From f540dc98850f6411794435fbc29014d170e714e5 Mon Sep 17 00:00:00 2001 From: Shuhui Bu Date: Sun, 14 Oct 2018 11:23:01 +0800 Subject: [PATCH] Add dnn tips, rearrange dir structures --- .../README.md | 0 .../bokeh_tutorial.ipynb | 893 ++++++++++++++++++ .../example.png | Bin .../ipython_notebook.ipynb | 0 .../matplotlib_ani.ipynb | 0 .../matplotlib_ani.py | 0 .../matplotlib_full.ipynb | 0 .../matplotlib_simple_tutorial.ipynb | 0 .../matplotlib_simple_tutorial.py | 0 .../numpy_tutorial.ipynb | 0 .../scipy_tutorial.ipynb | 0 .../stockholm_td_adj.dat | 0 .../sympy_tutorial.ipynb | 0 .../utils_git.ipynb | 0 .../utils_git_advanced.ipynb | 0 .../utils_shell.ipynb | 0 {1_knn => 2_knn}/images/knn.png | Bin {1_knn => 2_knn}/knn_classification.ipynb | 0 {1_knn => 2_knn}/knn_classification.py | 0 .../ClusteringAlgorithms.ipynb | 0 .../ClusteringAlgorithms.py | 0 {1_kmeans => 3_kmeans}/README.md | 0 {1_kmeans => 3_kmeans}/download_iris.py | 0 {1_kmeans => 3_kmeans}/images/ARI_ct.png | Bin {1_kmeans 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mode 100644 index 0000000..00fd6b4 --- /dev/null +++ b/1_numpy_matplotlib_scipy_sympy/bokeh_tutorial.ipynb @@ -0,0 +1,893 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "
\n", + "\n", + "# Bokeh 5-minute Overview\n", + "\n", + "Bokeh is a Python interactive visualization library that targets modern web browsers for presentation. Its goal is to provide elegant, concise construction of novel graphics in the style of D3.js, and to extend this capability with high-performance interactivity over very large or streaming datasets. Bokeh can help anyone who would like to quickly and easily create interactive plots, dashboards, and data applications." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Simple Example\n", + "\n", + "Here is a simple first example. First we'll import the [`figure`](https://bokeh.pydata.org/en/latest/docs/reference/plotting.html#bokeh.plotting.figure.figure) function from [`bokeh.plotting`](https://bokeh.pydata.org/en/latest/docs/user_guide/plotting.html), which will let us create all sorts of interesting plots easily. We also import the `show` and `ouptut_notebook` functions from `bokeh.io` — these let us display our results inline in the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from bokeh.plotting import figure \n", + "from bokeh.io import output_notebook, show" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we'll tell Bokeh to display its plots directly into the notebook.\n", + "This will cause all of the Javascript and data to be embedded directly\n", + "into the HTML of the notebook itself.\n", + "(Bokeh can output straight to HTML files, or use a server, which we'll\n", + "look at later.)" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "(function(root) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " var 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", + " var JS_MIME_TYPE = 'application/javascript';\n", + " var HTML_MIME_TYPE = 'text/html';\n", + " var EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n", + " var CLASS_NAME = 'output_bokeh rendered_html';\n", + "\n", + " /**\n", + " * Render data to the DOM node\n", + " */\n", + " function render(props, node) {\n", + " var 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", + " var cell = handle.cell;\n", + "\n", + " var id = cell.output_area._bokeh_element_id;\n", + " var 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", + " var cmd = \"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, {\n", + " iopub: {\n", + " output: function(msg) {\n", + " var 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", + " var cmd = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n", + " cell.notebook.kernel.execute(cmd);\n", + " }\n", + " }\n", + "\n", + " /**\n", + " * Handle when a new output is added\n", + " */\n", + " function handleAddOutput(event, handle) {\n", + " var output_area = handle.output_area;\n", + " var output = handle.output;\n", + "\n", + " // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n", + " if ((output.output_type != \"display_data\") || (!output.data.hasOwnProperty(EXEC_MIME_TYPE))) {\n", + " return\n", + " }\n", + "\n", + " var 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", + " var bk_div = document.createElement(\"div\");\n", + " bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n", + " var script_attrs = bk_div.children[0].attributes;\n", + " for (var i = 0; i < script_attrs.length; i++) {\n", + " toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\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", + " var 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", + " var 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", + " var events = require('base/js/events');\n", + " var 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", + "\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", + " var 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", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " var el = document.getElementById(\"8cd0437f-c78b-4d3f-afb8-1b008f84052d\");\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", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " }\n", + " finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.log(\"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.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };var element = document.getElementById(\"8cd0437f-c78b-4d3f-afb8-1b008f84052d\");\n", + " if (element == null) {\n", + " console.log(\"Bokeh: ERROR: autoload.js configured with elementid '8cd0437f-c78b-4d3f-afb8-1b008f84052d' but no matching script tag was found. \")\n", + " return false;\n", + " }\n", + "\n", + " var js_urls = [\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.js\", \"https://cdn.pydata.org/bokeh/release/bokeh-gl-0.13.0.min.js\"];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " \n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.13.0.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.13.0.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-tables-0.13.0.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " \n", + " if ((root.Bokeh !== undefined) || (force === true)) {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }if (force === true) {\n", + " display_loaded();\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", + " var cell = $(document.getElementById(\"8cd0437f-c78b-4d3f-afb8-1b008f84052d\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + "\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "\n(function(root) {\n function now() {\n return new Date();\n }\n\n var 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\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 var 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 \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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"output_type": "display_data" + } + ], + "source": [ + "p = figure(width=500, height=500)\n", + "p.circle(x, y, size=7, color=\"firebrick\", alpha=0.5)\n", + "show(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bar Plot Example\n", + "\n", + "\n", + "Bokeh's core display model relies on *composing graphical primitives* which are bound to data series. This is similar in spirit to Protovis and D3, and different than most other Python plotting libraries.\n", + "\n", + "A slightly more sophisticated example demonstrates this idea.\n", + "\n", + "Bokeh ships with a small set of interesting \"sample data\" in the `bokeh.sampledata` package. We'll load up some historical automobile mileage data, which is returned as a Pandas `DataFrame`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from bokeh.sampledata.autompg import autompg\n", + "\n", + "grouped = autompg.groupby(\"yr\")\n", + "\n", + "mpg = grouped.mpg\n", + "avg, std = mpg.mean(), mpg.std()\n", + "years = list(grouped.groups)\n", + "american = autompg[autompg[\"origin\"]==1]\n", + "japanese = autompg[autompg[\"origin\"]==3]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For each year, we want to plot the distribution of MPG within that year." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Application\",\"version\":\"0.13.0\"}};\n", + " var render_items = [{\"docid\":\"bad1f4b5-c81b-4fc1-872b-70166b3d66a6\",\"roots\":{\"42a1e000-ac23-4612-97b0-2fded48d2234\":\"92409529-2443-4698-83d8-8ad84ee19e96\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " clearInterval(timer);\n", + " }\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", + " clearInterval(timer);\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "42a1e000-ac23-4612-97b0-2fded48d2234" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p = figure(title=\"MPG by Year (Japan and US)\")\n", + "\n", + "p.vbar(x=years, bottom=avg-std, top=avg+std, width=0.8, \n", + " fill_alpha=0.2, line_color=None, legend=\"MPG 1 stddev\")\n", + "\n", + "p.circle(x=japanese[\"yr\"], y=japanese[\"mpg\"], size=10, alpha=0.5,\n", + " color=\"red\", legend=\"Japanese\")\n", + "\n", + "p.triangle(x=american[\"yr\"], y=american[\"mpg\"], size=10, alpha=0.3,\n", + " color=\"blue\", legend=\"American\")\n", + "\n", + "p.legend.location = \"top_left\"\n", + "show(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**This kind of approach can be used to generate other kinds of interesting plots. See many more examples in the [Bokeh Documentation Gallery](https://bokeh.pydata.org/en/latest/docs/gallery.html). **" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linked Brushing\n", + "\n", + "To link plots together at a data level, we can explicitly wrap the data in a `ColumnDataSource`. This allows us to reference columns by name.\n", + "\n", + "We can use a \"select\" tool to select points on one plot, and the linked points on the other plots will highlight." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "(function(root) {\n", + " function embed_document(root) {\n", + " \n", + " var docs_json = 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Application\",\"version\":\"0.13.0\"}};\n", + " var render_items = [{\"docid\":\"ebe9aadd-28b2-446b-8080-884f2d424dfc\",\"roots\":{\"f45a8a50-7184-4c9f-a979-e5d77e1eecef\":\"e42df904-4250-4f64-a038-aa32c6fccee9\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " clearInterval(timer);\n", + " }\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", + " clearInterval(timer);\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "f45a8a50-7184-4c9f-a979-e5d77e1eecef" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from bokeh.models import ColumnDataSource\n", + "from bokeh.layouts import gridplot\n", + "\n", + "source = ColumnDataSource(autompg)\n", + "\n", + "options = dict(plot_width=300, plot_height=300,\n", + " tools=\"pan,wheel_zoom,box_zoom,box_select,lasso_select\")\n", + "\n", + "p1 = figure(title=\"MPG by Year\", **options)\n", + "p1.circle(\"yr\", \"mpg\", color=\"blue\", source=source)\n", + "\n", + "p2 = figure(title=\"HP vs. Displacement\", **options)\n", + "p2.circle(\"hp\", \"displ\", color=\"green\", source=source)\n", + "\n", + "p3 = figure(title=\"MPG vs. Displacement\", **options)\n", + "p3.circle(\"mpg\", \"displ\", size=\"cyl\", line_color=\"red\", fill_color=None, source=source)\n", + "\n", + "p = gridplot([[ p1, p2, p3]], toolbar_location=\"right\")\n", + "\n", + "show(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can read more about the `ColumnDataSource` and other Bokeh data structures in [Providing Data for Plots and Tables](https://bokeh.pydata.org/en/latest/docs/user_guide/data.html)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Standalone HTML\n", + "\n", + "In addition to working well with the Notebook, Bokeh can also save plots out into their own HTML files. Here is the bar plot example from above, but saving into its own standalone file.\n", + "\n", + "Now when we call `show()`, a new browser tab is also opened with the plot. If we just wanted to save the file, we would use `save()` instead." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
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Application\",\"version\":\"0.13.0\"}};\n", + " var render_items = [{\"docid\":\"5427e26a-e4a6-41ff-906d-a3dc6a245c5a\",\"roots\":{\"71a19538-4460-4b13-b454-105dc614f5b1\":\"a3fbb3bd-5e38-4110-bb97-3dccf780acf7\"}}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + "\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " var attempts = 0;\n", + " var timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " clearInterval(timer);\n", + " }\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", + " clearInterval(timer);\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "71a19538-4460-4b13-b454-105dc614f5b1" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "from bokeh.plotting import output_file\n", + "\n", + "output_file(\"barplot.html\")\n", + "\n", + "p = figure(title=\"MPG by Year (Japan and US)\")\n", + "\n", + "p.vbar(x=years, bottom=avg-std, top=avg+std, width=0.8, \n", + " fill_alpha=0.2, line_color=None, legend=\"MPG 1 stddev\")\n", + "\n", + "p.circle(x=japanese[\"yr\"], y=japanese[\"mpg\"], size=10, alpha=0.3,\n", + " color=\"red\", legend=\"Japanese\")\n", + "\n", + "p.triangle(x=american[\"yr\"], y=american[\"mpg\"], size=10, alpha=0.3,\n", + " color=\"blue\", legend=\"American\")\n", + "\n", + "p.legend.location = \"top_left\"\n", + "show(p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bokeh Applications\n", + "\n", + "Bokeh also has a server component that can be used to build interactive web applications that easily connect the powerful constellation of PyData tools to sophisticated Bokeh visualizations. The Bokeh server can be used to:\n", + "\n", + "* respond to UI and tool events generated in a browser with computations or queries using the full power of python\n", + "* automatically push server-side updates to the UI (i.e. widgets or plots in a browser)\n", + "* use periodic, timeout, and asynchronous callbacks to drive streaming updates\n", + "\n", + "The cell below shows a simple deployed Bokeh application from https://demo.bokehplots.com embedded in an IFrame. Scrub the sliders or change the title to see the plot update. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + " \n", + " " + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from IPython.display import IFrame\n", + "IFrame('https://demo.bokehplots.com/apps/sliders/', width=900, height=410)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Click on any of the thumbnails below to launch other live Bokeh applications.\n", + "\n", + "
\n", + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + "\n", + " \n", + "\n", + "
\n", + "\n", + "Find more details and information about developing and deploying Bokeh server applications in the User's Guide chapter [Running a Bokeh Server](https://bokeh.pydata.org/en/latest/docs/user_guide/server.html)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## BokehJS\n", + "\n", + "At its core, Bokeh consists of a Javascript library, [BokehJS](https://github.com/bokeh/bokeh/tree/master/bokehjs), and a Python binding which provides classes and objects that ultimately generate a JSON representation of the plot structure.\n", + "\n", + "You can read more about design and usage in the [Developing with JavaScript](https://bokeh.pydata.org/en/latest/docs/user_guide/bokehjs.html) section of the Bokeh User's Guide." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## More Information\n", + "\n", + "Find more details and information at the resources listed below:\n", + "\n", + "*Documentation:* https://bokeh.pydata.org/en/latest\n", + "\n", + "*GitHub:* https://github.com/bokeh/bokeh\n", + "\n", + "*Mailing list:* [bokeh@anaconda.com](mailto:bokeh@anaconda.com)\n", + "\n", + "*Gitter Chat:* https://gitter.im/bokeh/bokeh\n", + "\n", + "Be sure to follow us on Twitter [@bokehplots](http://twitter.com/BokehPlots>) and on [Youtube](https://www.youtube.com/c/Bokehplots)!\n", + "\n", + "" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/0_numpy_matplotlib_scipy_sympy/example.png 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100% rename from 2_pytorch/1_NN/optimizer/adadelta.py rename to 6_pytorch/1_NN/optimizer/adadelta.py diff --git a/2_pytorch/1_NN/optimizer/adagrad.ipynb b/6_pytorch/1_NN/optimizer/adagrad.ipynb similarity index 99% rename from 2_pytorch/1_NN/optimizer/adagrad.ipynb rename to 6_pytorch/1_NN/optimizer/adagrad.ipynb index 85bfd1a..0f22510 100644 --- a/2_pytorch/1_NN/optimizer/adagrad.ipynb +++ b/6_pytorch/1_NN/optimizer/adagrad.ipynb @@ -80,9 +80,7 @@ { "cell_type": "code", "execution_count": 3, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -141,9 +139,7 @@ { "cell_type": "code", "execution_count": 4, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -189,9 +185,7 @@ { "cell_type": "code", "execution_count": 5, - "metadata": { - "collapsed": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -242,9 +236,9 @@ ], "metadata": { "kernelspec": { - "display_name": "mx", + "display_name": "Python 3", "language": "python", - "name": "mx" + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -256,7 +250,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.0" + "version": "3.5.2" } }, "nbformat": 4, diff --git a/2_pytorch/1_NN/optimizer/adam.ipynb b/6_pytorch/1_NN/optimizer/adam.ipynb similarity index 100% rename from 2_pytorch/1_NN/optimizer/adam.ipynb rename to 6_pytorch/1_NN/optimizer/adam.ipynb diff --git a/2_pytorch/1_NN/optimizer/adam.py b/6_pytorch/1_NN/optimizer/adam.py similarity index 100% rename from 2_pytorch/1_NN/optimizer/adam.py rename to 6_pytorch/1_NN/optimizer/adam.py diff --git a/2_pytorch/1_NN/optimizer/momentum.ipynb b/6_pytorch/1_NN/optimizer/momentum.ipynb similarity index 100% rename from 2_pytorch/1_NN/optimizer/momentum.ipynb rename to 6_pytorch/1_NN/optimizer/momentum.ipynb diff --git a/2_pytorch/1_NN/optimizer/momentum.py b/6_pytorch/1_NN/optimizer/momentum.py similarity index 100% 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b/6_pytorch/4_GAN/autoencoder.py similarity index 100% rename from 2_pytorch/4_GAN/autoencoder.py rename to 6_pytorch/4_GAN/autoencoder.py diff --git a/2_pytorch/4_GAN/gan.ipynb b/6_pytorch/4_GAN/gan.ipynb similarity index 100% rename from 2_pytorch/4_GAN/gan.ipynb rename to 6_pytorch/4_GAN/gan.ipynb diff --git a/2_pytorch/4_GAN/gan.py b/6_pytorch/4_GAN/gan.py similarity index 100% rename from 2_pytorch/4_GAN/gan.py rename to 6_pytorch/4_GAN/gan.py diff --git a/2_pytorch/4_GAN/vae.ipynb b/6_pytorch/4_GAN/vae.ipynb similarity index 100% rename from 2_pytorch/4_GAN/vae.ipynb rename to 6_pytorch/4_GAN/vae.ipynb diff --git a/2_pytorch/4_GAN/vae.py b/6_pytorch/4_GAN/vae.py similarity index 100% rename from 2_pytorch/4_GAN/vae.py rename to 6_pytorch/4_GAN/vae.py diff --git a/6_pytorch/5_NLP/README.md b/6_pytorch/5_NLP/README.md new file mode 100644 index 0000000..647b656 --- /dev/null +++ b/6_pytorch/5_NLP/README.md @@ -0,0 +1,8 @@ + + +## References + +* [神经网络嵌入详解](https://mp.weixin.qq.com/s/9Azv6xOZuY0ntcQpiqLD-A) + +* [Neural Network Embeddings Explained](https://towardsdatascience.com/neural-network-embeddings-explained-4d028e6f0526) + diff --git a/2_pytorch/PyTorch_quick_intro.ipynb b/6_pytorch/PyTorch_quick_intro.ipynb similarity index 100% rename from 2_pytorch/PyTorch_quick_intro.ipynb rename to 6_pytorch/PyTorch_quick_intro.ipynb diff --git a/2_pytorch/README.md b/6_pytorch/README.md similarity index 100% rename from 2_pytorch/README.md rename to 6_pytorch/README.md diff --git a/2_pytorch/imgs/Ipython-auto.png b/6_pytorch/imgs/Ipython-auto.png similarity index 100% rename from 2_pytorch/imgs/Ipython-auto.png rename to 6_pytorch/imgs/Ipython-auto.png diff --git a/2_pytorch/imgs/Ipython-help.png b/6_pytorch/imgs/Ipython-help.png similarity index 100% rename from 2_pytorch/imgs/Ipython-help.png rename to 6_pytorch/imgs/Ipython-help.png diff --git a/2_pytorch/imgs/Jupyter主页面.png b/6_pytorch/imgs/Jupyter主页面.png similarity index 100% rename from 2_pytorch/imgs/Jupyter主页面.png rename to 6_pytorch/imgs/Jupyter主页面.png diff --git a/2_pytorch/imgs/Notebook主界面.png b/6_pytorch/imgs/Notebook主界面.png similarity index 100% rename from 2_pytorch/imgs/Notebook主界面.png rename to 6_pytorch/imgs/Notebook主界面.png diff --git a/2_pytorch/imgs/autograd_Variable.png b/6_pytorch/imgs/autograd_Variable.png similarity index 100% rename from 2_pytorch/imgs/autograd_Variable.png rename to 6_pytorch/imgs/autograd_Variable.png diff --git a/2_pytorch/imgs/autograd_Variable.svg b/6_pytorch/imgs/autograd_Variable.svg similarity index 100% rename from 2_pytorch/imgs/autograd_Variable.svg rename to 6_pytorch/imgs/autograd_Variable.svg diff --git a/2_pytorch/imgs/del/img1.png b/6_pytorch/imgs/del/img1.png similarity index 100% rename from 2_pytorch/imgs/del/img1.png rename to 6_pytorch/imgs/del/img1.png diff --git a/2_pytorch/imgs/del/img2.png b/6_pytorch/imgs/del/img2.png similarity index 100% rename from 2_pytorch/imgs/del/img2.png rename to 6_pytorch/imgs/del/img2.png diff --git a/2_pytorch/imgs/install-1.png b/6_pytorch/imgs/install-1.png similarity index 100% rename from 2_pytorch/imgs/install-1.png rename to 6_pytorch/imgs/install-1.png diff --git a/2_pytorch/imgs/install-2.png b/6_pytorch/imgs/install-2.png similarity index 100% rename from 2_pytorch/imgs/install-2.png rename to 6_pytorch/imgs/install-2.png diff --git a/2_pytorch/imgs/nn_lenet.png b/6_pytorch/imgs/nn_lenet.png similarity index 100% rename from 2_pytorch/imgs/nn_lenet.png rename to 6_pytorch/imgs/nn_lenet.png diff --git a/README.md b/README.md index 545ed1f..7455b00 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,15 @@ # Python与机器学习 -本教程包含了一些使用Python来学习机器学习的notebook，通过本教程的引导来快速得学习Python、Python的常用库、机器学习的理论知识与实际编程，并学习如何解决实际问题。 +本教程包含了一些使用Python来学习机器学习的notebook，通过本教程的引导来快速学习Python、Python的常用库、机器学习的理论知识与实际编程，并学习如何解决实际问题。 由于**本课程需要大量的编程练习才能取得比较好的学习效果**，因此需要认真把作业和报告完成，写作业的过程可以查阅网上的资料，但是不能直接照抄，需要自己独立思考并独立写出代码。 + + ## 内容 1. [Python](0_python/) + - [Install Python](tips/InstallPython.md) - [Introduction](0_python/0_Introduction.ipynb) - [Python Basics](0_python/1_Basics.ipynb) - [Print Statement](0_python/2_Print_Statement.ipynb) @@ -15,51 +18,52 @@ - [Control Flow](0_python/5_Control_Flow.ipynb) - [Function](0_python/6_Function.ipynb) - [Class](0_python/7_Class.ipynb) -2. [numpy & matplotlib](0_numpy_matplotlib_scipy_sympy/) - - [numpy](0_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb) - - [matplotlib](0_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb) -3. [knn](1_knn/knn_classification.ipynb) -4. [kMenas](1_kmeans/knn_classification.ipynb) -5. [Logistic Regression](1_logistic_regression/) - - [Least squares](1_logistic_regression/Least_squares.ipynb) - - [Logistic regression](1_logistic_regression/Logistic_regression.ipynb) -6. [Neural Network](1_nn/) - - [Perceptron](1_nn/Perceptron.ipynb) - - [Multi-layer Perceptron & BP](1_nn/mlp_bp.ipynb) - - [Softmax & cross-entroy](1_nn/softmax_ce.ipynb) -7. [PyTorch](2_pytorch/) - - [short tutorial](2_pytorch/PyTorch_quick_intro.ipynb) - - [basic/Tensor-and-Variable](2_pytorch/0_basic/Tensor-and-Variable.ipynb) - - [basic/autograd](2_pytorch/0_basic/autograd.ipynb) - - [basic/dynamic-graph](2_pytorch/0_basic/dynamic-graph.ipynb) - - [nn/linear-regression-gradient-descend](2_pytorch/1_NN/linear-regression-gradient-descend.ipynb) - - [nn/logistic-regression](2_pytorch/1_NN/logistic-regression.ipynb) - - [nn/nn-sequential-module](2_pytorch/1_NN/nn-sequential-module.ipynb) - - [nn/bp](2_pytorch/1_NN/bp.ipynb) - - [nn/deep-nn](2_pytorch/1_NN/deep-nn.ipynb) - - [nn/param_initialize](2_pytorch/1_NN/param_initialize.ipynb) - - [optim/sgd](2_pytorch/1_NN/optimizer/sgd.ipynb) - - [optim/adam](2_pytorch/1_NN/optimizer/adam.ipynb) - - [cnn/basic_conv](2_pytorch/2_CNN/basic_conv.ipynb) - - [cnn/batch-normalization](2_pytorch/2_CNN/batch-normalization.ipynb) - - [cnn/regularization](2_pytorch/2_CNN/regularization.ipynb) - - [cnn/lr-decay](2_pytorch/2_CNN/lr-decay.ipynb) - - [cnn/vgg](2_pytorch/2_CNN/vgg.ipynb) - - [cnn/googlenet](2_pytorch/2_CNN/googlenet.ipynb) - - [cnn/densenet](2_pytorch/2_CNN/densenet.ipynb) - - [cnn/resnet](2_pytorch/2_CNN/resnet.ipynb) - - [rnn/pytorch-rnn](2_pytorch/3_RNN/pytorch-rnn.ipynb) - - [rnn/rnn-for-image](2_pytorch/3_RNN/rnn-for-image.ipynb) - - [rnn/lstm-time-series](2_pytorch/3_RNN/time-series/lstm-time-series.ipynb) - - [gan/autoencoder](2_pytorch/4_GNN/autoencoder.ipynb) - - [gan/vae](2_pytorch/4_GNN/vae.ipynb) - - [gan/gan](2_pytorch/4_GNN/gan.ipynb) +2. [numpy & matplotlib](1_numpy_matplotlib_scipy_sympy/) + - [numpy](1_numpy_matplotlib_scipy_sympy/numpy_tutorial.ipynb) + - [matplotlib](1_numpy_matplotlib_scipy_sympy/matplotlib_simple_tutorial.ipynb) +3. [knn](2_knn/knn_classification.ipynb) +4. [kMenas](3_kmeans/knn_classification.ipynb) +5. [Logistic Regression](4_logistic_regression/) + - [Least squares](4_logistic_regression/Least_squares.ipynb) + - [Logistic regression](4_logistic_regression/Logistic_regression.ipynb) +6. [Neural Network](5_nn/) + - [Perceptron](5_nn/Perceptron.ipynb) + - [Multi-layer Perceptron & BP](5_nn/mlp_bp.ipynb) + - [Softmax & cross-entroy](5_nn/softmax_ce.ipynb) +7. [PyTorch](6_pytorch/) + - [short tutorial](6_pytorch/PyTorch_quick_intro.ipynb) + - [basic/Tensor-and-Variable](6_pytorch/0_basic/Tensor-and-Variable.ipynb) + - [basic/autograd](6_pytorch/0_basic/autograd.ipynb) + - [basic/dynamic-graph](6_pytorch/0_basic/dynamic-graph.ipynb) + - [nn/linear-regression-gradient-descend](6_pytorch/1_NN/linear-regression-gradient-descend.ipynb) + - [nn/logistic-regression](6_pytorch/1_NN/logistic-regression.ipynb) + - [nn/nn-sequential-module](6_pytorch/1_NN/nn-sequential-module.ipynb) + - [nn/bp](6_pytorch/1_NN/bp.ipynb) + - [nn/deep-nn](6_pytorch/1_NN/deep-nn.ipynb) + - [nn/param_initialize](6_pytorch/1_NN/param_initialize.ipynb) + - [optim/sgd](6_pytorch/1_NN/optimizer/sgd.ipynb) + - [optim/adam](6_pytorch/1_NN/optimizer/adam.ipynb) + - [cnn/basic_conv](6_pytorch/2_CNN/basic_conv.ipynb) + - [cnn/batch-normalization](6_pytorch/2_CNN/batch-normalization.ipynb) + - [cnn/regularization](6_pytorch/2_CNN/regularization.ipynb) + - [cnn/lr-decay](6_pytorch/2_CNN/lr-decay.ipynb) + - [cnn/vgg](6_pytorch/2_CNN/vgg.ipynb) + - [cnn/googlenet](6_pytorch/2_CNN/googlenet.ipynb) + - [cnn/densenet](6_pytorch/2_CNN/densenet.ipynb) + - [cnn/resnet](6_pytorch/2_CNN/resnet.ipynb) + - [rnn/pytorch-rnn](6_pytorch/3_RNN/pytorch-rnn.ipynb) + - [rnn/rnn-for-image](6_pytorch/3_RNN/rnn-for-image.ipynb) + - [rnn/lstm-time-series](6_pytorch/3_RNN/time-series/lstm-time-series.ipynb) + - [gan/autoencoder](6_pytorch/4_GNN/autoencoder.ipynb) + - [gan/vae](6_pytorch/4_GNN/vae.ipynb) + - [gan/gan](6_pytorch/4_GNN/gan.ipynb) ## 其他参考 -* [学习参考资料等](References.md) +* [相关学习参考资料等](References.md) * [安装Python环境](tips/InstallPython.md) * [confusion matrix](tips/confusion_matrix.ipynb) * [一些速查手册](tips/cheatsheet) * [Python tips](tips/python) + diff --git a/tips/InstallPython.md b/tips/InstallPython.md index a3d1315..c5a610d 100644 --- a/tips/InstallPython.md +++ b/tips/InstallPython.md @@ -1,6 +1,8 @@ # Installing Python environments -这章，讲解如何安装Python的环境 +由于Python的库比较多，并且依赖关系比较复杂，所以请仔细阅读下面的说明，使用下面的说明来安装能够减少问题的可能。 + + ## 1. Windows @@ -25,6 +27,7 @@ pip3 install torchvision ``` + ## 2. Linux ### 安装pip @@ -32,12 +35,18 @@ pip3 install torchvision sudo apt-get install python3-pip ``` + + ### 设置PIP源 + ``` pip config set global.index-url 'https://mirrors.ustc.edu.cn/pypi/web/simple' ``` + + ### 安装常用的包 + ``` pip install -r requirements.txt ``` @@ -53,7 +62,10 @@ sudo pip install ipython sudo pip install jupyter ``` + + ### 安装pytorch + 到[pytorch 官网](https://pytorch.org)，根据自己的操作系统、CUDA版本，选择合适的安装命令。 例如Linux, Python3.5, CUDA 9.0： @@ -61,3 +73,11 @@ sudo pip install jupyter pip3 install torch torchvision ``` + + +## 3. 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