diff --git a/IntroductionToPlyto.ipynb b/IntroductionToPlyto.ipynb deleted file mode 100644 index daf731f..0000000 --- a/IntroductionToPlyto.ipynb +++ /dev/null @@ -1,351 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Introduction to Plyto with Keras\n", - "#### Python Machine Learning Visualization Toolkit\n", - "This notebook will demonstrate how to use our Keras Accuracy Loss function with the PlytoAPI to visualize model accuracy and loss throughout the training process of a machine learning algorithm, as well as a tutorial on how to create your own callback function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - " toolbar item opens the Plyto model visualizer for this notebook!" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [], - "source": [ - "from plyto import PlytoAPI, KerasAccuracyLossCallback" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### How it works" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A callback function that takes a plyto instance as a parameter is passed as a callback to model.fit when using keras for machine learning. A plyto instance requires an Altair spec to define plots. Below is an example of a simple altair spec to create a line graph of samples versus accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "# an array of altair specs with one plot of samples versus accuracy\n", - "spec = [\n", - " {\n", - " # specifies an altair spec\n", - " \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.json\",\n", - " \"name\": \"accuracyGraph\",\n", - " \n", - " #size of the plot\n", - " \"config\": {\n", - " \"view\": {\n", - " \"height\": 300,\n", - " \"width\": 300\n", - " }\n", - " },\n", - " \n", - " # name of the dataset as defined in the backend of the callback function\n", - " \"data\": {\n", - " \"name\": \"dataSet\"\n", - " },\n", - " \n", - " # visual encodings of the plot\n", - " \"encoding\": {\n", - " \"x\": {\n", - " \"field\": \"samples\",\n", - " \"type\": \"quantitative\"\n", - " },\n", - " \"y\": {\n", - " \"field\": \"accuracy\",\n", - " \"type\": \"quantitative\"\n", - " }\n", - " },\n", - " \n", - " \n", - " \"mark\": \"line\"\n", - " }\n", - "]\n", - "\n", - "# pass the spec into PlytoAPI\n", - "plytoInstance = PlytoAPI(spec)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Running a model\n", - "To demonstrate how plyto works, we will be looking at the MNIST handwritten digit data, which can be loaded from keras.datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.datasets import mnist\n", - "from keras.models import Sequential\n", - "from keras.layers import Dense, Dropout, Activation, Flatten, Convolution2D, MaxPooling2D\n", - "from keras.utils import np_utils\n", - "\n", - "# Load pre-shuffled MNIST data into train and test sets\n", - "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n", - " \n", - "# Preprocess input data\n", - "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)\n", - "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)\n", - "X_train = X_train.astype('float32')\n", - "X_test = X_test.astype('float32')\n", - "X_train /= 255\n", - "X_test /= 255\n", - " \n", - "# Preprocess class labels\n", - "Y_train = np_utils.to_categorical(y_train, 10)\n", - "Y_test = np_utils.to_categorical(y_test, 10)\n", - " \n", - "# Define model architecture\n", - "model = Sequential()\n", - " \n", - "model.add(Convolution2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", - "model.add(Convolution2D(32, (3, 3), activation='tanh'))\n", - "model.add(MaxPooling2D(pool_size=(2,2)))\n", - "model.add(Dropout(0.25))\n", - " \n", - "model.add(Flatten())\n", - "model.add(Dense(128, activation='relu'))\n", - "model.add(Dropout(0.5))\n", - "model.add(Dense(10, activation='tanh'))\n", - "\n", - "# Compile the model\n", - "model.compile(loss='categorical_crossentropy',\n", - " optimizer='adam',\n", - " metrics=['accuracy'])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However you fit your model, simply add the callback as a parameter and open the Plyto model visualizer to see your statistics and plots update." - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "metadata": {}, - "outputs": [ - { - "ename": "TypeError", - "evalue": "unsupported operand type(s) for /: 'int' and 'dict'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m 1035\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1036\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1037\u001b[0;31m validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m 1038\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1039\u001b[0m def evaluate(self, x=None, y=None,\n", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m 125\u001b[0m \u001b[0;34m'metrics'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcallback_metrics\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 126\u001b[0m })\n\u001b[0;32m--> 127\u001b[0;31m \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 128\u001b[0m \u001b[0mcallback_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_training\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 129\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mcbk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;32m~/github/jupyterlab/plyto/plyto/keras_accuracy_loss_callback.py\u001b[0m in \u001b[0;36mon_train_begin\u001b[0;34m(self, logs)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[0mReset\u001b[0m \u001b[0mthe\u001b[0m \u001b[0mcurrent\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;31m'\u001b[0m\u001b[0ms\u001b[0m \u001b[0mprogress\u001b[0m \u001b[0mevery\u001b[0m \u001b[0mnew\u001b[0m \u001b[0mepoch\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 64\u001b[0m \"\"\"\n\u001b[0;32m---> 65\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_progress\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 66\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 67\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccuracy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - 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"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for /: 'int' and 'dict'" - ] - } - ], - "source": [ - "model.fit(X_train, Y_train, batch_size=None, epochs=5, verbose=0, callbacks=[KerasAccuracyLossCallback(plytoInstance)])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#### Writing your own callback function" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "A callback function for Plyto is a class that takes a keras callback as a parameter. \n", - "\n", - "Within this custom function, you can define functions to execute at specific points in running the model.\n", - "\n", - "Keras Callbacks\n", - "- on_epoch_begin: called at the beginning of every epoch.\n", - "- on_epoch_end: called at the end of every epoch.\n", - "- on_batch_begin: called at the beginning of every batch.\n", - "- on_batch_end: called at the end of every batch.\n", - "- on_train_begin: called at the beginning of model training.\n", - "- on_train_end: called at the end of model training.\n", - "\n", - "For more information on using callbacks, visit [keras callback documentation](https://keras.io/callbacks/).\n", - "\n", - "For the progress bars in the status bar to work correctly, your callback function must have epochs, sample_amount, total_progress, current_progress, mode, and epoch_number. Further, total_runtime and starttime is required for the panel to display the runtime once the model is complete. Below is a base to work off of, only containing these variables for basic functionality and passing no altair spec for plots." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "from keras.callbacks import Callback\n", - "from time import time\n", - "\n", - "\n", - "class Basic(Callback):\n", - "\n", - " def __init__(self, plyto_instance):\n", - " self.epochs = None\n", - " self.sample_amount = 0\n", - " self.total_progress = 0\n", - " self.current_progress = 0\n", - " self.mode = 0\n", - " self.total_runtime = 0\n", - " self.starttime = time()\n", - " self.epoch_number = 1\n", - " self.plyto = plyto_instance\n", - "\n", - " def on_train_begin(self, logs={}):\n", - " \"\"\"\n", - " Get number of samples/steps per epoch and total number of epochs\n", - " \"\"\"\n", - " if \"samples\" in self.params:\n", - " self.sample_amount = self.params[\"samples\"]\n", - " elif \"nb_sample\" in self.params:\n", - " self.sample_amount = self.params[\"nb_sample\"]\n", - " else:\n", - " self.sample_amount = self.params[\"steps\"]\n", - " self.mode = 1\n", - "\n", - " self.epochs = self.params[\"epochs\"]\n", - " self.plyto.update_size(self.sample_amount)\n", - " self.plyto.update_total_steps(self.epochs)\n", - "\n", - " def on_epoch_begin(self, epoch, logs={}):\n", - " \"\"\"\n", - " Reset the current epoch's progress every new epoch\n", - " \"\"\"\n", - " self.current_progress = 0\n", - "\n", - " def on_epoch_end(self, epoch, logs={}):\n", - " self.epoch_number += 1\n", - " self.plyto.update_current_step(self.epoch_number)\n", - "\n", - " def on_batch_end(self, batch, logs={}):\n", - " \"\"\"\n", - " Update statistics and datasets\n", - " \"\"\"\n", - " if self.mode == 0:\n", - " self.current_progress += logs.get(\"size\")\n", - " self.total_progress += logs.get(\"size\")\n", - " else:\n", - " self.current_progress += 1\n", - " self.total_progress += 1\n", - " self.total_runtime = time() - self.starttime\n", - " self.plyto.update_runtime(self.total_runtime)\n", - " self.plyto.update_current_progress(self.current_progress)\n", - " self.plyto.update_total_progress(self.total_progress)\n", - " self.plyto.update_data_set(\n", - " {\n", - " \"samples\": self.total_progress,\n", - " }\n", - " )\n", - " if self.total_progress % 256 == 0 or self.total_progress == (self.epochs * self.sample_amount):\n", - " self.plyto.send_data()\n", - "\n", - " \n", - "basicPlatoInstance = PlytoAPI([])\n", - "basicCallback = Basic(basicPlatoInstance)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "KeyboardInterrupt", - "evalue": "", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mY_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbose\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcallbacks\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbasicCallback\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, **kwargs)\u001b[0m\n\u001b[1;32m 1035\u001b[0m \u001b[0minitial_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minitial_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1036\u001b[0m \u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0msteps_per_epoch\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1037\u001b[0;31m validation_steps=validation_steps)\n\u001b[0m\u001b[1;32m 1038\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1039\u001b[0m def evaluate(self, x=None, y=None,\n", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/engine/training_arrays.py\u001b[0m in \u001b[0;36mfit_loop\u001b[0;34m(model, f, ins, out_labels, batch_size, epochs, verbose, callbacks, val_f, val_ins, shuffle, callback_metrics, initial_epoch, steps_per_epoch, validation_steps)\u001b[0m\n\u001b[1;32m 197\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mins_batch\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 198\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mins_batch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 200\u001b[0m \u001b[0mouts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mto_list\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 201\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ml\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mo\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mouts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2670\u001b[0m \u001b[0;34m'In order to feed symbolic tensors to a Keras model '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2671\u001b[0m 'in TensorFlow, you need tensorflow 1.8 or higher.')\n\u001b[0;32m-> 2672\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_legacy_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2673\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2674\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/keras/backend/tensorflow_backend.py\u001b[0m in \u001b[0;36m_legacy_call\u001b[0;34m(self, inputs)\u001b[0m\n\u001b[1;32m 2652\u001b[0m \u001b[0msession\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_session\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2653\u001b[0m updated = session.run(fetches=fetches, feed_dict=feed_dict,\n\u001b[0;32m-> 2654\u001b[0;31m **self.session_kwargs)\n\u001b[0m\u001b[1;32m 2655\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mupdated\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2656\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 776\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 777\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[0;32m--> 778\u001b[0;31m run_metadata_ptr)\n\u001b[0m\u001b[1;32m 779\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 780\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run\u001b[0;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 980\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 981\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[0;32m--> 982\u001b[0;31m feed_dict_string, options, run_metadata)\n\u001b[0m\u001b[1;32m 983\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 984\u001b[0m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_run\u001b[0;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[1;32m 1030\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1031\u001b[0m return self._do_call(_run_fn, self._session, feed_dict, fetch_list,\n\u001b[0;32m-> 1032\u001b[0;31m target_list, options, run_metadata)\n\u001b[0m\u001b[1;32m 1033\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1034\u001b[0m return self._do_call(_prun_fn, self._session, handle, feed_dict,\n", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_do_call\u001b[0;34m(self, fn, *args)\u001b[0m\n\u001b[1;32m 1037\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1038\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1039\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1040\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1041\u001b[0m \u001b[0mmessage\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/tensorflow/python/client/session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[0;34m(session, feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[1;32m 1019\u001b[0m return tf_session.TF_Run(session, options,\n\u001b[1;32m 1020\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1021\u001b[0;31m status, run_metadata)\n\u001b[0m\u001b[1;32m 1022\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1023\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msession\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mKeyboardInterrupt\u001b[0m: " - ] - } - ], - "source": [ - "model.fit(X_train, Y_train, batch_size=None, epochs=2, verbose=0, callbacks=[basicCallback])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Only variables in the data set can be used in the Altair plotting spec and will be displayed in the panel. The basic example only includes the variale \"samples\", as seen when updating the Plyto data set with\n", - "\n", - "```python\n", - "self.plyto.update_data_set({\n", - " \"samples\": self.total_progress\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": "3.6.5" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/PlytoData.ipynb b/PlytoData.ipynb new file mode 100644 index 0000000..17d671d --- /dev/null +++ b/PlytoData.ipynb @@ -0,0 +1,63454 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to Plyto for Data Exploration\n", + "Currently Plyto provides two functions to check your Pandas DataFrame for abnormalities as well as further explore specific columns." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from plyto import CheckData, CheckColumn\n", + "import pandas as pd\n", + "data = pd.read_csv('titanic3.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['pclass', 'survived', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket',\n", + " 'fare', 'cabin', 'embarked', 'boat', 'body', 'home.dest'],\n", + " dtype='object')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.columns" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Check Data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The CheckData function looks for abnormalties in data columns. The function must take in a dataframe, and has optional parameters to specify which columns to check and cutoff points for what you consider abnormal in your data.\n", + "\n", + "Below is an example of the CheckData function specifying columns, a cutoff point for proportion of missing or zero values that I consider abnormal (0.10), a cutoff point for high cardinality of categorical data, a cutoff point for high frequency of floating point numbers, and a cutoff point for low frequency of categories in categorical data. Another option that this function has is to specify your own outlier function that should take in the dataset and column name and output a lower limit and an upper limit for outliers. By default, the outlier function has lower and upper limits of 1.5*IQR above the third quartile and below the first quartile." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "survived: \n", + "- high proportion of zero values at 61.76%\n", + "- high frequency of floating point numbers with 2 number(s) having frequency over 45" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "parch: \n", + "- high proportion of zero values at 76.49%\n", + "- high frequency of floating point numbers with 3 number(s) having frequency over 45\n", + "- high number of outliers with 307 high outliers and 0 low outliers" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "age: \n", + "- high proportion of null values at 20.15%\n", + "- high frequency of floating point numbers with 1 number(s) having frequency over 45" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CheckData(data, columns=['sex','survived','parch','age'], \n", + " missing=0.10, cardinality=25, floatFrequency=45, catFrequency=150)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Check Column(s)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The CheckColumn function gives you a more detailed look on one or more columns of the DataFrame with summary statistics, a plot of the column's distribution, and the same abnormality details given by the CheckData function. The DataFrame and either a single column name or list of column names are required parameters. Optional parameters include all those used in CheckData as well as bins, which specifies the maximum number of bins in the distribution plot if the plot is of quantitative data.\n", + "\n", + "From the CheckData call above, we found that the *parch* column (the number of parents / children an individual had aboard) has high outliers. Below we inspect this column further with CheckColumn." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "###### Column Summary: Parch" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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Caroline", + "parch": 0, + "pclass": 1, + "sex": "female", + "sibsp": 0, + "survived": 1, + "ticket": "36928" + }, + { + "age": 58, + "boat": "8", + "body": null, + "cabin": "C103", + "embarked": "S", + "fare": 26.55, + "home.dest": "Birkdale, England Cleveland, Ohio", + "name": "Bonnell, Miss. Elizabeth", + "parch": 0, + "pclass": 1, + "sex": "female", + "sibsp": 0, + "survived": 1, + "ticket": "113783" + }, + { + "age": 42, + "boat": null, + "body": null, + "cabin": "D22", + "embarked": "S", + "fare": 26.55, + "home.dest": "London / Winnipeg, MB", + "name": "Borebank, Mr. John James", + "parch": 0, + "pclass": 1, + "sex": "male", + "sibsp": 0, + "survived": 0, + "ticket": "110489" + }, + { + "age": 45, + "boat": "4", + "body": null, + "cabin": null, + "embarked": "C", + "fare": 262.375, + "home.dest": "Cooperstown, NY", + "name": "Bowen, Miss. 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Elsie Edith", + "parch": 1, + "pclass": 1, + "sex": "female", + "sibsp": 0, + "survived": 1, + "ticket": "113505" + }, + { + "age": null, + "boat": "9", + "body": null, + "cabin": null, + "embarked": "S", + "fare": 26.55, + "home.dest": "Los Angeles, CA", + "name": "Bradley, Mr. George (\"George Arthur Brayton\")", + "parch": 0, + "pclass": 1, + "sex": "male", + "sibsp": 0, + "survived": 1, + "ticket": "111427" + }, + { + "age": 41, + "boat": null, + "body": null, + "cabin": "A21", + "embarked": "S", + "fare": 30.5, + "home.dest": "Pomeroy, WA", + "name": "Brady, Mr. John Bertram", + "parch": 0, + "pclass": 1, + "sex": "male", + "sibsp": 0, + "survived": 0, + "ticket": "113054" + }, + { + "age": 48, + "boat": null, + "body": 208, + "cabin": "B10", + "embarked": "C", + "fare": 50.4958, + "home.dest": "Omaha, NE", + "name": "Brandeis, Mr. Emil", + "parch": 0, + "pclass": 1, + "sex": "male", + "sibsp": 0, + "survived": 0, + "ticket": "PC 17591" + }, + { + "age": null, + "boat": null, + "body": null, + "cabin": null, + "embarked": "C", + "fare": 39.6, + "home.dest": "Philadelphia, PA", + "name": "Brewe, Dr. Arthur Jackson", + "parch": 0, + "pclass": 1, + "sex": "male", + "sibsp": 0, + "survived": 0, + "ticket": "112379" + }, + { + "age": 44, + "boat": "6", + "body": null, + "cabin": "B4", + "embarked": "C", + "fare": 27.7208, + "home.dest": "Denver, CO", + "name": "Brown, Mrs. James Joseph (Margaret Tobin)", + "parch": 0, + "pclass": 1, + "sex": "female", + "sibsp": 0, + "survived": 1, + "ticket": "PC 17610" + }, + { + "age": 59, + "boat": "D", + "body": null, + "cabin": "C101", + "embarked": "S", + "fare": 51.4792, + "home.dest": "Belmont, MA", + "name": "Brown, Mrs. John Murray (Caroline Lane Lamson)", + "parch": 0, + "pclass": 1, + "sex": "female", + "sibsp": 2, + "survived": 1, + "ticket": "11769" + }, + { + "age": 60, + "boat": "8", + "body": null, + "cabin": "D15", + "embarked": "C", + "fare": 76.2917, + "home.dest": "Philadelphia, PA", + "name": "Bucknell, Mrs. William Robert (Emma Eliza Ward)", + "parch": 0, + "pclass": 1, + "sex": "female", + "sibsp": 0, + "survived": 1, + "ticket": "11813" + }, + { + "age": 41, + "boat": "3", + "body": null, + "cabin": "E40", + "embarked": "C", + "fare": 134.5, + "home.dest": null, + "name": "Burns, Miss. 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+ "text/plain": [ + "\n", + "\n", + "If you see this message, it means the renderer has not been properly enabled\n", + "for the frontend that you are using. For more information, see\n", + "https://altair-viz.github.io/user_guide/troubleshooting.html\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "\n", + "- high proportion of zero values at 76.49%\n", + "- high frequency of floating point numbers with 3 number(s) having frequency over 45\n", + "- high number of outliers with 307 high outliers and 0 low outliers" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CheckColumn(data, 'parch', bins=15, missing=0.10, cardinality=25, floatFrequency=45, catFrequency=150)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After observing this column more carefully, we can conclude that the high outliers make sense, and they are only considered outliers because of how many individuals traveled without any parents or children." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "No abnormalities were found in the sex column, but if you were still curious about its distribution you could check both of these columns in one function call." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "###### Column Summary: Parch" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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For more information, see\n", + "https://altair-viz.github.io/user_guide/troubleshooting.html\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "\n", + "- high proportion of zero values at 76.49%\n", + "- high frequency of floating point numbers with 3 number(s) having frequency over 45\n", + "- high number of outliers with 307 high outliers and 0 low outliers" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "###### Column Summary: Sex" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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vshJB7jPAmmu/C5we8ToSP6ndap6Dy8/BQkrZUbRN0gVDNgfPBPbsS2BSA1wNYJ1BC8x8UYaUogHBG6O1Aa4GcGPA1NWwAa4GsNgr+6v8FklWHrLqmOy8ui78pmzHAFcDWMHQsryFmbA9RE+pds9X6PMqWr3UligzLVTyKtkzK0i45mCdS3dezOBqDNZWKSu6PcqM5NYGftk5uo7RMVbFbFb4eYiuqMV9H6L/MbwYdDypomFaPkpXVPzu2h73EF1tiK4NiKYaMsDFAc6sKZWEclDxKrqAtvZxiNY+VzdICpuk/a/ic6wYGUP1aQZ4mgM8vvuyqlSkO8WplHegrhAVMLxoyTz5pSDZnK427OFfVJJRv+p9sBzG9olTK4GiOB1K2lymvBVQkkt5ScwHtgAujQyk8hdWmh6lEVBOxMXhpipncbmWynRXOYQHFs/BxedgHWzsAnwxJPpp4N+CdZqfVZRyNrWoPeUJVjAXpYmXn5MAP9Qe/qkiHFyvLIMH+fPm36DhNYuxkdpDzecCWlePBwD7Az+xh3+q+OoFWKdXcjrTYmtQ0fHl0SXMZtWuzrU1lyuIyzeLevjbAXxpOKqeZFVTseeezgK5yJtfkXkUjOWS8PDX371M8T4qhu91AKzI8UrlriymWjhtEost5SPubYp3A1wM+q1ivs2e2jTSBdjDv5gcl6hddpFV4ZWTPipPfq3Cp0WKdzO4KTUo2K73wcX3wQVF3G11A2yAkzVwFAOCJwunq4pmsBmcrHtmcLKo2qtoBpvBydpmBieLqr2KZrAZnKxtZnCyqNqraAabwcnaZgYni6q9imawGZysbWZwsqjaq2gGm8HJ2mYGJ4uqvYpmsBmcrG1mcLKo2qtoBpvBydpmBieLqr2KZrAZnKxtZnCyqNqraAabwcnaZgYni6q9imZwfxisxJRKKatg4vflVMAO4CX50CfPhvUi76Eix28XvsFKFeAU7yXB1WN9AviCiPvxvQjZoIxq8uZXelmneC8Jcp8A1jD8UIQ/nAccDMwFBLyymyr+lrwO5aQm/+PzIlxElvllJ+DeQXLwHNyvOfjI8PKXd6H8hguleLcD+NIq3gcHcPVqdUBZTBXU5dgItKJQEZfZAbzk+NyzOVhM1Xx7SoSG0NCr4KZO8V4e394ssjQUXx5zbPY5V0aG0w0CZP1cq+rdIruLFGHnqKw6yjw+sHgO7s8cPBFGTvFeksV9WkWX/ISpHzOD+8/gqVGcpIYBNsDJCuTLhmRRtVfRDDaDk7XNDE4WVXsVzWAzOFnbzOBkUbVX0Qw2g5O1zQxOFlV7Fc1gMzhZ28zgZFG1V9EMNoOTtc0MThZVexXNYDM4WdvM4GRRtVfRDDaDk7XNDE4WVXsVzWAzOFnbzOBkUbVX0Qw2g5O1zQxOFlV7Fc1gMzhZ28zgZFG1V9EM7heD5QQul5XHcipgB/CSfOiT4bvyB68Y3oRH5TKJ2wG8JLh6rE8AvxIQsEoTv2FkHVUewxvtAF4e4T4BnH3FVwPoH4VL6YV2AB8ugM8C/hkQwOsUdQAfJAovsvq1yMoDXNgB3B7+S6t4Xzz8s57lAdYcbAfw8iN0rxZZ2WecFMOyhmgVZwAfMoAHfY4dwEuC3MdVdMlPmfgxL7L6tcgywDVKwAwuKExfNhQUWBvVPUR7iE7WMzM4WVTtVTSDzeBkbTODk0XVXkUz2AxO1jYzOFlU7VU0g83gZG0zg5NF1V5FM9gMTtY2MzhZVO1VNIPN4GRtM4OTRdVeRTPYDE7WNjM4WVTtVTSDzeBkbTODk0XVXkUz2AxO1jYzOFlU7VU0g83gZG0zg5NF1V5FM9gMTtY2MzhZVJUrKt/hGsCj49LBL9WwGTz9GCxXlouA64HNgUOAMydSGQM8/QB+PzAbOACQi+mtwKuBhweBbICnH8CfG9YU77ssfGwzxlC8kmplBk+fNHfmldPVdWUhcHBEAVAiaQ3P+4nJgxzAl19++acWLVqkOXvkyuzZsx+aN2/ey/MfLoH1vewK3AZcDMhJXHPxRsCDVTquoXzBggW1fn8f26z1A6sIfJJntwX2jgzhawEXAOuOi61V+NV9BGOiNUUVRZwOAKuPySneU5E2wKmSaq/eysCi2AtXfqsBrizCfjegBdr8+fO1eKut9LHN6TBE1wbAKDY0qgC/BlgG+A3wQE3A19XmcsBEuDxRtK+jBrD2x+cA7wIUNvFe4GlgN+CZosKL+nW2qRO6myfox/1xoldoezhqAL8O2Av4B+ArwA7Ad+JnEwl2KtzrbtMMnkrik/xe+2ftqz8DnAFsH4HYjo8DlDJN19mmovBeCfxsQEc0Bbyx6AHPqDFYJ2FXAHcDWwJnA5vGwcmTZdCN07W62hR715xgDh5TSOgIoJ7c1VEDWIJ5ETAXWDUWWedWPRVroE317e9ye/7FsUZQ+OXfJ6M7yWqtSBvToe5KwDxAghpfxBoN1zpEKVKaaDN7v9p+W46tmwDbARrC8+kPpuzvqDC4CTCaaHMywMTefYB7pkQ1V2FUAM7LRNdpfxjs0BbpA8CXYrguIrsm29So8mfxAs29MwGFXv5b4M4inRw1gFeJvW9eRrIQkcVI2UVWE22+CvhlrpOPAPOBEwEBnlxGDWDtWd8KnA7sDnwW+EawWEIsU5poU/2QpcdLx62Tfle0g6MGsPasGpL3B64G3hTWITrVyoKSF5VhE21qGvk+8NpcZ3ySlYBMZqH5cWAbYEHsiSXIsgxuok2NClJE9a9SGTUG54WlM+S1Y04udL47icTralPbIdmd7VHhjPzZbo4iwGLuZjmQdPDxiYqGBHW3KYBlh6Zyfvyt+fidPqqcfEDTavm7gAz5stujZcOgr/BVXLyqiTY17OvIUts49U9F/dWFSKHV/qgxWHPbe8IMt9Lclnu4iTbr6tvIDdHyjLgWuBT4RVwU6FDh6KJnvDkEmmjTAJeUQDacahWdDdFaGMm4vuhZdNaFJtos+XlLPzZqQ7TyI24NHF6bBJ/LuVh3m7V1b9QAXh24KzwUbwopaoiWGU9ZBjfRpgEuKQFlF5c9lhRbwOr6UH+fVmEObqLNkp/nIbo2wY1rSCDLiECnYTpS/OlE7q1NdWCidkdtiG5Cvjo3/nGY/+hcWgcTMuzTvwvtWZvonAGuLlUBKfYeEX7MbwdOCGM+MbnTYoCri1+raN1O6fpR6XJ3DFNc/V93zZ0WA1xd/JKhtl375po6NlxeyxrTV+9VtGCAy4tSh/8y/ZEJ7mrhCvOS2G7JvLUXxQCXh0Fzr0xu5bssq8dj4mJAJjU6HdNiq+wFRvlejXvSAJcXpUDUnnqLuMb7fNhHq8Wq59vle2WAa5Nd1pDuk2VC++vaW66hQTO4BiH2uQkD3Gd0auibAa5BiH1uwgD3GZ0a+maAaxBin5swwMXR0RZIFwxPAb8t/ni7TxjgYvKeE/Zc2VPyhpBb5+3FmmmvtgFOl7WiA1wXhgG6OZIlx1ERckHAy8S1d8UAp0OSRcARuDKUV/kIILNZxbJeATgU2DmOKXX5IMtN1ZflpbwDZ8WlxJeBb6e/unxNA5wuOzH4GmD9sNpQ4BaZ314VZ9D6nc6eDwQ+BLwlLv1lwK7ffR14fbxug4qeFMm9NsDJonq2ojwO3g28N86h9TPd+er/Gr41J+tM+h3B5I3Di1E+RlIImfS8YZzvb7EeFKxtgNMFJkdvhV36JnBHPLYncBzwvghUrpslucZopa0/+rfqatg+NZivWNeZ31H620vWNMDpgsvMY3X/u1MEQ5EBvdi6YYRnUlC1g+J26VOAGCzWK+7VDTG8/yBiX7dir2WA0wFWTd37KiKAQhuraMiVZ6Gi0b8Z+Fbud/LvVfqByyOJiDwgdLWo2FyKtCfmN14McDkRy5JDRZlfdOCRFYVdeFkstgrFsyrXjamfMsBTy2ha1zDA0xq+qTtvgKeW0bSuYYCnNXxTd94ATy2jaV3DAE9r+KbuvAGeWkbTuoYBntbwTd35/wc13WbdNCWyeQAAAABJRU5ErkJggg==", + "text/plain": [ + "\n", + "\n", + "If you see this message, it means the renderer has not been properly enabled\n", + "for the frontend that you are using. For more information, see\n", + "https://altair-viz.github.io/user_guide/troubleshooting.html\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "No abnormalities found" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "CheckColumn(data, ['parch', 'sex'], bins=[15, False], missing=0.10, cardinality=25, floatFrequency=45, catFrequency=150)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/PlytoKeras.ipynb b/PlytoKeras.ipynb new file mode 100644 index 0000000..dc2c313 --- /dev/null +++ b/PlytoKeras.ipynb @@ -0,0 +1,346 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction to Plyto with Keras\n", + "#### Python Machine Learning Visualization Toolkit\n", + "This notebook will demonstrate how to use our Keras Accuracy Loss function with the PlytoAPI to visualize model accuracy and loss throughout the training process of a machine learning algorithm, as well as a tutorial on how to create your own callback function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + "\n", + "Plyto toolbar item opens the model visualizer for this notebook!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Using TensorFlow backend.\n" + ] + } + ], + "source": [ + "from plyto import PlytoAPI, KerasAccuracyLossCallback" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### How it works" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A callback function that takes a plyto instance as a parameter is passed as a callback to model.fit when using keras for machine learning. A plyto instance requires an Altair spec to define plots. Below is an example of a simple altair spec to create a line graph of samples versus accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# an array of altair specs with one plot of samples versus accuracy\n", + "spec = [\n", + " {\n", + " # specifies an altair spec\n", + " \"$schema\": \"https://vega.github.io/schema/vega-lite/v2.json\",\n", + " \"name\": \"accuracyGraph\",\n", + " \n", + " #size of the plot\n", + " \"config\": {\n", + " \"view\": {\n", + " \"height\": 300,\n", + " \"width\": 300\n", + " }\n", + " },\n", + " \n", + " # name of the dataset as defined in the backend of the callback function\n", + " \"data\": {\n", + " \"name\": \"dataSet\"\n", + " },\n", + " \n", + " # visual encodings of the plot\n", + " \"encoding\": {\n", + " \"x\": {\n", + " \"field\": \"samples\",\n", + " \"type\": \"quantitative\"\n", + " },\n", + " \"y\": {\n", + " \"field\": \"accuracy\",\n", + " \"type\": \"quantitative\"\n", + " }\n", + " },\n", + " \n", + " \n", + " \"mark\": \"line\"\n", + " }\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Running a model\n", + "To demonstrate how plyto works, we will be looking at the MNIST handwritten digit data, which can be loaded from keras.datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.datasets import mnist\n", + "from keras.models import Sequential\n", + "from keras.layers import Dense, Dropout, Activation, Flatten, Convolution2D, MaxPooling2D\n", + "from keras.utils import np_utils\n", + "\n", + "# Load pre-shuffled MNIST data into train and test sets\n", + "(X_train, y_train), (X_test, y_test) = mnist.load_data()\n", + " \n", + "# Preprocess input data\n", + "X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)\n", + "X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)\n", + "X_train = X_train.astype('float32')\n", + "X_test = X_test.astype('float32')\n", + "X_train /= 255\n", + "X_test /= 255\n", + " \n", + "# Preprocess class labels\n", + "Y_train = np_utils.to_categorical(y_train, 10)\n", + "Y_test = np_utils.to_categorical(y_test, 10)\n", + " \n", + "# Define model architecture\n", + "model = Sequential()\n", + " \n", + "model.add(Convolution2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)))\n", + "model.add(Convolution2D(32, (3, 3), activation='tanh'))\n", + "model.add(MaxPooling2D(pool_size=(2,2)))\n", + "model.add(Dropout(0.25))\n", + " \n", + "model.add(Flatten())\n", + "model.add(Dense(128, activation='relu'))\n", + "model.add(Dropout(0.5))\n", + "model.add(Dense(10, activation='tanh'))\n", + "\n", + "# Compile the model\n", + "model.compile(loss='categorical_crossentropy',\n", + " optimizer='adam',\n", + " metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However you fit your model, simply add the callback as a parameter and open the Plyto model visualizer to see your statistics and plots update." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pass the spec into PlytoAPI\n", + "plytoInstance = PlytoAPI(spec)\n", + "\n", + "# pass plyto instance to the callback function\n", + "callback = KerasAccuracyLossCallback(plytoInstance)\n", + "\n", + "model.fit(X_train, Y_train, batch_size=None, epochs=2, verbose=0, callbacks=[callback])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "*Note: if you are to stop and re-run the model, the plytoInstance and callback must be re-initialized. We recommend initializing them in the same cell as the call to model.fit() to ensure this works properly*" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Writing your own callback function" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A callback function for Plyto is a class that takes a keras callback as a parameter. \n", + "\n", + "Within this custom function, you can define functions to execute at specific points in running the model.\n", + "\n", + "Keras Callbacks\n", + "- on_epoch_begin: called at the beginning of every epoch.\n", + "- on_epoch_end: called at the end of every epoch.\n", + "- on_batch_begin: called at the beginning of every batch.\n", + "- on_batch_end: called at the end of every batch.\n", + "- on_train_begin: called at the beginning of model training.\n", + "- on_train_end: called at the end of model training.\n", + "\n", + "For more information on using callbacks, visit [keras callback documentation](https://keras.io/callbacks/).\n", + "\n", + "For the progress bars in the status bar to work correctly, your callback function must have epochs, sample_amount, total_progress, current_progress, mode, and epoch_number. Further, total_runtime and starttime is required for the panel to display the runtime once the model is complete. Below is a base to work off of, only containing these variables for basic functionality and passing no altair spec for plots." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from keras.callbacks import Callback\n", + "from time import time\n", + "\n", + "\n", + "class Basic(Callback):\n", + "\n", + " def __init__(self, plyto_instance):\n", + " self.epochs = None\n", + " self.sample_amount = 0\n", + " self.total_progress = 0\n", + " self.current_progress = 0\n", + " self.mode = 0\n", + " self.total_runtime = 0\n", + " self.starttime = time()\n", + " self.epoch_number = 1\n", + " self.plyto = plyto_instance\n", + "\n", + " def on_train_begin(self, logs={}):\n", + " \"\"\"\n", + " Get number of samples/steps per epoch and total number of epochs\n", + " \"\"\"\n", + " if \"samples\" in self.params:\n", + " self.sample_amount = self.params[\"samples\"]\n", + " elif \"nb_sample\" in self.params:\n", + " self.sample_amount = self.params[\"nb_sample\"]\n", + " else:\n", + " self.sample_amount = self.params[\"steps\"]\n", + " self.mode = 1\n", + "\n", + " self.epochs = self.params[\"epochs\"]\n", + " self.plyto.update_size(self.sample_amount)\n", + " self.plyto.update_total_steps(self.epochs)\n", + "\n", + " def on_epoch_begin(self, epoch, logs={}):\n", + " \"\"\"\n", + " Reset the current epoch's progress every new epoch\n", + " \"\"\"\n", + " self.current_progress = 0\n", + "\n", + " def on_epoch_end(self, epoch, logs={}):\n", + " self.epoch_number += 1\n", + " self.plyto.update_current_step(self.epoch_number)\n", + "\n", + " def on_batch_end(self, batch, logs={}):\n", + " \"\"\"\n", + " Update statistics and datasets\n", + " \"\"\"\n", + " if self.mode == 0:\n", + " self.current_progress += logs.get(\"size\")\n", + " self.total_progress += logs.get(\"size\")\n", + " else:\n", + " self.current_progress += 1\n", + " self.total_progress += 1\n", + " self.total_runtime = time() - self.starttime\n", + " self.plyto.update_runtime(self.total_runtime)\n", + " self.plyto.update_current_progress(self.current_progress)\n", + " self.plyto.update_total_progress(self.total_progress)\n", + " self.plyto.update_data_set(\n", + " {\n", + " \"samples\": self.total_progress,\n", + " }\n", + " )\n", + " if self.total_progress % 256 == 0 or self.total_progress == (self.epochs * self.sample_amount):\n", + " self.plyto.send_data()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "basicPlatoInstance = PlytoAPI([])\n", + "basicCallback = Basic(basicPlatoInstance)\n", + "model.fit(X_train, Y_train, batch_size=None, epochs=2, verbose=0, callbacks=[basicCallback])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Only variables in the data set can be used in the Altair plotting spec and will be displayed in the panel. The basic example only includes the variale \"samples\", as seen when updating the Plyto data set with\n", + "\n", + "```python\n", + "self.plyto.update_data_set({\n", + " \"samples\": self.total_progress\n", + "})\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/package.json b/package.json index 077d603..21ed3c9 100644 --- a/package.json +++ b/package.json @@ -26,6 +26,7 @@ "@jupyterlab/mainmenu": "^0.6.2", "@jupyterlab/notebook": "^0.17.2", "@jupyterlab/statusbar": "^0.2.0", + "pandas": "0.0.3", "typestyle": "^2.0.1", "vega": "^4.2.0", "vega-embed": "^3.15.0", diff --git a/plyto/__init__.py b/plyto/__init__.py index d4c7122..1e248fb 100644 --- a/plyto/__init__.py +++ b/plyto/__init__.py @@ -1,2 +1,3 @@ from .plyto import PlytoAPI -from .keras_accuracy_loss_callback import KerasAccuracyLossCallback \ No newline at end of file +from .keras_accuracy_loss_callback import KerasAccuracyLossCallback +from .checkData import CheckData, CheckColumn \ No newline at end of file diff --git a/plyto/checkData.py b/plyto/checkData.py new file mode 100644 index 0000000..811d5d0 --- /dev/null +++ b/plyto/checkData.py @@ -0,0 +1,178 @@ +import altair as alt +import pandas as pd +from IPython.display import display, Markdown + +def Quartile(data, col): + q1 = data[col].quantile(.25) + q3 = data[col].quantile(.75) + iqr = q3 - q1 + upperLim = q3 + 1.5*iqr + lowerLim = q1 - 1.5*iqr + return (lowerLim, upperLim) + +def CheckData(data, columns=[], missing=0.1, cardinality=15, floatFrequency=30, catFrequency=100, outlierFunction=Quartile, title=True): + """ + Checks all columns of the given pandas dataframe for data abnormalities + :param columns: a list of column names to analyze, defaults to all columns in the dataframe + :param missing: a cutoff point for high percentage of missing / zero values, defaults to 10% + :param cardinality: a cutoff point for high cardinality of a categorical column, defaults to 15 + :param floatFrequency: a cutoff point for high frequency of floating point numbers, defaults to 30 + :param catFrequency: a cutoff point for low frequency of categories in categorical columns, defaults to 100 + :param outlierFunction: a function of the dataset and column name that returns the lower and upper limit for outliers, + defaults to 1.5*IQR above the 3rd quartile or below the 1st quartile + """ + + if columns == []: + columns = data.columns + abnormalities = {} + + for col in columns: + abnormalities[col] = [] + + """ + Check for high proportion of zeros or missing values + """ + propZero = sum(data[col] == 0)/len(data) + if propZero > missing: + ab = "high proportion of zero values at " + str(round(propZero*100, 2)) + "%" + abnormalities[col].append(ab) + + propNull = sum(data[col].isnull())/len(data) + if propNull > missing: + ab = "high proportion of null values at " + str(round(propNull*100, 2)) + "%" + abnormalities[col].append(ab) + + """ + Check for high cardinality of categorical variables + """ + if data[col].dtype == 'O': + numUnique = len(data[col].unique()) + if numUnique > cardinality: + ab = "high cardinality with " + str(numUnique) + " unique values" + abnormalities[col].append(ab) + + """ + Check for high frequency of floating point columns + """ + if data[col].dtype == 'float64': + numHighFreq = sum(data.groupby(col)[col].count().values > floatFrequency) + if numHighFreq > 0: + ab = "high frequency of floating point numbers with " + str(numHighFreq) + " number(s) having frequency over " + str(floatFrequency) + abnormalities[col].append(ab) + + """ + Check for low frequency of categories of categorical variables + """ + if data[col].dtype == 'O': + lowFreqCount = 0 + for val in data.groupby(col)[col].count().values: + if val < catFrequency: + lowFreqCount += 1 + if lowFreqCount > 0: + ab = "low frequncy in categories with " + str(lowFreqCount) + " categorie(s) having under " + str(catFrequency) + " observations" + abnormalities[col].append(ab) + + """ + Check for outliers in quantitative data + """ + if data[col].dtype == 'float64': + lowerLim, upperLim = outlierFunction(data, col) + numUpperOutliers = sum(data[col] > upperLim) + numLowerOutliers = sum(data[col] < lowerLim) + if numUpperOutliers > 10 or numLowerOutliers > 10: + ab = "high number of outliers with " + str(numUpperOutliers) + " high outliers and " + str(numLowerOutliers) + " low outliers" + abnormalities[col].append(ab) + + has_abnormalities = False + longestCol = 0 + for col in columns: + if len(col) > longestCol: + longestCol = len(col) + + for col in abnormalities: + if len(abnormalities[col]) > 0: + abs = '' + for ab in abnormalities[col]: + abs += '\n- ' + ab + if title: + display(Markdown(col + ": " + abs)) + else: + display(Markdown(abs)) + has_abnormalities = True + + + if not has_abnormalities: + display(Markdown("No abnormalities found")) + + +def CheckColumn(data, columns, bins=False, missing=0.1, cardinality=15, floatFrequency=30, catFrequency=100, outlierFunction=Quartile): + """ + Presents a summary of given columns of the given pandas dataframe + including summary statistics, bar chart or histogram, and any abnormalities found + + :param data: a pandas dataframe + :param columns: a single column name or a list of column names to analyze + :param bins: a boolean value or list of boolean values to determine whether to bin the histogram for each column + :param missing: a cutoff point for high percentage of missing / zero values, defaults to 10% + :param cardinality: a cutoff point for high cardinality of a categorical column, defaults to 15 + :param floatFrequency: a cutoff point for high frequency of floating point numbers, defaults to 30 + :param catFrequency: a cutoff point for low frequency of categories in categorical columns, defaults to 100 + :param outlierFunction: a function of the dataset and column name that returns the lower and upper limit for outliers, + defaults to 1.5*IQR above the 3rd quartile or below the 1st quartile + """ + + if isinstance(columns, str): + # with only one column, convert to lists + columns = [columns] + bins = [bins] + else: + if bins == False: + # with multiple columns and no bins, + # convert to list of correct length + bins = [False]*len(columns) + + if isinstance(bins, int): + # with multiple columns and only one bin + # specification, convert to list of correct length + bins = [bins]*len(columns) + + i = 0 + for col in columns: + bin = bins[i] + + if data[col].dtype == 'O': + # cannot bin categorical data + bin = False + + if bin == False: + if data[col].dtype == 'O': + chart = alt.Chart(data).mark_bar(color='#64b5f6').encode( + alt.X(col, axis=alt.Axis(title=col.title()), sort = alt.SortField(field = 'count()', order = 'descending', op='values')), + alt.Y("count()") + ) + else: + chart = alt.Chart(data).mark_bar(color='#64b5f6').encode( + alt.X(col, axis=alt.Axis(title=col.title())), + alt.Y("count()") + ) + else: + chart = alt.Chart(data).mark_bar(color='#64b5f6').encode( + alt.X(col, bin=alt.Bin(maxbins = bin), axis=alt.Axis(title=col.title())), + alt.Y("count()") + ) + + if (data[col].dtype == "float64"): + stats = data[col].describe() + else: + stats = data.groupby(col)[col].agg(['count']) + stats['prop'] = stats['count']/len(data) + + stats = pd.DataFrame(stats).T + + display(Markdown('###### Column Summary: ' + col.title())) + display(stats) + display(chart) + CheckData(data, [col], missing=missing, cardinality=cardinality, floatFrequency=floatFrequency, catFrequency=catFrequency, outlierFunction=outlierFunction, title=False) + + i+=1 + \ No newline at end of file diff --git a/src/componentStyle/StatusStyle.tsx b/src/componentStyle/StatusStyle.tsx index 534054b..90f65ad 100644 --- a/src/componentStyle/StatusStyle.tsx +++ b/src/componentStyle/StatusStyle.tsx @@ -28,7 +28,7 @@ export function progressBarStyle(stat: number) { return style({ width: '42px', backgroundColor: 'var(--jp-content-font-color0)', - borderRadius: '5px', + borderRadius: '4px', height: '8px', marginTop: '3px', @@ -36,7 +36,7 @@ export function progressBarStyle(stat: number) { '& .progress': { width: stat * 0.4, backgroundColor: 'var(--jp-brand-color2)', - height: '5px', + height: '6px', borderRadius: '3px 0px 0px 3px', marginTop: '1.5px', marginLeft: '1px' @@ -47,7 +47,7 @@ export function progressBarStyle(stat: number) { return style({ width: '42px', backgroundColor: 'var(--jp-content-font-color0)', - borderRadius: '5px', + borderRadius: '4px', height: '8px', marginTop: '3px', @@ -55,7 +55,7 @@ export function progressBarStyle(stat: number) { '& .progress': { width: stat * 0.4, backgroundColor: 'var(--jp-brand-color2)', - height: '5px', + height: '6px', borderRadius: '3px', marginTop: '1.5px', marginLeft: '1px' diff --git a/src/index.tsx b/src/index.tsx index bb749e2..210fa78 100644 --- a/src/index.tsx +++ b/src/index.tsx @@ -21,7 +21,7 @@ const extension: JupyterLabPlugin = { tracker: INotebookTracker, statusBar: IStatusBar ): void => { - console.log('test2') + console.log('test3') function hasKernel(): boolean { return ( diff --git a/titanic3.csv b/titanic3.csv new file mode 100644 index 0000000..db56673 --- /dev/null +++ b/titanic3.csv @@ -0,0 +1,1311 @@ +pclass,survived,name,sex,age,sibsp,parch,ticket,fare,cabin,embarked,boat,body,home.dest +1,1,"Allen, Miss. Elisabeth Walton",female,29,0,0,24160,211.3375,B5,S,2,,"St Louis, MO" +1,1,"Allison, Master. Hudson Trevor",male,0.9167,1,2,113781,151.5500,C22 C26,S,11,,"Montreal, PQ / Chesterville, ON" +1,0,"Allison, Miss. Helen Loraine",female,2,1,2,113781,151.5500,C22 C26,S,,,"Montreal, PQ / Chesterville, ON" +1,0,"Allison, Mr. Hudson Joshua Creighton",male,30,1,2,113781,151.5500,C22 C26,S,,135,"Montreal, PQ / Chesterville, ON" +1,0,"Allison, Mrs. Hudson J C (Bessie Waldo Daniels)",female,25,1,2,113781,151.5500,C22 C26,S,,,"Montreal, PQ / Chesterville, ON" +1,1,"Anderson, Mr. Harry",male,48,0,0,19952,26.5500,E12,S,3,,"New York, NY" +1,1,"Andrews, Miss. Kornelia Theodosia",female,63,1,0,13502,77.9583,D7,S,10,,"Hudson, NY" +1,0,"Andrews, Mr. Thomas Jr",male,39,0,0,112050,0.0000,A36,S,,,"Belfast, NI" +1,1,"Appleton, Mrs. Edward Dale (Charlotte Lamson)",female,53,2,0,11769,51.4792,C101,S,D,,"Bayside, Queens, NY" +1,0,"Artagaveytia, Mr. Ramon",male,71,0,0,PC 17609,49.5042,,C,,22,"Montevideo, Uruguay" +1,0,"Astor, Col. John Jacob",male,47,1,0,PC 17757,227.5250,C62 C64,C,,124,"New York, NY" +1,1,"Astor, Mrs. John Jacob (Madeleine Talmadge Force)",female,18,1,0,PC 17757,227.5250,C62 C64,C,4,,"New York, NY" +1,1,"Aubart, Mme. Leontine Pauline",female,24,0,0,PC 17477,69.3000,B35,C,9,,"Paris, France" +1,1,"Barber, Miss. Ellen ""Nellie""",female,26,0,0,19877,78.8500,,S,6,, +1,1,"Barkworth, Mr. Algernon Henry Wilson",male,80,0,0,27042,30.0000,A23,S,B,,"Hessle, Yorks" +1,0,"Baumann, Mr. John D",male,,0,0,PC 17318,25.9250,,S,,,"New York, NY" +1,0,"Baxter, Mr. Quigg Edmond",male,24,0,1,PC 17558,247.5208,B58 B60,C,,,"Montreal, PQ" +1,1,"Baxter, Mrs. James (Helene DeLaudeniere Chaput)",female,50,0,1,PC 17558,247.5208,B58 B60,C,6,,"Montreal, PQ" +1,1,"Bazzani, Miss. Albina",female,32,0,0,11813,76.2917,D15,C,8,, +1,0,"Beattie, Mr. Thomson",male,36,0,0,13050,75.2417,C6,C,A,,"Winnipeg, MN" +1,1,"Beckwith, Mr. Richard Leonard",male,37,1,1,11751,52.5542,D35,S,5,,"New York, NY" +1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47,1,1,11751,52.5542,D35,S,5,,"New York, NY" +1,1,"Behr, Mr. Karl Howell",male,26,0,0,111369,30.0000,C148,C,5,,"New York, NY" +1,1,"Bidois, Miss. Rosalie",female,42,0,0,PC 17757,227.5250,,C,4,, +1,1,"Bird, Miss. Ellen",female,29,0,0,PC 17483,221.7792,C97,S,8,, +1,0,"Birnbaum, Mr. Jakob",male,25,0,0,13905,26.0000,,C,,148,"San Francisco, CA" +1,1,"Bishop, Mr. Dickinson H",male,25,1,0,11967,91.0792,B49,C,7,,"Dowagiac, MI" +1,1,"Bishop, Mrs. Dickinson H (Helen Walton)",female,19,1,0,11967,91.0792,B49,C,7,,"Dowagiac, MI" +1,1,"Bissette, Miss. Amelia",female,35,0,0,PC 17760,135.6333,C99,S,8,, +1,1,"Bjornstrom-Steffansson, Mr. Mauritz Hakan",male,28,0,0,110564,26.5500,C52,S,D,,"Stockholm, Sweden / Washington, DC" +1,0,"Blackwell, Mr. Stephen Weart",male,45,0,0,113784,35.5000,T,S,,,"Trenton, NJ" +1,1,"Blank, Mr. Henry",male,40,0,0,112277,31.0000,A31,C,7,,"Glen Ridge, NJ" +1,1,"Bonnell, Miss. Caroline",female,30,0,0,36928,164.8667,C7,S,8,,"Youngstown, OH" +1,1,"Bonnell, Miss. Elizabeth",female,58,0,0,113783,26.5500,C103,S,8,,"Birkdale, England Cleveland, Ohio" +1,0,"Borebank, Mr. John James",male,42,0,0,110489,26.5500,D22,S,,,"London / Winnipeg, MB" +1,1,"Bowen, Miss. 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