{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example: Train sizing\n", "-----------------------\n", "\n", "This example shows how to asses a model's performance based on the size of the training set.\n", "\n", "The data used is a variation on the [Australian weather dataset](https://www.kaggle.com/jsphyg/weather-dataset-rattle-package) from Kaggle. You can download it from [here](https://github.com/tvdboom/ATOM/blob/master/examples/datasets/weatherAUS.csv). The goal of this dataset is to predict whether or not it will rain tomorrow training a binary classifier on target `RainTomorrow`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Load the data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Import packages\n", "import pandas as pd\n", "from atom import ATOMClassifier" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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LocationMinTempMaxTempRainfallEvaporationSunshineWindGustDirWindGustSpeedWindDir9amWindDir3pm...Humidity9amHumidity3pmPressure9amPressure3pmCloud9amCloud3pmTemp9amTemp3pmRainTodayRainTomorrow
0MelbourneAirport18.026.921.47.08.9SSE41.0WSSE...95.054.01019.51017.08.05.018.526.0Yes0
1Adelaide17.223.40.0NaNNaNS41.0SWSW...59.036.01015.71015.7NaNNaN17.721.9No0
2Cairns18.624.67.43.06.1SSE54.0SSESE...78.057.01018.71016.63.03.020.824.1Yes0
3Portland13.616.84.21.20.0ESE39.0ESEESE...76.074.01021.41020.57.08.015.616.0Yes1
4Walpole16.419.90.0NaNNaNSE44.0SESE...78.070.01019.41018.9NaNNaN17.418.1No0
\n", "

5 rows × 22 columns

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" ], "text/plain": [ " Location MinTemp MaxTemp Rainfall Evaporation Sunshine \\\n", "0 MelbourneAirport 18.0 26.9 21.4 7.0 8.9 \n", "1 Adelaide 17.2 23.4 0.0 NaN NaN \n", "2 Cairns 18.6 24.6 7.4 3.0 6.1 \n", "3 Portland 13.6 16.8 4.2 1.2 0.0 \n", "4 Walpole 16.4 19.9 0.0 NaN NaN \n", "\n", " WindGustDir WindGustSpeed WindDir9am WindDir3pm ... Humidity9am \\\n", "0 SSE 41.0 W SSE ... 95.0 \n", "1 S 41.0 S WSW ... 59.0 \n", "2 SSE 54.0 SSE SE ... 78.0 \n", "3 ESE 39.0 ESE ESE ... 76.0 \n", "4 SE 44.0 SE SE ... 78.0 \n", "\n", " Humidity3pm Pressure9am Pressure3pm Cloud9am Cloud3pm Temp9am \\\n", "0 54.0 1019.5 1017.0 8.0 5.0 18.5 \n", "1 36.0 1015.7 1015.7 NaN NaN 17.7 \n", "2 57.0 1018.7 1016.6 3.0 3.0 20.8 \n", "3 74.0 1021.4 1020.5 7.0 8.0 15.6 \n", "4 70.0 1019.4 1018.9 NaN NaN 17.4 \n", "\n", " Temp3pm RainToday RainTomorrow \n", "0 26.0 Yes 0 \n", "1 21.9 No 0 \n", "2 24.1 Yes 0 \n", "3 16.0 Yes 1 \n", "4 18.1 No 0 \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load the data\n", "X = pd.read_csv(\"./datasets/weatherAUS.csv\")\n", "\n", "# Let's have a look\n", "X.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the pipeline" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "<< ================== ATOM ================== >>\n", "\n", "Configuration ==================== >>\n", "Algorithm task: Binary classification.\n", "\n", "Dataset stats ==================== >>\n", "Shape: (142193, 22)\n", "Train set size: 113755\n", "Test set size: 28438\n", "-------------------------------------\n", "Memory: 25.03 MB\n", "Scaled: False\n", "Missing values: 316559 (10.1%)\n", "Categorical features: 5 (23.8%)\n", "Duplicates: 45 (0.0%)\n", "\n", "Fitting Cleaner...\n", "Cleaning the data...\n", "Fitting Imputer...\n", "Imputing missing values...\n", " --> Dropping 161 samples for containing more than 16 missing values.\n", " --> Imputing 481 missing values with median (12.0) in column MinTemp.\n", " --> Imputing 265 missing values with median (22.6) in column MaxTemp.\n", " --> Imputing 1354 missing values with median (0.0) in column Rainfall.\n", " --> Imputing 60682 missing values with median (4.8) in column Evaporation.\n", " --> Imputing 67659 missing values with median (8.4) in column Sunshine.\n", " --> Imputing 9187 missing values with most_frequent (W) in column WindGustDir.\n", " --> Imputing 9127 missing values with median (39.0) in column WindGustSpeed.\n", " --> Imputing 9852 missing values with most_frequent (N) in column WindDir9am.\n", " --> Imputing 3617 missing values with most_frequent (SE) in column WindDir3pm.\n", " --> Imputing 1187 missing values with median (13.0) in column WindSpeed9am.\n", " --> Imputing 2469 missing values with median (19.0) in column WindSpeed3pm.\n", " --> Imputing 1613 missing values with median (70.0) in column Humidity9am.\n", " --> Imputing 3449 missing values with median (52.0) in column Humidity3pm.\n", " --> Imputing 13863 missing values with median (1017.6) in column Pressure9am.\n", " --> Imputing 13830 missing values with median (1015.2) in column Pressure3pm.\n", " --> Imputing 53496 missing values with median (5.0) in column Cloud9am.\n", " --> Imputing 56933 missing values with median (5.0) in column Cloud3pm.\n", " --> Imputing 743 missing values with median (16.7) in column Temp9am.\n", " --> Imputing 2565 missing values with median (21.1) in column Temp3pm.\n", " --> Imputing 1354 missing values with most_frequent (No) in column RainToday.\n", "Fitting Encoder...\n", "Encoding categorical columns...\n", " --> Target-encoding feature Location. Contains 49 classes.\n", " --> Target-encoding feature WindGustDir. Contains 16 classes.\n", " --> Target-encoding feature WindDir9am. Contains 16 classes.\n", " --> Target-encoding feature WindDir3pm. Contains 16 classes.\n", " --> Ordinal-encoding feature RainToday. Contains 2 classes.\n" ] } ], "source": [ "# Initialize atom and prepare the data\n", "atom = ATOMClassifier(X, verbose=2, random_state=1)\n", "atom.clean()\n", "atom.impute(strat_num=\"median\", strat_cat=\"most_frequent\", max_nan_rows=0.8)\n", "atom.encode()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Training ========================= >>\n", "Metric: f1\n", "\n", "\n", "Run: 0 =========================== >>\n", "Models: LR01\n", "Size of training set: 11362 (10%)\n", "Size of test set: 28408\n", "\n", "\n", "Results for LogisticRegression:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.563\n", "Test evaluation --> f1: 0.5854\n", "Time elapsed: 1.201s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> f1: 0.5849 ± 0.002\n", "Time elapsed: 0.909s\n", "-------------------------------------------------\n", "Time: 2.110s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 2.133s\n", "-------------------------------------\n", "LogisticRegression --> f1: 0.5849 ± 0.002\n", "\n", "\n", "Run: 1 =========================== >>\n", "Models: LR02\n", "Size of training set: 22724 (20%)\n", "Size of test set: 28408\n", "\n", "\n", "Results for LogisticRegression:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.582\n", "Test evaluation --> f1: 0.5873\n", "Time elapsed: 1.239s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> f1: 0.5852 ± 0.0021\n", "Time elapsed: 1.076s\n", "-------------------------------------------------\n", "Time: 2.315s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 2.342s\n", "-------------------------------------\n", "LogisticRegression --> f1: 0.5852 ± 0.0021\n", "\n", "\n", "Run: 2 =========================== >>\n", "Models: LR03\n", "Size of training set: 34087 (30%)\n", "Size of test set: 28408\n", "\n", "\n", "Results for LogisticRegression:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.581\n", "Test evaluation --> f1: 0.5851\n", "Time elapsed: 1.426s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> f1: 0.5861 ± 0.0009\n", "Time elapsed: 1.457s\n", "-------------------------------------------------\n", "Time: 2.883s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 2.913s\n", "-------------------------------------\n", "LogisticRegression --> f1: 0.5861 ± 0.0009\n", "\n", "\n", "Run: 3 =========================== >>\n", "Models: LR04\n", "Size of training set: 45449 (40%)\n", "Size of test set: 28408\n", "\n", "\n", "Results for LogisticRegression:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.5827\n", "Test evaluation --> f1: 0.5869\n", "Time elapsed: 1.501s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> f1: 0.5863 ± 0.0017\n", "Time elapsed: 1.585s\n", "-------------------------------------------------\n", "Time: 3.086s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 3.118s\n", "-------------------------------------\n", "LogisticRegression --> f1: 0.5863 ± 0.0017\n", "\n", "\n", "Run: 4 =========================== >>\n", "Models: LR05\n", "Size of training set: 56812 (50%)\n", "Size of test set: 28408\n", "\n", "\n", "Results for LogisticRegression:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.5819\n", "Test evaluation --> f1: 0.585\n", "Time elapsed: 1.673s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> f1: 0.5854 ± 0.0017\n", "Time elapsed: 1.635s\n", "-------------------------------------------------\n", "Time: 3.308s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 3.347s\n", "-------------------------------------\n", "LogisticRegression --> f1: 0.5854 ± 0.0017\n", "\n", "\n", "Run: 5 =========================== >>\n", "Models: LR06\n", "Size of training set: 68174 (60%)\n", "Size of test set: 28408\n", "\n", "\n", "Results for LogisticRegression:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.5832\n", "Test evaluation --> f1: 0.5848\n", "Time elapsed: 1.865s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> f1: 0.5849 ± 0.0018\n", "Time elapsed: 2.218s\n", "-------------------------------------------------\n", "Time: 4.083s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 4.141s\n", "-------------------------------------\n", "LogisticRegression --> f1: 0.5849 ± 0.0018\n", "\n", "\n", "Run: 6 =========================== >>\n", "Models: LR07\n", "Size of training set: 79536 (70%)\n", "Size of test set: 28408\n", "\n", "\n", "Results for LogisticRegression:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.5873\n", "Test evaluation --> f1: 0.5849\n", "Time elapsed: 2.302s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> f1: 0.5852 ± 0.0012\n", "Time elapsed: 2.969s\n", "-------------------------------------------------\n", "Time: 5.271s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 5.364s\n", "-------------------------------------\n", "LogisticRegression --> f1: 0.5852 ± 0.0012\n", "\n", "\n", "Run: 7 =========================== >>\n", "Models: LR08\n", "Size of training set: 90899 (80%)\n", "Size of test set: 28408\n", "\n", "\n", "Results for LogisticRegression:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.589\n", "Test evaluation --> f1: 0.5837\n", "Time elapsed: 5.182s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> f1: 0.5853 ± 0.0026\n", "Time elapsed: 4.662s\n", "-------------------------------------------------\n", "Time: 9.844s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 9.934s\n", "-------------------------------------\n", "LogisticRegression --> f1: 0.5853 ± 0.0026\n", "\n", "\n", "Run: 8 =========================== >>\n", "Models: LR09\n", "Size of training set: 102261 (90%)\n", "Size of test set: 28408\n", "\n", "\n", "Results for LogisticRegression:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.5871\n", "Test evaluation --> f1: 0.5845\n", "Time elapsed: 7.434s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> f1: 0.5846 ± 0.002\n", "Time elapsed: 7.263s\n", "-------------------------------------------------\n", "Time: 14.697s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 14.891s\n", "-------------------------------------\n", "LogisticRegression --> f1: 0.5846 ± 0.002\n", "\n", "\n", "Run: 9 =========================== >>\n", "Models: LR10\n", "Size of training set: 113624 (100%)\n", "Size of test set: 28408\n", "\n", "\n", "Results for LogisticRegression:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.5858\n", "Test evaluation --> f1: 0.5848\n", "Time elapsed: 7.974s\n", "Bootstrap ---------------------------------------\n", "Evaluation --> f1: 0.5848 ± 0.0007\n", "Time elapsed: 6.985s\n", "-------------------------------------------------\n", "Time: 14.959s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 15.082s\n", "-------------------------------------\n", "LogisticRegression --> f1: 0.5848 ± 0.0007\n" ] } ], "source": [ "# Analyze the impact of the training set's size on a LR model\n", "atom.train_sizing(\"LR\", train_sizes=10, n_bootstrap=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyze the results" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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f1_trainf1_testtime_fitf1_bootstraptime_bootstraptime
fracmodel
0.1LR010.56210.58481.2009890.5849220.9094242.110413
0.2LR020.58320.58461.2388030.5852341.0762372.315040
0.3LR030.58000.58521.4263560.5861181.4567872.883143
0.4LR040.58450.58571.5011770.5863481.5851523.086329
0.5LR050.58330.58651.6729440.5853841.6351413.308085
0.6LR060.58310.58321.8650110.5848912.2177604.082771
0.7LR070.58780.58582.3016550.5852352.9694475.271102
0.8LR080.59160.58865.1815740.5852694.6622529.843826
0.9LR090.58560.58337.4343350.5846337.26254214.696877
1.0LR100.58580.58487.9740120.5848366.98508914.959101
\n", "
" ], "text/plain": [ " f1_train f1_test time_fit f1_bootstrap time_bootstrap \\\n", "frac model \n", "0.1 LR01 0.5621 0.5848 1.200989 0.584922 0.909424 \n", "0.2 LR02 0.5832 0.5846 1.238803 0.585234 1.076237 \n", "0.3 LR03 0.5800 0.5852 1.426356 0.586118 1.456787 \n", "0.4 LR04 0.5845 0.5857 1.501177 0.586348 1.585152 \n", "0.5 LR05 0.5833 0.5865 1.672944 0.585384 1.635141 \n", "0.6 LR06 0.5831 0.5832 1.865011 0.584891 2.217760 \n", "0.7 LR07 0.5878 0.5858 2.301655 0.585235 2.969447 \n", "0.8 LR08 0.5916 0.5886 5.181574 0.585269 4.662252 \n", "0.9 LR09 0.5856 0.5833 7.434335 0.584633 7.262542 \n", "1.0 LR10 0.5858 0.5848 7.974012 0.584836 6.985089 \n", "\n", " time \n", "frac model \n", "0.1 LR01 2.110413 \n", "0.2 LR02 2.315040 \n", "0.3 LR03 2.883143 \n", "0.4 LR04 3.086329 \n", "0.5 LR05 3.308085 \n", "0.6 LR06 4.082771 \n", "0.7 LR07 5.271102 \n", "0.8 LR08 9.843826 \n", "0.9 LR09 14.696877 \n", "1.0 LR10 14.959101 " ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# The results are now multi-index, where frac is the fraction\n", "# of the training set used to fit the model. The model names\n", "# end with the fraction as well (without the dot)\n", "atom.results" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "image/png": 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