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muellerzr/tabular-export-learner-testing.ipynb

Created November 12, 2020 15:30
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tabular-export-learner-testing.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"accelerator": "GPU",
"colab": {
"name": "tabular-export-learner-testing.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"jupytext": {
"split_at_heading": true
},
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/muellerzr/df3fc4a12b021be85639afddab3c5d32/tabular-export-learner-testing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "XrJzkfJ-1bKp"
},
"source": [
"# Tabular models"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Lu4U4bzjv0oH",
"outputId": "4ad0e0bf-7db1-4f47-b8fc-90bcec46dea7",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"!pip install fastai --upgrade -qqq\n",
"!pip install pympler"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 194kB 5.7MB/s \n",
"\u001b[K |████████████████████████████████| 51kB 5.5MB/s \n",
"\u001b[?25h"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "_kw8-Dju_6J5",
"outputId": "52b5073f-77be-4778-aed2-6eb1cfeb04fd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 35
}
},
"source": [
"import fastai\n",
"fastai.__version__"
],
"execution_count": 1,
"outputs": [
{
"output_type": "execute_result",
"data": {
"application/vnd.google.colaboratory.intrinsic+json": {
"type": "string"
},
"text/plain": [
"'2.1.5'"
]
},
"metadata": {
"tags": []
},
"execution_count": 1
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "K-NLPMGM1bKr"
},
"source": [
"from fastai.tabular.all import *"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "dzHkM_t41bKx"
},
"source": [
"Tabular data should be in a Pandas `DataFrame`."
]
},
{
"cell_type": "code",
"metadata": {
"id": "xLLnzxw_1bKy"
},
"source": [
"path = untar_data(URLs.ADULT_SAMPLE)\n",
"df = pd.read_csv(path/'adult.csv')\n",
"for i in range(4):\n",
" df = df.append(df.copy())"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "TBoo6c761bK2"
},
"source": [
"dep_var = 'salary'\n",
"cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']\n",
"cont_names = ['age', 'fnlwgt', 'education-num']\n",
"procs = [Categorify, FillMissing, Normalize]"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZNlr9V4H1bK_"
},
"source": [
"splits = RandomSplitter()(range_of(df))"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "2ERqyHkxE7sq",
"outputId": "77c557b4-b2d5-4d5b-a0b2-1e12f71b60cc",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"splits"
],
"execution_count": 6,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"((#416781) [307989,75517,414193,363282,349727,241733,40196,389451,329687,91195...],\n",
" (#104195) [406723,268985,333518,493941,351485,213666,430108,31256,68008,294457...])"
]
},
"metadata": {
"tags": []
},
"execution_count": 6
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cTd_Ckt61bLH"
},
"source": [
"to = TabularPandas(df, procs, cat_names, cont_names, y_names=\"salary\", splits=splits)"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "7UvARddj1bLN"
},
"source": [
"dls = to.dataloaders(bs=128)"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "Ucg9ao3dEpbF"
},
"source": [
"First we'll check before exporting:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "KiNZuAIrC4m6",
"outputId": "85446930-4508-4935-d43c-3e23491b3081",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"from pympler import muppy\n",
"all_objects = muppy.get_objects()\n",
"my_types = muppy.filter(all_objects, Type=pd.DataFrame)\n",
"len(my_types)"
],
"execution_count": 8,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/torch/distributed/distributed_c10d.py:126: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead\n",
" warnings.warn(\"torch.distributed.reduce_op is deprecated, please use \"\n"
],
"name": "stderr"
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"5"
]
},
"metadata": {
"tags": []
},
"execution_count": 8
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "N8vdsac4DPdM",
"outputId": "647eb163-596f-4531-ce89-d7970cae91c4",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"for t in my_types:\n",
" print(len(t))"
],
"execution_count": 9,
"outputs": [
{
"output_type": "stream",
"text": [
"520976\n",
"0\n",
"104195\n",
"520976\n",
"416781\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "bRYiL1gZ1bLZ"
},
"source": [
"learn = tabular_learner(dls, layers=[200,100], metrics=accuracy)"
],
"execution_count": 10,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "O_x8daKB1DqB"
},
"source": [
"learn.export('t1')"
],
"execution_count": 11,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "i7En8TFR1FPN"
},
"source": [
"l1 = load_learner('t1')"
],
"execution_count": 12,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "06Uyq54OwdOj",
"outputId": "1fd7e30f-0748-4c5e-9520-227d8de369be",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 77
}
},
"source": [
"learn.fit(1)"
],
"execution_count": 13,
"outputs": [
{
"output_type": "display_data",
"data": {
"text/html": [
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: left;\">\n",
" <th>epoch</th>\n",
" <th>train_loss</th>\n",
" <th>valid_loss</th>\n",
" <th>accuracy</th>\n",
" <th>time</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.331344</td>\n",
" <td>0.325066</td>\n",
" <td>0.847334</td>\n",
" <td>01:00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {
"tags": []
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "DUPAf9D1z-Pu"
},
"source": [
"learn.export('t2')"
],
"execution_count": 14,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "64IiOmQuEuxV"
},
"source": [
"And then we'll check t2 (what we just exported):"
]
},
{
"cell_type": "code",
"metadata": {
"id": "UCEOGECvz_jA",
"outputId": "8636eb57-3d9e-43fe-f14a-8af748449b42",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"from fastai.tabular.all import *\n",
"from pympler import muppy\n",
"l2 = load_learner('t2')\n",
"\n",
"all_objects = muppy.get_objects()\n",
"my_types = muppy.filter(all_objects, Type=pd.DataFrame)\n",
"for t in my_types:\n",
" print(len(t))"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/torch/distributed/distributed_c10d.py:126: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead\n",
" warnings.warn(\"torch.distributed.reduce_op is deprecated, please use \"\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"0\n",
"0\n",
"104195\n",
"0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "zdX_t_XqExMD"
},
"source": [
"And t1 (exported prior to training):"
]
},
{
"cell_type": "code",
"metadata": {
"id": "kBwPMhK8DeTl",
"outputId": "d77b575f-c9e3-4798-fe3c-8492bb106a0c",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"from fastai.tabular.all import *\n",
"from pympler import muppy\n",
"l2 = load_learner('t1')\n",
"\n",
"all_objects = muppy.get_objects()\n",
"my_types = muppy.filter(all_objects, Type=pd.DataFrame)\n",
"for t in my_types:\n",
" print(len(t))"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"/usr/local/lib/python3.6/dist-packages/torch/distributed/distributed_c10d.py:126: UserWarning: torch.distributed.reduce_op is deprecated, please use torch.distributed.ReduceOp instead\n",
" warnings.warn(\"torch.distributed.reduce_op is deprecated, please use \"\n"
],
"name": "stderr"
},
{
"output_type": "stream",
"text": [
"0\n",
"0\n",
"0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "U5qaQvxXE1ax"
},
"source": [
"We can see that the exported version had a copy of the validation dataset somewhere"
]
},
{
"cell_type": "code",
"metadata": {
"id": "ptg9TfCBE4U7"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}
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