{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Example: Calibration\n", "----------------------\n", "\n", "This example shows how to calibrate a classifier through atom.\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
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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: (10000, 22)\n", "Train set size: 8000\n", "Test set size: 2000\n", "-------------------------------------\n", "Memory: 1.76 MB\n", "Scaled: False\n", "Missing values: 21951 (10.0%)\n", "Categorical features: 5 (23.8%)\n", "Duplicates: 6 (0.1%)\n", "\n", "Fitting Cleaner...\n", "Cleaning the data...\n", "Fitting Imputer...\n", "Imputing missing values...\n", "Fitting Encoder...\n", "Encoding categorical columns...\n", "\n", "Training ========================= >>\n", "Models: GNB\n", "Metric: f1\n", "\n", "\n", "Results for GaussianNB:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.586\n", "Test evaluation --> f1: 0.5668\n", "Time elapsed: 0.104s\n", "-------------------------------------------------\n", "Time: 0.104s\n", "\n", "\n", "Final results ==================== >>\n", "Total time: 0.111s\n", "-------------------------------------\n", "GaussianNB --> f1: 0.5668\n" ] } ], "source": [ "atom = ATOMClassifier(X, \"RainTomorrow\", n_rows=1e4, verbose=1, warnings=False)\n", "\n", "# Apply data cleaning steps\n", "atom.clean()\n", "atom.impute(strat_num=\"median\", strat_cat=\"most_frequent\")\n", "atom.encode(strategy=\"target\", max_onehot=5, infrequent_to_value=0.05)\n", "\n", "# Train a linear SVM\n", "atom.run(\"gnb\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyze the results" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ " \n", " " ] }, "metadata": {}, "output_type": "display_data" }, { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "hovertemplate": "(%{x}, %{y})GNB - test", "legendgroup": "GNB", "legendgrouptitle": { "font": { 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"width": 900, "xaxis": { "anchor": "y", "automargin": true, "domain": [ 0, 1 ], "title": { "font": { "size": 16 } } }, "xaxis2": { "anchor": "y2", "automargin": true, "domain": [ 0, 1 ], "range": [ 0, 1 ], "showgrid": true, "title": { "font": { "size": 16 }, "text": "Predicted value" }, "type": "linear" }, "yaxis": { "anchor": "x2", "automargin": true, "domain": [ 0.31, 1 ], "range": [ -0.05, 1.05 ], "title": { "font": { "size": 16 }, "text": "Fraction of positives" }, "type": "linear" }, "yaxis2": { "anchor": "x2", "automargin": true, "autorange": true, "domain": [ 0, 0.29 ], "range": [ 0, 1298.9473684210527 ], "title": { "font": { "size": 16 }, "text": "Count" } } } }, "text/html": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Check the model's calibration\n", "atom.plot_calibration()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Results for GaussianNB:\n", "Fit ---------------------------------------------\n", "Train evaluation --> f1: 0.484\n", "Test evaluation --> f1: 0.4664\n", "Time elapsed: 0.207s\n" ] } ], "source": [ "# Let's try to improve it using the calibrate method\n", "atom.winner.calibrate(method=\"isotonic\")" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "tags": [] }, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "config": { "plotlyServerURL": "https://plotly.com" }, "data": [ { "hovertemplate": "(%{x}, %{y})GNB - test", "legendgroup": "GNB", "legendgrouptitle": { "font": { "size": 16 }, "text": "GNB" }, "line": { "color": "rgb(0, 98, 98)", "dash": "solid", "width": 2 }, "marker": { "color": "rgb(0, 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