{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Playlist Neural Network\n", "\n", "Given a list of playlists, can unknown tracks be correctly classified?" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# playlist_names = [\"RAP\", \"EDM\", \"ROCK\", \"METAL\", \"JAZZ\", \"POP\"] # super-genres\n", "# playlist_names = [\"ALL RAP\", \"EDM\", \"ROCK\", \"METAL\", \"JAZZ\", \"POP\"] # super-genres\n", "# playlist_names = [\"RAP\", \"EDM\", \"ROCK\", \"METAL\", \"JAZZ\"] # super-genres without POP\n", "# playlist_names = [\"ALL RAP\", \"EDM\", \"ROCK\", \"METAL\", \"JAZZ\"] # super-genres without POP\n", "playlist_names = [\"ALL RAP\", \"DNB\", \"4/4\", \"cRock\", \"METAL\", \"cJazz\"] # super-genres with decomposed EDM\n", "# playlist_names = [\"DNB\", \"HOUSE\", \"TECHNO\", \"GARAGE\", \"DUBSTEP\", \"BASS\"] # EDM playlists\n", "# playlist_names = [\"20s rap\", \"10s rap\", \"00s rap\", \"90s rap\", \"80s rap\"] # rap decades\n", "# playlist_names = [\"UK RAP\", \"US RAP\"] # UK/US split\n", "# playlist_names = [\"uk rap\", \"grime\", \"drill\", \"afro bash\"] # british rap playlists\n", "# playlist_names = [\"20s rap\", \"10s rap\", \"00s rap\", \"90s rap\", \"80s rap\", \"trap\", \"gangsta rap\", \"industrial rap\", \"weird rap\", \"jazz rap\", \"boom bap\", \"trap metal\"] # american rap playlists\n", "# playlist_names = [\"rock\", \"indie\", \"punk\", \"pop rock\", \"bluesy rock\", \"hard rock\", \"chilled rock\", \"emo\", \"pop punk\", \"stoner rock/metal\", \"post-hardcore\", \"melodic hardcore\", \"art rock\", \"post-rock\", \"classic pop punk\", \"90s rock & grunge\", \"90s indie & britpop\", \"psych\"] # rock playlists\n", "# playlist_names = [\"metal\", \"metalcore\", \"mathcore\", \"hardcore\", \"black metal\", \"death metal\", \"doom metal\", \"sludge metal\", \"classic metal\", \"industrial\", \"nu metal\", \"calm metal\", \"thrash metal\"] # metal playlists\n", "\n", "# headers = float_headers + [\"duration_ms\", \"mode\", \"loudness\", \"tempo\"]\n", "headers = float_headers + [\"mode\", \"loudness\", \"tempo\"]\n", "# headers = float_headers\n", "\n", "BALANCED_WEIGHTS = True" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pull and process playlist information.\n", "\n", "1. Get live playlist track information from spotify\n", "2. Filter listening history for these tracks\n", "\n", "Filter out tracks without features and drop duplicates before taking only the descriptor parameters" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "playlists = [get_playlist(i, spotnet) for i in playlist_names] # 1)\n", "\n", "# filter playlists by join with playlist track/artist names\n", "filtered_playlists = [pd.merge(track_frame(i.tracks), scrobbles, on=['track', 'artist']) for i in playlists] # 2)\n", "\n", "filtered_playlists = [i[pd.notnull(i[\"uri\"])] for i in filtered_playlists]\n", "# distinct on uri\n", "filtered_playlists = [i.drop_duplicates(['uri']) for i in filtered_playlists]\n", "# select only descriptor float columns\n", "filtered_playlists = [i[headers] for i in filtered_playlists]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construct the dataset with associated labels before splitting into a train and test set." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "dataset = pd.concat(filtered_playlists)\n", "labels = [np.full(len(plst), idx) for idx, plst in enumerate(filtered_playlists)]\n", "labels = np.concatenate(labels)\n", "\n", "# stratify: maintains class proportions in test and train set\n", "data_train, data_test, labels_train, labels_test = train_test_split(dataset, labels, \n", " test_size=0.1, \n", "# random_state=70, \n", " stratify=labels\n", " )\n", "\n", "class_weights = class_weight.compute_class_weight('balanced',\n", " classes=np.unique(labels_train),\n", " y=labels_train)\n", "class_weights = {i: j for i, j in zip(range(len(filtered_playlists)), class_weights)}\n", "\n", "labels_train = tf.one_hot(labels_train, len(filtered_playlists))\n", "labels_test = tf.one_hot(labels_test, len(filtered_playlists))" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def tensorboard_callback(path='tensorboard-logs', prefix=''):\n", " return tf.keras.callbacks.TensorBoard(\n", " log_dir=os.path.normpath(os.path.join(path, prefix + datetime.now().strftime(\"%Y%m%d-%H%M%S\"))), histogram_freq=1\n", " )" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def get_model(hidden_nodes=128,\n", " layers=2,\n", " classes=len(filtered_playlists),\n", " activation=lambda: 'sigmoid', \n", " weight_init=lambda: 'glorot_uniform'):\n", " l = [tf.keras.layers.InputLayer(input_shape=data_train.to_numpy()[0].shape, name='Input')]\n", " \n", " for i in range(layers):\n", " l.append(\n", " tf.keras.layers.Dense(hidden_nodes, \n", " activation=activation(), \n", " kernel_initializer=weight_init(), \n", " name=f'Hidden{i+1}')\n", " )\n", " \n", " l.append(tf.keras.layers.Dense(classes, \n", " activation='softmax', \n", " kernel_initializer=weight_init(), \n", " name='Output'))\n", " \n", " model = tf.keras.models.Sequential(l)\n", " return model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Single Model" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Model: \"sequential\"\n", "_________________________________________________________________\n", "Layer (type) Output Shape Param # \n", "=================================================================\n", "Hidden1 (Dense) (None, 64) 704 \n", "_________________________________________________________________\n", "Output (Dense) (None, 6) 390 \n", "=================================================================\n", "Total params: 1,094\n", "Trainable params: 1,094\n", "Non-trainable params: 0\n", "_________________________________________________________________\n" ] } ], "source": [ "model = get_model(hidden_nodes=64, layers=1)\n", "\n", "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), \n", "# optimizer=tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.9),\n", " loss='categorical_crossentropy', \n", " metrics=['accuracy'])\n", "model.summary()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "if BALANCED_WEIGHTS:\n", " cw = class_weights\n", "else:\n", " cw = None\n", "history = model.fit(data_train.to_numpy(), labels_train, \n", " callbacks=[tensorboard_callback()], \n", " validation_split=0.11,\n", " verbose=0,\n", " class_weight=cw,\n", " epochs=50)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "history.history\n", "plt.plot(range(len(history.history[\"accuracy\"])), history.history[\"accuracy\"], label=\"Train\", c=(0, 0, 1))\n", "plt.plot(range(len(history.history[\"val_accuracy\"])), history.history[\"val_accuracy\"], label=\"Validation\", c=(1, 0, 0))\n", "\n", "plt.xlabel(\"Epochs\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.ylim(0, 1)\n", "\n", "plt.grid()\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test\n", "\n", "Single number below from the evaluate function" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "10/10 [==============================] - 0s 971us/step - loss: 0.7446 - accuracy: 0.7384\n" ] }, { "data": { "text/plain": [ "[0.7446056008338928, 0.7384105920791626]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.evaluate(data_test.to_numpy(), labels_test)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get raw predictions from test data to generate a confusion matrix" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "predictions = model(data_test.to_numpy())\n", "\n", "conf = tf.math.confusion_matrix([tf.math.argmax(i) for i in labels_test], \n", " [tf.math.argmax(i) for i in predictions], \n", " num_classes=len(filtered_playlists))\n", "\n", "normalised_conf = np.ndarray((len(filtered_playlists), len(filtered_playlists)))\n", "for idx, row in enumerate(conf):\n", " normalised_conf[idx, :] = row / np.sum(row)\n", "\n", "sns.heatmap(normalised_conf, \n", " annot=True, \n", " xticklabels=playlist_names, yticklabels=playlist_names, \n", " cmap='inferno')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Ensemble Model" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "models = [get_model(hidden_nodes=random.randint(16, 128), \n", " layers=random.randint(1, 2)) \n", " for _ in range(9)]\n", "\n", "for m in models:\n", " m.compile(\n", " optimizer=tf.keras.optimizers.Adam(learning_rate=0.01), \n", "# optimizer=tf.keras.optimizers.SGD(learning_rate=0.01, momentum=0.9),\n", " loss='categorical_crossentropy', \n", " metrics=['accuracy'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Train the models of the meta-classifier. Get a random number of epochs from a reasonable range to introduce variation between models. *Weird?*: Randomly decide whether to regularise by class weights. Class weights change the penalisation methods such that smaller classes are treated more importantly when computing loss. I'm not sure whether it's going to help. " ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "training model 1\n", "training model 2\n", "training model 3\n", "training model 4\n", "training model 5\n", "training model 6\n", "training model 7\n", "training model 8\n", "training model 9\n" ] } ], "source": [ "if BALANCED_WEIGHTS:\n", " cw = class_weights\n", "else:\n", " cw = None\n", "\n", "ensem_histories = list()\n", "for idx, m in enumerate(models):\n", " print(f'training model {idx+1}')\n", " h = m.fit(data_train.to_numpy(), labels_train, \n", " callbacks=[tensorboard_callback()], \n", " validation_split=0.11,\n", " verbose=0,\n", "# class_weight=cw,\n", " class_weight=random.choice([*([class_weights]*3), None]),\n", " epochs=random.randint(20, 100))\n", " ensem_histories.append(h)" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "77.2% Accuracy, 77.3% Agreement, 62.2% Ind. Accuracy\n" ] } ], "source": [ "ensem_results = ensem_classify(models, data_test, labels_test)\n", "print(f\"{ensem_results[2]*100:.3}% Accuracy, {ensem_results[3]*100:.3}% Agreement, {ensem_results[4]*100:.3}% Ind. Accuracy\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ensem_conf = tf.math.confusion_matrix([tf.math.argmax(i) for i in labels_test], \n", " ensem_results[0], \n", " num_classes=len(filtered_playlists))\n", "\n", "normalised_ensem_conf = np.ndarray((len(filtered_playlists), len(filtered_playlists)))\n", "for idx, row in enumerate(ensem_conf):\n", " normalised_ensem_conf[idx, :] = row / np.sum(row)\n", "\n", "sns.heatmap(normalised_ensem_conf, \n", " annot=True, \n", " xticklabels=playlist_names, yticklabels=playlist_names, \n", " cmap='inferno')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Imports & Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from datetime import datetime\n", "import os\n", "import random\n", "\n", "from google.cloud import bigquery\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "mpl.rcParams['figure.dpi'] = 120\n", "import seaborn as sns\n", "\n", "from analysis.nn import ensem_classify\n", "from analysis.net import get_spotnet, get_playlist, track_frame\n", "from analysis.query import *\n", "from analysis import spotify_descriptor_headers, float_headers, days_since\n", "\n", "import numpy as np\n", "import pandas as pd\n", "\n", "from sklearn import svm\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import plot_confusion_matrix\n", "from sklearn.utils import class_weight\n", "\n", "import tensorflow as tf\n", "\n", "client = bigquery.Client()\n", "spotnet = get_spotnet()\n", "cache = 'query.csv'\n", "first_day = datetime(year=2017, month=11, day=3)\n", "sig_max, c_max = 0.5, 20" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Read Scrobble Frame" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "scrobbles = get_query(cache=cache)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Write Scrobble Frame" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "scrobbles.reset_index().to_csv(cache, sep='\\t')" ] } ], "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": "3.8.9" }, "metadata": { "interpreter": { "hash": "bce1a3677099e73bf385a0de8ef462673e03f7df0abce93e57e7ca76e8c504a2" } } }, "nbformat": 4, "nbformat_minor": 4 }