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muellerzr/debuggingtabularissue.ipynb

Created November 7, 2020 05:18
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DebuggingTabularIssue.ipynb
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debuggingtabularissue.ipynb
{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "DebuggingTabularIssue.ipynb",
"provenance": [],
"collapsed_sections": [],
"authorship_tag": "ABX9TyPLPsJRdI6vngHgslSY2/K4",
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/muellerzr/c126e75b0265f88c3baa1ea50a4dfe6b/debuggingtabularissue.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "OFjBvIDJGjBz"
},
"source": [
"First install the dev versions:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "uGmiTzBYwiDb",
"outputId": "2b0dae90-687f-4d69-8277-80e1b2bb94d8",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"!pip install git+https://github.com/fastai/fastai -qqq\n",
"!pip install git+https://github.com/fastai/fastcore -qqq"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
" Building wheel for fastai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
" Building wheel for fastcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "T2TgGzKAGmtz"
},
"source": [
"Next we'll import the library:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "g7G6d684mM1x"
},
"source": [
"from fastai.tabular.all import *"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "8XxG8jkTnN7s"
},
"source": [
"We will download the `ADULT_SAMPLE` dataset and load it into `Pandas`:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "RoyiwS9CnK-l"
},
"source": [
"path = untar_data(URLs.ADULT_SAMPLE)\n",
"df = pd.read_csv(path/'adult.csv')"
],
"execution_count": 3,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "grxUf3JHvB1f"
},
"source": [
"cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race']\n",
"cont_names = ['age', 'fnlwgt', 'education-num']\n",
"procs = [Categorify, FillMissing, Normalize]\n",
"y_names = 'salary'\n",
"y_block = CategoryBlock()"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tWbwa_8iy8Sa"
},
"source": [
"splits = RandomSplitter()(range_of(df))"
],
"execution_count": 5,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "f5MHZdbfnZKp"
},
"source": [
"to = TabularPandas(df, procs=procs, cat_names=cat_names, cont_names=cont_names,\n",
" y_names=y_names, y_block=y_block, splits=splits)"
],
"execution_count": 6,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "WqxzBYEHttNx"
},
"source": [
"dls = to.dataloaders(bs=200)\n",
"learn = tabular_learner(dls, layers=[200,100])"
],
"execution_count": 7,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "ARODGoeXGtgl"
},
"source": [
"Next we'll export the learner:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "IVckiWKat2KT"
},
"source": [
"learn.export(\"testing\")"
],
"execution_count": 8,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "D208e_aHGz6l"
},
"source": [
"And force a reboot:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "EiDMTZGw0fN0"
},
"source": [
"exit()"
],
"execution_count": 9,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "6jBsmTp6HJTF"
},
"source": [
"Next let's import fastai and our `muppy` helper. \n",
"\n",
"Now **at this point** we have zero references to a `DataFrame`, we can verify with `muppy`:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "YZsM6_J2HSdY"
},
"source": [
"from fastai.tabular.all import *\n",
"from pympler import muppy"
],
"execution_count": 2,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "RR_2KPirHQzp",
"outputId": "d1826b04-9e35-40d3-982b-f329268a748a",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"all_objects = muppy.get_objects()\n",
"my_types = muppy.filter(all_objects, Type=pd.DataFrame)\n",
"len(my_types)"
],
"execution_count": 3,
"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": [
"0"
]
},
"metadata": {
"tags": []
},
"execution_count": 3
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "goP3S3DAHT2O"
},
"source": [
"Let's try loading in our learner and see what happens:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Eaxl0nTyEMg7"
},
"source": [
"learn = load_learner('testing')"
],
"execution_count": 4,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "1ECTynCc-0fG",
"outputId": "1d734456-b3a7-40f2-e983-74b8b9d07015",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"all_objects = muppy.get_objects()\n",
"my_types = muppy.filter(all_objects, Type=pd.DataFrame)\n",
"len(my_types)"
],
"execution_count": 5,
"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": 5
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "MbBXuu5QHY2P"
},
"source": [
"Suddenly we have five! In actuality we should only have really 2, our `train` (blank) and our `valid` (blank). We can also look at their values:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "dD6uzTkuEg_j",
"outputId": "aa1235fc-f0e4-4e84-d71b-84f997b2af81",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 101
}
},
"source": [
"my_types[0].head()"
],
"execution_count": 10,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education-num</th>\n",
" <th>marital-status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>sex</th>\n",
" <th>capital-gain</th>\n",
" <th>capital-loss</th>\n",
" <th>hours-per-week</th>\n",
" <th>native-country</th>\n",
" <th>salary</th>\n",
" <th>education-num_na</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, native-country, salary, education-num_na]\n",
"Index: []"
]
},
"metadata": {
"tags": []
},
"execution_count": 10
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dMsUWGprHktJ",
"outputId": "a39e4861-fc5d-431e-c090-cda97d5e356c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 101
}
},
"source": [
"my_types[1].head()"
],
"execution_count": 11,
"outputs": [
{
"output_type": "execute_result",
"data": {
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"<div>\n",
"<style scoped>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education-num</th>\n",
" <th>marital-status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>sex</th>\n",
" <th>capital-gain</th>\n",
" <th>capital-loss</th>\n",
" <th>hours-per-week</th>\n",
" <th>native-country</th>\n",
" <th>salary</th>\n",
" <th>education-num_na</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"Empty DataFrame\n",
"Columns: [age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, native-country, salary, education-num_na]\n",
"Index: []"
]
},
"metadata": {
"tags": []
},
"execution_count": 11
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "UnPVbKJcHmAd",
"outputId": "31e132bb-c19a-4dee-9b0e-ab2b939f194e",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 299
}
},
"source": [
"my_types[2].head()"
],
"execution_count": 12,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>age</th>\n",
" <th>workclass</th>\n",
" <th>fnlwgt</th>\n",
" <th>education</th>\n",
" <th>education-num</th>\n",
" <th>marital-status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>sex</th>\n",
" <th>capital-gain</th>\n",
" <th>capital-loss</th>\n",
" <th>hours-per-week</th>\n",
" <th>native-country</th>\n",
" <th>salary</th>\n",
" <th>education-num_na</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>19109</th>\n",
" <td>-0.626635</td>\n",
" <td>5</td>\n",
" <td>1.069460</td>\n",
" <td>12</td>\n",
" <td>-0.424470</td>\n",
" <td>5</td>\n",
" <td>8</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22624</th>\n",
" <td>-1.434871</td>\n",
" <td>1</td>\n",
" <td>-1.257606</td>\n",
" <td>16</td>\n",
" <td>-0.030491</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>30</td>\n",
" <td>Japan</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29269</th>\n",
" <td>0.034649</td>\n",
" <td>5</td>\n",
" <td>-0.333725</td>\n",
" <td>10</td>\n",
" <td>1.151445</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>United-States</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16316</th>\n",
" <td>0.181601</td>\n",
" <td>5</td>\n",
" <td>-0.696871</td>\n",
" <td>10</td>\n",
" <td>1.151445</td>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>Germany</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21530</th>\n",
" <td>-0.112303</td>\n",
" <td>3</td>\n",
" <td>-0.586142</td>\n",
" <td>16</td>\n",
" <td>-0.030491</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt ... native-country salary education-num_na\n",
"19109 -0.626635 5 1.069460 ... United-States 0 1\n",
"22624 -1.434871 1 -1.257606 ... Japan 0 1\n",
"29269 0.034649 5 -0.333725 ... United-States 1 1\n",
"16316 0.181601 5 -0.696871 ... Germany 0 1\n",
"21530 -0.112303 3 -0.586142 ... United-States 0 1\n",
"\n",
"[5 rows x 16 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 12
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QubxTc1CHp7h",
"outputId": "bff79dc5-a652-4639-b8bd-033529ff0988",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 101
}
},
"source": [
"my_types[3].head()"
],
"execution_count": 13,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
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" <th>fnlwgt</th>\n",
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" <th>education-num</th>\n",
" <th>marital-status</th>\n",
" <th>occupation</th>\n",
" <th>relationship</th>\n",
" <th>race</th>\n",
" <th>sex</th>\n",
" <th>capital-gain</th>\n",
" <th>capital-loss</th>\n",
" <th>hours-per-week</th>\n",
" <th>native-country</th>\n",
" <th>salary</th>\n",
" <th>education-num_na</th>\n",
" </tr>\n",
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],
"text/plain": [
"Empty DataFrame\n",
"Columns: [age, workclass, fnlwgt, education, education-num, marital-status, occupation, relationship, race, sex, capital-gain, capital-loss, hours-per-week, native-country, salary, education-num_na]\n",
"Index: []"
]
},
"metadata": {
"tags": []
},
"execution_count": 13
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "0UXh4qucHr0j",
"outputId": "fd210efc-65a1-4f28-ad83-b9e8888e6ccc",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 316
}
},
"source": [
"my_types[4].head()"
],
"execution_count": 14,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
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" <th>education-num</th>\n",
" <th>marital-status</th>\n",
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" <th>relationship</th>\n",
" <th>race</th>\n",
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" <th>capital-gain</th>\n",
" <th>capital-loss</th>\n",
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" <th>native-country</th>\n",
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" <tr>\n",
" <th>24346</th>\n",
" <td>1.283741</td>\n",
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" <td>6</td>\n",
" <td>5</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>1977</td>\n",
" <td>50</td>\n",
" <td>United-States</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
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" <tr>\n",
" <th>12537</th>\n",
" <td>0.108125</td>\n",
" <td>8</td>\n",
" <td>-0.069612</td>\n",
" <td>13</td>\n",
" <td>1.545424</td>\n",
" <td>3</td>\n",
" <td>11</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>38</td>\n",
" <td>China</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5036</th>\n",
" <td>0.475505</td>\n",
" <td>5</td>\n",
" <td>-1.477885</td>\n",
" <td>10</td>\n",
" <td>1.151445</td>\n",
" <td>3</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>Male</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>50</td>\n",
" <td>United-States</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21718</th>\n",
" <td>-1.140967</td>\n",
" <td>5</td>\n",
" <td>-0.146010</td>\n",
" <td>10</td>\n",
" <td>1.151445</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>5</td>\n",
" <td>Female</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>40</td>\n",
" <td>United-States</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21744</th>\n",
" <td>-0.479683</td>\n",
" <td>5</td>\n",
" <td>0.307879</td>\n",
" <td>9</td>\n",
" <td>0.363488</td>\n",
" <td>3</td>\n",
" <td>11</td>\n",
" <td>1</td>\n",
" <td>5</td>\n",
" <td>Male</td>\n",
" <td>7298</td>\n",
" <td>0</td>\n",
" <td>42</td>\n",
" <td>United-States</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" age workclass fnlwgt ... native-country salary education-num_na\n",
"24346 1.283741 7 -1.345207 ... United-States 1 1\n",
"12537 0.108125 8 -0.069612 ... China 1 1\n",
"5036 0.475505 5 -1.477885 ... United-States 1 1\n",
"21718 -1.140967 5 -0.146010 ... United-States 0 1\n",
"21744 -0.479683 5 0.307879 ... United-States 1 1\n",
"\n",
"[5 rows x 16 columns]"
]
},
"metadata": {
"tags": []
},
"execution_count": 14
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Hgc_7RoCHges"
},
"source": [
"And we can see a copy of our train and validation dataframes, *not* what we want!\n",
"\n",
"The issue is I don't know where these originated from. When you try to investigate the size of the `DataLoader` with:"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iWZd-gPyFYGE"
},
"source": [
"from pympler import asizeof"
],
"execution_count": 16,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "t9bX-9k4IAG6",
"outputId": "c8c08547-ff79-403c-f884-63897106a9ae",
"colab": {
"base_uri": "https://localhost:8080/"
}
},
"source": [
"print(asizeof.asized(learn, detail=1).format())"
],
"execution_count": 17,
"outputs": [
{
"output_type": "stream",
"text": [
"<fastai.tabular.learner.TabularLearner object at 0x7f24cfcb8390> size=258032 flat=56\n",
" __dict__ size=257976 flat=1184\n",
" __class__ size=0 flat=0\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TkboSoFfIDKF"
},
"source": [
"You can see it's only 250,000 bytes. That doesn't add up to the ~2.1 MB our exported model is (that's only ~3%). Any help would be appreciated"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NxIOVIOkIyXn"
},
"source": [
"> Note: If you decide to investigate `locals()` make sure to restart the runtime as `muppy` will show it's intermediate results"
]
}
]
}
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