON27caKSlpbXY59ixY0ZsbKxRXl5uNDU1GXfccYfxxz/+MYhThiczbWpra42kpCSjsLDQMAzDaGxsNKqqqoI1Ytg6fvy48aMf/cjIzs42Nm3a1GL74cOHjc8//9wwjPNN7rnnHuPpp58O7pBh6nJt9u3bZ7hcLqOmpsYwDMN44403jJEjRwZ5yvBzqe/P/q8XXnjByMvLM6Kjo6/qTHLeI488YsydO9f/fVp5eXmLfezyfYCeqRcAwI033oi4uLhL7pOZmYnk5GQAQFRUFDIyMlBSUhKE6cKbmTYOhwM+nw/19fUwDANer/ey95HvLyoqCrfddhscDgcAIDs7O+BjorKyEosWLcLSpUuDOGF4u/iZkOrqan+ni61fvx6TJ09GTEwMHA4H5syZg3Xr1gVxyvBkps3atWuRnZ2NsWPHAgA6dOiAPn36BGvEsNTU1IS8vDy88sorAV/qKSUlBWlpaQDONxk5cqS+FwgCM20cDgcaGhpQW1sLADh9+rS+FyBSVFSEzZs344knnrB6lLBQW1uLlStX4ve//73//5iYmJgW+9nl+4COVg8goamiogLr16/H1q1brR5FANxxxx3Yvn07YmJi0L17d/Tv3x87duyweqyws2zZMkyZMqXVbbNmzcLzzz+P7t27B3mq8Hbvvfdi+/btAIB33nmnxXa3242EhAT/24mJiXC73UGbL5xdrs2XX36JyMhI3H777Thx4gTS0tLwwgsvaGF/FS1duhRjxozB8OHDTe1fW1uLV199FYsXL77Kk4mZNunp6Xj00UeRlJSEnj17IjIyEjt37gzilOHr3nvvhWEYGDVqFJYsWdLi36mGhgbMmjULK1euRIcOHSyaMrwcPXoUPXv2xKJFi/DBBx+gc+fOeOaZZzB+/Phm+9nl+wA9Uy9t5vV6cccdd+Dxxx/HiBEjrB5HAOzZswdffPEFvv76a5SVlWH8+PGYM2eO1WOFlUWLFqG4uLjVb25fffVVuFwu3HLLLRZMFt5Wr14Nj8eDZ599FvPmzbN6HLnI5do0Njbigw8+QEFBAfbt24f+/fvjgQcesGDS8PDFF19gw4YN+M1vfmNq/3PnzmH69OmYOHEi7rrrrqs8XXgz2+b48ePYuHEjiouLceLECTz66KOYPn16kKYMXzt37sSBAwewd+9e9O7dGzNnzmyxz8KFC3H33Xdj8ODBFkwYnhobG1FaWoohQ4Zgz549ePnllzF9+nRUVlZaPdpVoUW9tElNTQ0mTZqEKVOm4LHHHrN6HPlfq1ev9l94JSIiAjNnzvQ/AyZX3x/+8Ads3LgR7777Lrp06dJi+/bt2/H3v/8diYmJSExMBACkpaUFvDiYtL8Lj4lTp041e7/L5UJpaan/7ZKSErhcrmCPF9Yu1ebmm29G//794XA4kJOTg08//dSiKe2vsLAQJSUlSElJQWJiIj799FPMnj0bf/nLX1rs29DQgOnTpyM2NhbLli2zYNrwYrbNhg0bcN1116Ffv34AgPvuuw8ff/wxzp07Z8XYYePC/xmdOnXCI488gsLCwhb77NixA6+88goSExMxduxYeL1eJCYm4j//+U+wxw0bLpcLERER+NnPfgbg/GnESUlJOHjwYIv97PB9gBb1YtqZM2cwadIkTJo0yfRP8iU4kpOT8dFHH/n/4966dSuGDRtm8VThYenSpVi3bh3++c9/Brya7ZtvvgmPx4OSkhL/uacHDhxAZmZm8AYNM6dPn0ZZWZn/7c2bN6NXr17o2bNns/2mTp2KLVu2oKKiAoZhYPny5ZgxY0awxw0rZtvcc8892L17N7xeL4Dzh+inp6cHddZw8sADD6C8vNz/71R2djZWrFjR4uiIxsZGzJgxAz179sSKFStavR6CtC+zbZKTk/Hxxx/jzJkzAM5/L5CamoprrrnGirHDQm1tbbNXs1m3bl2r/7cXFhaitLQUJSUl2LVrF5xOJ0pKSnQ60VXUu3dvjB8/Hu+99x6A80eyHD9+vMXREnb5PkDn1AsAID8/H9u2bUNFRQV+/OMfo3v37iguLkZeXh4mT56MyZMnY9myZfjss89QW1vrf1mhadOm4cknn7R4ensz0+ahhx7CV199hfT0dHTq1AkxMTFYvny51aPb3okTJ/DLX/4SycnJuPnmmwEAkZGR+Pe//42nnnoK/fr102kQFqmursa0adNQV1eHiIgI9OnTB1u3boXD4Wj22ElOTsbChQsxZswYAMC4ceOQn59v8fT2ZraNy+XCr3/9a4wePRoRERHo378/VqxYYfX4Yenif8/eeustbNy4EWlpaf7Fy5gxY/CnP/3J4inD08Vt7rrrLuzevRsjRoxAZGQkunbtirVr11o9oq1VVlZi6tSp+O6772AYBpKTk7F69WoAaPbvmVhj+fLluP/++zFv3jxERESgoKAA/fv3t+X3AQ7DMAyrhxARERERERGRttPh9yIiIiIiIiIhSot6ERERERERkRClRb2IiIiIiIhIiNKiXkRERERERCREaVEvIiIiIiIiEqK0qBcREREREREJUVrUi4iIiIiIiIQoLepFREREREREQpQW9SIiIiIiIiIhSot6ERERERERkRClRb2IiIiIiIhIiPr/n9DMo7mF+w8AAAAASUVORK5CYII=", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Graph mode with @tp.compile\n", "\n", @@ -1546,10 +3437,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "e4aa008f-aa8e-486f-a96c-854fb5b2bd83", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Build schedule\n", + "Run 4 operators\n", + " 1 / 4: SELECT [0.00007 s]\n", + " 2 / 4: SIMPLE_MOVING_AVERAGE [0.00008 s]\n", + " 3 / 4: SIMPLE_MOVING_AVERAGE [0.00003 s]\n", + " 4 / 4: SUBTRACTION [0.00005 s]\n", + "Execution in 0.00075 s\n" + ] + }, + { + "data": { + "image/png": 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", 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", 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\n" - ], - "text/plain": [ - "indexes: []\n", - "features: [('calendar_year', int32), ('calendar_hour', int32), ('calendar_second', int32)]\n", - "events:\n", - " (3 events):\n", - " timestamps: ['1970-01-01T00:00:00' '1970-01-01T00:00:01' '1970-01-01T00:00:02']\n", - " 'calendar_year': [1970 1970 1970]\n", - " 'calendar_hour': [0 0 0]\n", - " 'calendar_second': [0 1 2]\n", - "memory usage: 0.8 kB" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "a_evset = tp.event_set(\n", " timestamps=[0, 1, 2],\n", @@ -704,103 +286,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "899fee67-01d0-4680-a223-7b7b144a46ca", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
\n" - ], - "text/plain": [ - "indexes: []\n", - "features: [('count', int32)]\n", - "events:\n", - " (3 events):\n", - " timestamps: ['2023-03-13T00:00:00' '2023-03-14T00:00:00' '2023-03-15T00:00:00']\n", - " 'count': [1 2 2]\n", - "memory usage: 0.5 kB" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "a = tp.event_set(timestamps=[\"2023-03-13\", \"2023-03-14\", \"2023-03-15\"])\n", "\n", @@ -842,21 +331,10 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "cbf42f42", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "evset = tp.event_set(\n", "\ttimestamps=[1, 2, 3, 4, 5],\n", @@ -887,21 +365,10 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "82552e04", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "figure = evset.plot(\n", " style=\"marker\",\n", @@ -927,351 +394,10 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "37feddd0", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[33mWARNING: There was an error checking the latest version of pip.\u001b[0m\u001b[33m\n", - "\u001b[0m" - ] - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function now() {\n", - " return new Date();\n", - " }\n", - "\n", - " const force = true;\n", - "\n", - " if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n", - " root._bokeh_onload_callbacks = [];\n", - " root._bokeh_is_loading = undefined;\n", - " }\n", - "\n", - "const JS_MIME_TYPE = 'application/javascript';\n", - " const HTML_MIME_TYPE = 'text/html';\n", - " const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", - " const CLASS_NAME = 'output_bokeh rendered_html';\n", - "\n", - " /**\n", - " * Render data to the DOM node\n", - " */\n", - " function render(props, node) {\n", - " const script = document.createElement(\"script\");\n", - " node.appendChild(script);\n", - " }\n", - "\n", - " /**\n", - " * Handle when an output is cleared or removed\n", - " */\n", - " function handleClearOutput(event, handle) {\n", - " const cell = handle.cell;\n", - "\n", - " const id = cell.output_area._bokeh_element_id;\n", - " const server_id = cell.output_area._bokeh_server_id;\n", - " // Clean up Bokeh references\n", - " if (id != null && id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - "\n", - " if (server_id !== undefined) {\n", - " // Clean up Bokeh references\n", - " const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n", - " cell.notebook.kernel.execute(cmd_clean, {\n", - " iopub: {\n", - " output: function(msg) {\n", - " const id = msg.content.text.trim();\n", - " if (id in Bokeh.index) {\n", - " Bokeh.index[id].model.document.clear();\n", - " delete Bokeh.index[id];\n", - " }\n", - " }\n", - " }\n", - " });\n", - " // Destroy server and session\n", - " const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", - " cell.notebook.kernel.execute(cmd_destroy);\n", - " }\n", - " }\n", - "\n", - " /**\n", - " * Handle when a new output is added\n", - " */\n", - " function handleAddOutput(event, handle) {\n", - " const output_area = handle.output_area;\n", - " const output = handle.output;\n", - "\n", - " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", - " if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n", - " return\n", - " }\n", - "\n", - " const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n", - "\n", - " if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n", - " toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n", - " // store reference to embed id on output_area\n", - " output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n", - " }\n", - " if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n", - " const bk_div = document.createElement(\"div\");\n", - " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", - " const script_attrs = bk_div.children[0].attributes;\n", - " for (let i = 0; i < script_attrs.length; i++) {\n", - " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n", - " toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n", - " }\n", - " // store reference to server id on output_area\n", - " output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n", - " }\n", - " }\n", - "\n", - " function register_renderer(events, OutputArea) {\n", - "\n", - " function append_mime(data, metadata, element) {\n", - " // create a DOM node to render to\n", - " const toinsert = this.create_output_subarea(\n", - " metadata,\n", - " CLASS_NAME,\n", - " EXEC_MIME_TYPE\n", - " );\n", - " this.keyboard_manager.register_events(toinsert);\n", - " // Render to node\n", - " const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n", - " render(props, toinsert[toinsert.length - 1]);\n", - " element.append(toinsert);\n", - " return toinsert\n", - " }\n", - "\n", - " /* Handle when an output is cleared or removed */\n", - " events.on('clear_output.CodeCell', handleClearOutput);\n", - " events.on('delete.Cell', handleClearOutput);\n", - "\n", - " /* Handle when a new output is added */\n", - " events.on('output_added.OutputArea', handleAddOutput);\n", - "\n", - " /**\n", - " * Register the mime type and append_mime function with output_area\n", - " */\n", - " OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n", - " /* Is output safe? */\n", - " safe: true,\n", - " /* Index of renderer in `output_area.display_order` */\n", - " index: 0\n", - " });\n", - " }\n", - "\n", - " // register the mime type if in Jupyter Notebook environment and previously unregistered\n", - " if (root.Jupyter !== undefined) {\n", - " const events = require('base/js/events');\n", - " const OutputArea = require('notebook/js/outputarea').OutputArea;\n", - "\n", - " if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n", - " register_renderer(events, OutputArea);\n", - " }\n", - " }\n", - " if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n", - " root._bokeh_timeout = Date.now() + 5000;\n", - " root._bokeh_failed_load = false;\n", - " }\n", - "\n", - " const NB_LOAD_WARNING = {'data': {'text/html':\n", - " \"
\\n\"+\n", - " \"

\\n\"+\n", - " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", - " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", - " \"

\\n\"+\n", - " \"
    \\n\"+\n", - " \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n", - " \"
  • use INLINE resources instead, as so:
    • \\n\"+\n", - " \"
\\n\"+\n", - " \"\\n\"+\n", - " \"from bokeh.resources import INLINE\\n\"+\n", - " \"output_notebook(resources=INLINE)\\n\"+\n", - " \"\\n\"+\n", - " \"
\"}};\n", - "\n", - " function display_loaded() {\n", - " const el = document.getElementById(null);\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS is loading...\";\n", - " }\n", - " if (root.Bokeh !== undefined) {\n", - " if (el != null) {\n", - " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", - " }\n", - " } else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(display_loaded, 100)\n", - " }\n", - " }\n", - "\n", - " function run_callbacks() {\n", - " try {\n", - " root._bokeh_onload_callbacks.forEach(function(callback) {\n", - " if (callback != null)\n", - " callback();\n", - " });\n", - " } finally {\n", - " delete root._bokeh_onload_callbacks\n", - " }\n", - " console.debug(\"Bokeh: all callbacks have finished\");\n", - " }\n", - "\n", - " function load_libs(css_urls, js_urls, callback) {\n", - " if (css_urls == null) css_urls = [];\n", - " if (js_urls == null) js_urls = [];\n", - "\n", - " root._bokeh_onload_callbacks.push(callback);\n", - " if (root._bokeh_is_loading > 0) {\n", - " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", - " return null;\n", - " }\n", - " if (js_urls == null || js_urls.length === 0) {\n", - " run_callbacks();\n", - " return null;\n", - " }\n", - " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", - " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", - "\n", - " function on_load() {\n", - " root._bokeh_is_loading--;\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", - " run_callbacks()\n", - " }\n", - " }\n", - "\n", - " function on_error(url) {\n", - " console.error(\"failed to load \" + url);\n", - " }\n", - "\n", - " for (let i = 0; i < css_urls.length; i++) {\n", - " const url = css_urls[i];\n", - " const element = document.createElement(\"link\");\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.rel = \"stylesheet\";\n", - " element.type = \"text/css\";\n", - " element.href = url;\n", - " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " for (let i = 0; i < js_urls.length; i++) {\n", - " const url = js_urls[i];\n", - " const element = document.createElement('script');\n", - " element.onload = on_load;\n", - " element.onerror = on_error.bind(null, url);\n", - " element.async = false;\n", - " element.src = url;\n", - " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", - " document.head.appendChild(element);\n", - " }\n", - " };\n", - "\n", - " function inject_raw_css(css) {\n", - " const element = document.createElement(\"style\");\n", - " element.appendChild(document.createTextNode(css));\n", - " document.body.appendChild(element);\n", - " }\n", - "\n", - " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n", - " const css_urls = [];\n", - "\n", - " const inline_js = [ function(Bokeh) {\n", - " Bokeh.set_log_level(\"info\");\n", - " },\n", - "function(Bokeh) {\n", - " }\n", - " ];\n", - "\n", - " function run_inline_js() {\n", - " if (root.Bokeh !== undefined || force === true) {\n", - " for (let i = 0; i < inline_js.length; i++) {\n", - " inline_js[i].call(root, root.Bokeh);\n", - " }\n", - "} else if (Date.now() < root._bokeh_timeout) {\n", - " setTimeout(run_inline_js, 100);\n", - " } else if (!root._bokeh_failed_load) {\n", - " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", - " root._bokeh_failed_load = true;\n", - " } else if (force !== true) {\n", - " const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n", - " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", - " }\n", - " }\n", - "\n", - " if (root._bokeh_is_loading === 0) {\n", - " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", - " run_inline_js();\n", - " } else {\n", - " load_libs(css_urls, js_urls, function() {\n", - " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", - " run_inline_js();\n", - " });\n", - " }\n", - "}(window));" - ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(null);\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.1.1.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.1.1.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\n} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(null)).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - "
\n" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/javascript": [ - "(function(root) {\n", - " function embed_document(root) {\n", - " const docs_json = {\"44c6b85f-9b8f-4b65-b24c-9efd2b909138\":{\"version\":\"3.1.1\",\"title\":\"Bokeh 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install bokeh -q\n", "\n", @@ -1295,18 +421,10 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "38ff0e20", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['feature_1', 'feature_2']\n" - ] - } - ], + "outputs": [], "source": [ "evset = tp.event_set(\n", "\ttimestamps=[0],\n", @@ -1332,21 +450,10 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "id": "838449d4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['feature_1', 'feature_2']" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "evset.moving_sum(window_length=10).schema.feature_names()" ] @@ -1366,21 +473,10 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "id": "b10fdec3", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "['mult_feature_1_feature_2']" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "(evset[\"feature_1\"] * evset[\"feature_2\"]).schema.feature_names()" ] @@ -1401,18 +497,10 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "4182d3f8", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['calendar_month']\n" - ] - } - ], + "outputs": [], "source": [ "date_events = tp.event_set(\n", "\ttimestamps=[\"2020-02-15\", \"2020-06-20\"],\n", @@ -1436,87 +524,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "id": "bc36bc1f", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "daily_sales = tp.event_set(\n", "\ttimestamps=[\"2020-01-01\", \"2020-01-01\", \"2020-01-02\", \"2020-01-02\"],\n", @@ -2744,7 +1140,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "id": "2c87a680", "metadata": { "lines_to_next_cell": 2 @@ -2783,7 +1179,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "id": "8badbebf", "metadata": {}, "outputs": [], @@ -2814,176 +1210,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "id": "479096c3", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [ - { - "data": { - "text/html": [ - "
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43, + "execution_count": null, "id": "7da63117", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "IndexData(features=[array([0.1, 0.2, 0.3])], timestamps=array([1., 2., 3.]))" - ] - }, - "execution_count": 43, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "evset = tp.event_set(\n", "\ttimestamps=[1, 2, 3, 5, 6],\n", @@ -3335,18 +1467,10 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "da239494", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING:root:Feature \"f2\" is an array of numpy.object_ and will be casted to numpy.string_ (Note: numpy.string_ is equivalent to numpy.bytes_).\n" - ] - } - ], + "outputs": [], "source": [ "df = pd.DataFrame({\n", " \"timestamp\": [1, 2, 3, 5, 6],\n", @@ -3380,21 +1504,10 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "id": "d5405837-c801-40e9-8b41-4d4dc901d429", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Graph mode with @tp.compile\n", "\n", @@ -3437,34 +1539,10 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "id": "e4aa008f-aa8e-486f-a96c-854fb5b2bd83", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "Build schedule\n", - "Run 4 operators\n", - " 1 / 4: SELECT [0.00007 s]\n", - " 2 / 4: SIMPLE_MOVING_AVERAGE [0.00008 s]\n", - " 3 / 4: SIMPLE_MOVING_AVERAGE [0.00003 s]\n", - " 4 / 4: SUBTRACTION [0.00005 s]\n", - "Execution in 0.00075 s\n" - ] - }, - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Graph mode without @tp.compile\n", "\n", @@ -3493,7 +1571,7 @@ "\n", "**Remark:** While you will likely use the graph mode with `@tp.compile` , it is useful for you to understand the graph model without `@tp.compile`.\n", "\n", - "A Temporian program is a graph of [EventSetNodes][temporian.EventSetNode] connecting operators. A graph is executed with the function `tp.run(, )`.\n", + "A Temporian program is a graph of `EventSetNodes` connecting operators. A graph is executed with the function `tp.run(, )`.\n", "\n", "\"eager\n", "\n", @@ -3511,7 +1589,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "id": "dd4142a0-df47-4ca3-aafe-7a105b4f2deb", "metadata": {}, "outputs": [], @@ -3550,7 +1628,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "id": "b0b0220b-9869-454c-8f3b-afeabaca06bc", "metadata": {}, "outputs": [], @@ -3581,7 +1659,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "id": "945d8f39-18ad-44df-a4d2-9d8986e7825b", "metadata": {}, "outputs": [], @@ -3604,19 +1682,10 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "id": "941a5fc4-edfc-4fba-8bc8-22f3a7387e91", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[('a_branch', float64)]\n", - "[('non_a_branch', float64)]\n" - ] - } - ], + "outputs": [], "source": [ "@tp.compile\n", "def my_function(x : tp.types.EventSetOrNode, a:bool) -> tp.types.EventSetOrNode:\n", @@ -3641,95 +1710,10 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "id": "a9ab68f3-91a6-445e-99c2-7f2379185f21", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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\n", - "
\n" - ], - "text/plain": [ - "indexes: []\n", - "features: [('count', int32)]\n", - "events:\n", - " (3 events):\n", - " timestamps: [1. 2. 3.]\n", - " 'count': [1 1 1]\n", - "memory usage: 0.5 kB" - ] - }, - "execution_count": 56, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Define a graph.\n", "evset = tp.event_set(\n", From 13711cd92917f771e3cc64d8e0cf471eb3605401 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Braulio=20R=C3=ADos?= Date: Fri, 27 Oct 2023 15:23:47 -0300 Subject: [PATCH 6/9] Update getting_started path in rtd & git-hook --- .git-hooks/pre-commit | 2 +- docs/.readthedocs.yaml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.git-hooks/pre-commit b/.git-hooks/pre-commit index fc5307923..b161bf8b0 100755 --- a/.git-hooks/pre-commit +++ b/.git-hooks/pre-commit @@ -2,7 +2,7 @@ # files_to_check="docs/src/recipes/*.ipynb" files_to_check+=" docs/src/user_guide.ipynb" -files_to_check+=" docs/src/tutorials/getting_started.ipynb" +files_to_check+=" docs/src/getting_started.ipynb" for path in `git diff --name-only --staged $files_to_check` diff --git a/docs/.readthedocs.yaml b/docs/.readthedocs.yaml index c75ae4b88..16bb1b18c 100644 --- a/docs/.readthedocs.yaml +++ b/docs/.readthedocs.yaml @@ -27,9 +27,9 @@ build: - pip install -r docs/src/tutorials/requirements.txt pre_build: + - tools/run_notebooks.sh docs/src/getting_started.ipynb - tools/run_notebooks.sh docs/src/user_guide.ipynb - tools/run_notebooks.sh $(ls docs/src/recipes/*.ipynb) - - tools/run_notebooks.sh docs/src/tutorials/getting_started.ipynb # These are too slow # - tools/run_notebooks.sh docs/src/tutorials/*.ipynb From 3275d158373aef2c46ebdee77f2f59ce2a446a8b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Braulio=20R=C3=ADos?= Date: Tue, 31 Oct 2023 12:10:37 -0300 Subject: [PATCH 7/9] Symlink in tutorials/getting_started (old url was shared) --- docs/src/tutorials/getting_started.ipynb | 1 + 1 file changed, 1 insertion(+) create mode 120000 docs/src/tutorials/getting_started.ipynb diff --git a/docs/src/tutorials/getting_started.ipynb b/docs/src/tutorials/getting_started.ipynb new file mode 120000 index 000000000..fae9ca7ed --- /dev/null +++ b/docs/src/tutorials/getting_started.ipynb @@ -0,0 +1 @@ +docs/src/getting_started.ipynb \ No newline at end of file From 3d235c8c9ed7ce8c33e4e80cf44e9604cdfbde53 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Braulio=20R=C3=ADos?= Date: Tue, 31 Oct 2023 13:54:59 -0300 Subject: [PATCH 8/9] Address PR comments on getting_started --- docs/src/getting_started.ipynb | 95 +++++++++++++++++++++++----------- docs/src/user_guide.ipynb | 10 ++-- 2 files changed, 70 insertions(+), 35 deletions(-) diff --git a/docs/src/getting_started.ipynb b/docs/src/getting_started.ipynb index b01eebab9..b48013491 100644 --- a/docs/src/getting_started.ipynb +++ b/docs/src/getting_started.ipynb @@ -6,19 +6,23 @@ "id": "b8dd3ccc", "metadata": {}, "source": [ - "# Getting Started with Temporian\n", + "# Getting Started\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/temporian/blob/last-release/docs/src/tutorials/getting_started.ipynb)\n", "\n", - "This guide will introduce you to the basics of Temporian, including:\n", - "- What is an **EventSet** and how to create one from scratch.\n", - "- Visualizing input/output data using **EventSet.plot()** and interactive plots.\n", - "- Converting back and forth between EventSets and pandas **DataFrames**.\n", - "- Transforming the EventSets by using **operators**.\n", - "- How operators work when using **indexes**.\n", - "- Commonly used operations like **glue**, **resample**, **lag**, moving windows and arithmetics.\n", + "Temporian is an open-source Python library for preprocessing and feature engineering temporal data, to get it ready for machine learning applications 🤖.\n", "\n", - "If you're interested in a topic that is not included here, we provide links to other parts of the documentation on the final section, to continue learning." + "This guide will introduce you to the basics of the library, including how to:\n", + "- Create an `EventSet` and use it.\n", + "- Visualize input/output data using `EventSet.plot()` and interactive plots.\n", + "- Convert back and forth between `EventSet` and pandas `DataFrame`.\n", + "- Transform an `EventSet` by using **operators**.\n", + "- Work with `indexes`.\n", + "- Use common operators like `glue`, `resample`, `lag`, moving windows and arithmetics.\n", + "\n", + "If you're interested in a topic that is not included here, we provide links to other parts of the documentation on the final section, to continue learning.\n", + "\n", + "By reading this guide, you will learn how to implement a processing pipeline with Temporian, to get your data ready to train machine learning models by using straightforward operations and avoiding common mistakes." ] }, { @@ -68,17 +72,47 @@ "source": [ "## Part 1: Events and EventSets\n", "\n", - "The most basic unit of data in Temporian is an **event**. An event consists of a timestamp and a set of feature values.\n", + "Events are the basic unit of data in Temporian. They consist of a timestamp and a set of feature values. Events are not handled individually, but are instead grouped together into **[`EventSets`](https://temporian.readthedocs.io/en/stable/user_guide/#events-and-eventsets)**.\n", + "\n", + "The main data structure in Temporian is the **[`EventSet`](https://temporian.readthedocs.io/en/stable/user_guide/#events-and-eventsets)**, and it represents **[multivariate and multi-index time sequences](https://temporian.readthedocs.io/en/stable/user_guide/#what-is-temporal-data)**. Let's break that down:\n", + "\n", + "- **multivariate:** indicates that each event in the time sequence holds several feature values.\n", + "- **multi-index:** indicates that the events can represent hierarchical data, and be therefore grouped by one or more of their features' values.\n", + "- **time sequence:** indicates that the events are not necessarily sampled at a uniform rate (in which case we would call it a *time series*).\n", "\n", - "Events are not handled individually. Instead, events are grouped together into an **`EventSet`**.\n", + "You can create an `EventSet` from a pandas DataFrame, NumPy arrays, CSV files, and more. Here is an example containing only 3 events and 2 features:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9cfb0cb1", + "metadata": {}, + "outputs": [], + "source": [ + "evset = tp.event_set(\n", + " timestamps=[1, 2, 3],\n", + " features={\n", + " \"feature_1\": [10, 20, 30],\n", + " \"feature_2\": [False, False, True],\n", + " },\n", + ")\n", + "evset" + ] + }, + { + "cell_type": "markdown", + "id": "c8267798", + "metadata": {}, + "source": [ + "An `EventSet` can hold one or several time sequences, depending on its index.\n", "\n", - "`EventSets` are the main data structures in Temporian, and represent **[multivariate and multi-index time sequences](../user_guide/#what-is-temporal-data)**. Let's break that down:\n", + "- If it has no index (e.g: above case), an `EventSet` holds a single multivariate time sequence.\n", + "- If it has one (or more) indexes, the events are grouped by their index values. This means that the `EventSet` will hold one multivariate time sequence for each unique value (or unique combination of values) of its indexes.\n", "\n", - "- \"multivariate\" indicates that each event in the time sequence holds several feature values.\n", - "- \"multi-index\" indicates that the events can represent hierarchical data, and be therefore grouped by one or more of their features' values.\n", - "- \"sequence\" indicates that the events are not necessarily sampled at a uniform rate (in which case we would call it a time \"series\").\n", + "Operators are applied on each time sequence of an `EventSet` independently. Indexing is the primary way to handle rich and complex databases. For instance, in a retail database, you can index on customers, stores, products, etc.\n", "\n", - "You can create an `EventSet` from a pandas DataFrame, NumPy arrays, CSV files, and more. Here is an example containing four events and three features (one of which is used as an `index`):" + "The following example will create one sequence for `blue` events, and another one for `red` ones, by specifying that one of the features is an `index`:" ] }, { @@ -88,6 +122,7 @@ "metadata": {}, "outputs": [], "source": [ + "# EventSet with indexes\n", "evset = tp.event_set(\n", " timestamps=[\"2023-02-04\", \"2023-02-06\", \"2023-02-07\", \"2023-02-07\"],\n", " features={\n", @@ -105,12 +140,6 @@ "id": "effc4483-9a1a-4e21-b376-3ed188ced821", "metadata": {}, "source": [ - "An `EventSet` can hold one or several time sequences, depending on what its `index` is.\n", - "\n", - "If it has no index, it will hold a single multivariate time sequence, which means that all events will be considered part of the same group and will interact with each other when operators are applied.\n", - "\n", - "If it has one (or many) indexes, its events will be grouped by their `indexes` values, so it will hold one multivariate time sequence for each unique value (or unique combination of values) of its indexes, and operators will be applied to each time sequence independently.\n", - "\n", "See the last part of this tutorial to see some examples using `indexes` and operators." ] }, @@ -122,9 +151,9 @@ "source": [ "### Example Data\n", "\n", - "This minimal data consists of just one `signal` with a `timestamp` for each sample.\n", + "For the following examples, we will generate some fake data which consists of a `signal` with a `timestamp` for each sample.\n", "\n", - "The signal is a periodic sinusoidal `season` with a slight positive slope in the long run, which we call `trend`. Plus the ubiquitous `noise`." + "The signal is composed of a periodic `season` (sine wave), with a slight positive slope which we call `trend`. Plus the ubiquitous `noise`. We will include all these components as separate features, together with the resulting `signal`." ] }, { @@ -388,7 +417,7 @@ "### Exporting outputs from Temporian\n", "You may need to use this data in different ways for downstream tasks, like training a model using whatever library you need. \n", "\n", - "If you can't use the data directly from Temporian, you can always go back to a pandas DataFrame:" + "If you can't use the data directly from Temporian, you can always go back to a pandas `DataFrame`:" ] }, { @@ -514,11 +543,11 @@ "## Summary\n", "\n", "Congratulations! You now have the basic concepts needed to create a data preprocessing pipeline with Temporian:\n", - "- Defining an **EventSet** and using **operators** on it.\n", - "- Combine **features** using **select** and **glue**.\n", - "- Coverting data back and forth between Temporian's **EventSet** and pandas **DataFrames**.\n", - "- Visualizing input/output data using **EventSet.plot()**.\n", - "- Operating and plotting with an **index**.\n", + "- Defining an `EventSet` and using **operators** on it.\n", + "- Combine features using `select` and `glue`.\n", + "- Converting data back and forth between Temporian's `EventSet` and pandas `DataFrames`.\n", + "- Visualizing input/output data using `EventSet.plot()`.\n", + "- Operating and plotting with `indexes`.\n", "\n", "### Other important details\n", "\n", @@ -536,6 +565,12 @@ "- We could only cover a small fraction of **[all available operators](https://temporian.readthedocs.io/en/stable/reference/temporian/operators/add_index/)**.\n", "- We put a lot of ❤️ in the **[User Guide](https://temporian.readthedocs.io/en/stable/user_guide/)**, so make sure to check it out 🙂." ] + }, + { + "cell_type": "markdown", + "id": "cebffed7", + "metadata": {}, + "source": [] } ], "metadata": { diff --git a/docs/src/user_guide.ipynb b/docs/src/user_guide.ipynb index ca7602e5a..6a018928d 100644 --- a/docs/src/user_guide.ipynb +++ b/docs/src/user_guide.ipynb @@ -8,7 +8,7 @@ "source": [ "# User Guide\n", "\n", - "This is a complete tour of Temporian's capabilities. For a quick hands-on overview, make sure to check the [Getting started guide](./getting_started)." + "This is a complete tour of Temporian's capabilities. For a quick hands-on overview, make sure to check the [Getting started guide](https://temporian.readthedocs.io/en/stable/getting_started)." ] }, { @@ -187,7 +187,7 @@ "result = evset.simple_moving_average(window_length=1)\n", "\n", "# Plot the results\n", - "tp.plot([evset, result]) " + "tp.plot([evset, result])" ] }, { @@ -1533,7 +1533,7 @@ " return sma_2 - sma_4\n", "\n", "result = my_function(evset)\n", - " \n", + "\n", "result.plot()" ] }, @@ -1557,7 +1557,7 @@ "sma_4_node = f1_node.simple_moving_average(window_length=4)\n", "result_node = sma_2_node - sma_4_node\n", "result = tp.run(result_node, {input_node: evset}, verbose=1)\n", - " \n", + "\n", "result.plot()" ] }, @@ -1801,7 +1801,7 @@ " features=a_evset.schema.features,\n", " indexes=a_evset.schema.indexes\n", ")\n", - " \n", + "\n", "a_node = a_evset.node()" ] }, From 6bb9f13494e8e654a2e59e99887f91d36a83117e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Braulio=20R=C3=ADos?= Date: Tue, 31 Oct 2023 15:30:17 -0300 Subject: [PATCH 9/9] Added rtd redirect (admin page), removed symlink tutorials/getting_started --- docs/src/tutorials/getting_started.ipynb | 1 - 1 file changed, 1 deletion(-) delete mode 120000 docs/src/tutorials/getting_started.ipynb diff --git a/docs/src/tutorials/getting_started.ipynb b/docs/src/tutorials/getting_started.ipynb deleted file mode 120000 index fae9ca7ed..000000000 --- a/docs/src/tutorials/getting_started.ipynb +++ /dev/null @@ -1 +0,0 @@ -docs/src/getting_started.ipynb \ No newline at end of file