{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example: Binary classification\n",
"--------------------------------\n",
"\n",
"This example shows how to use ATOM to solve a binary classification problem. Additonnaly, we'll perform a variety of data cleaning steps to prepare the data for modelling.\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": [
"
\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Location | \n",
" MinTemp | \n",
" MaxTemp | \n",
" Rainfall | \n",
" Evaporation | \n",
" Sunshine | \n",
" WindGustDir | \n",
" WindGustSpeed | \n",
" WindDir9am | \n",
" WindDir3pm | \n",
" ... | \n",
" Humidity9am | \n",
" Humidity3pm | \n",
" Pressure9am | \n",
" Pressure3pm | \n",
" Cloud9am | \n",
" Cloud3pm | \n",
" Temp9am | \n",
" Temp3pm | \n",
" RainToday | \n",
" RainTomorrow | \n",
"
\n",
" \n",
" \n",
" \n",
" 0 | \n",
" MelbourneAirport | \n",
" 18.0 | \n",
" 26.9 | \n",
" 21.4 | \n",
" 7.0 | \n",
" 8.9 | \n",
" SSE | \n",
" 41.0 | \n",
" W | \n",
" SSE | \n",
" ... | \n",
" 95.0 | \n",
" 54.0 | \n",
" 1019.5 | \n",
" 1017.0 | \n",
" 8.0 | \n",
" 5.0 | \n",
" 18.5 | \n",
" 26.0 | \n",
" Yes | \n",
" 0 | \n",
"
\n",
" \n",
" 1 | \n",
" Adelaide | \n",
" 17.2 | \n",
" 23.4 | \n",
" 0.0 | \n",
" NaN | \n",
" NaN | \n",
" S | \n",
" 41.0 | \n",
" S | \n",
" WSW | \n",
" ... | \n",
" 59.0 | \n",
" 36.0 | \n",
" 1015.7 | \n",
" 1015.7 | \n",
" NaN | \n",
" NaN | \n",
" 17.7 | \n",
" 21.9 | \n",
" No | \n",
" 0 | \n",
"
\n",
" \n",
" 2 | \n",
" Cairns | \n",
" 18.6 | \n",
" 24.6 | \n",
" 7.4 | \n",
" 3.0 | \n",
" 6.1 | \n",
" SSE | \n",
" 54.0 | \n",
" SSE | \n",
" SE | \n",
" ... | \n",
" 78.0 | \n",
" 57.0 | \n",
" 1018.7 | \n",
" 1016.6 | \n",
" 3.0 | \n",
" 3.0 | \n",
" 20.8 | \n",
" 24.1 | \n",
" Yes | \n",
" 0 | \n",
"
\n",
" \n",
" 3 | \n",
" Portland | \n",
" 13.6 | \n",
" 16.8 | \n",
" 4.2 | \n",
" 1.2 | \n",
" 0.0 | \n",
" ESE | \n",
" 39.0 | \n",
" ESE | \n",
" ESE | \n",
" ... | \n",
" 76.0 | \n",
" 74.0 | \n",
" 1021.4 | \n",
" 1020.5 | \n",
" 7.0 | \n",
" 8.0 | \n",
" 15.6 | \n",
" 16.0 | \n",
" Yes | \n",
" 1 | \n",
"
\n",
" \n",
" 4 | \n",
" Walpole | \n",
" 16.4 | \n",
" 19.9 | \n",
" 0.0 | \n",
" NaN | \n",
" NaN | \n",
" SE | \n",
" 44.0 | \n",
" SE | \n",
" SE | \n",
" ... | \n",
" 78.0 | \n",
" 70.0 | \n",
" 1019.4 | \n",
" 1018.9 | \n",
" NaN | \n",
" NaN | \n",
" 17.4 | \n",
" 18.1 | \n",
" No | \n",
" 0 | \n",
"
\n",
" \n",
"
\n",
"
5 rows × 22 columns
\n",
"
"
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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]"
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},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Load 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",
"Algorithm task: binary classification.\n",
"Parallel processing with 8 cores.\n",
"Parallelization backend: loky\n",
"\n",
"Dataset stats ==================== >>\n",
"Shape: (7109, 22)\n",
"Train set size: 5688\n",
"Test set size: 1421\n",
"-------------------------------------\n",
"Memory: 3.08 MB\n",
"Scaled: False\n",
"Missing values: 15681 (10.0%)\n",
"Categorical features: 5 (23.8%)\n",
"Duplicate samples: 2 (0.0%)\n",
"\n"
]
}
],
"source": [
"# Call atom using only 5% of the complete dataset (for explanatory purposes)\n",
"atom = ATOMClassifier(X, \"RainTomorrow\", n_rows=0.05, n_jobs=8, verbose=2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting Imputer...\n",
"Imputing missing values...\n",
" --> Dropping 11 samples for containing more than 16 missing values.\n",
" --> Imputing 23 missing values with median (11.9) in feature MinTemp.\n",
" --> Imputing 23 missing values with median (22.4) in feature MaxTemp.\n",
" --> Imputing 69 missing values with median (0.0) in feature Rainfall.\n",
" --> Imputing 2986 missing values with median (4.8) in feature Evaporation.\n",
" --> Imputing 3358 missing values with median (8.4) in feature Sunshine.\n",
" --> Dropping 474 samples due to missing values in feature WindGustDir.\n",
" --> Imputing 471 missing values with median (39.0) in feature WindGustSpeed.\n",
" --> Dropping 490 samples due to missing values in feature WindDir9am.\n",
" --> Dropping 179 samples due to missing values in feature WindDir3pm.\n",
" --> Imputing 50 missing values with median (13.0) in feature WindSpeed9am.\n",
" --> Imputing 121 missing values with median (19.0) in feature WindSpeed3pm.\n",
" --> Imputing 73 missing values with median (69.0) in feature Humidity9am.\n",
" --> Imputing 176 missing values with median (52.0) in feature Humidity3pm.\n",
" --> Imputing 695 missing values with median (1017.6) in feature Pressure9am.\n",
" --> Imputing 697 missing values with median (1015.1) in feature Pressure3pm.\n",
" --> Imputing 2605 missing values with median (5.0) in feature Cloud9am.\n",
" --> Imputing 2756 missing values with median (5.0) in feature Cloud3pm.\n",
" --> Imputing 36 missing values with median (16.6) in feature Temp9am.\n",
" --> Imputing 131 missing values with median (20.9) in feature Temp3pm.\n",
" --> Dropping 69 samples due to missing values in feature RainToday.\n"
]
}
],
"source": [
"# Impute missing values\n",
"atom.impute(strat_num=\"median\", strat_cat=\"drop\", max_nan_rows=0.8)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting Encoder...\n",
"Encoding categorical columns...\n",
" --> Target-encoding feature Location. Contains 47 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": [
"# Encode the categorical features\n",
"atom.encode(strategy=\"Target\", max_onehot=10, infrequent_to_value=0.04)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"Training ========================= >>\n",
"Models: ET, RF\n",
"Metric: f1\n",
"\n",
"\n",
"Results for ExtraTrees:\n",
"Fit ---------------------------------------------\n",
"Train evaluation --> f1: 1.0\n",
"Test evaluation --> f1: 0.5688\n",
"Time elapsed: 9.395s\n",
"Bootstrap ---------------------------------------\n",
"Evaluation --> f1: 0.5463 ± 0.0135\n",
"Time elapsed: 4.742s\n",
"-------------------------------------------------\n",
"Total time: 14.138s\n",
"\n",
"\n",
"Results for RandomForest:\n",
"Fit ---------------------------------------------\n",
"Train evaluation --> f1: 1.0\n",
"Test evaluation --> f1: 0.5969\n",
"Time elapsed: 1.368s\n",
"Bootstrap ---------------------------------------\n",
"Evaluation --> f1: 0.576 ± 0.0117\n",
"Time elapsed: 5.341s\n",
"-------------------------------------------------\n",
"Total time: 6.709s\n",
"\n",
"\n",
"Final results ==================== >>\n",
"Total time: 20.861s\n",
"-------------------------------------\n",
"ExtraTrees --> f1: 0.5463 ± 0.0135 ~\n",
"RandomForest --> f1: 0.576 ± 0.0117 ~ !\n"
]
}
],
"source": [
"# Train an Extra-Trees and a Random Forest model\n",
"atom.run(models=[\"ET\", \"RF\"], metric=\"f1\", n_bootstrap=5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Analyze the results"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" score_train | \n",
" score_test | \n",
" time_fit | \n",
" score_bootstrap | \n",
" time_bootstrap | \n",
" time | \n",
"
\n",
" \n",
" \n",
" \n",
" ET | \n",
" 1.0 | \n",
" 0.5688 | \n",
" 9.395485 | \n",
" 0.546307 | \n",
" 4.742292 | \n",
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\n",
" \n",
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" 1.0 | \n",
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" 5.341101 | \n",
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\n",
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\n",
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" score_train score_test time_fit score_bootstrap time_bootstrap \\\n",
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"metadata": {},
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"source": [
"# Let's have a look at the final results\n",
"atom.results"
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},
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"data": {
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"\n",
"\n",
"
\n",
" \n",
" \n",
" | \n",
" accuracy | \n",
" average_precision | \n",
" balanced_accuracy | \n",
" f1 | \n",
" jaccard | \n",
" matthews_corrcoef | \n",
" precision | \n",
" recall | \n",
" roc_auc | \n",
"
\n",
" \n",
" \n",
" \n",
" ET | \n",
" 0.8427 | \n",
" 0.6813 | \n",
" 0.6911 | \n",
" 0.5403 | \n",
" 0.3701 | \n",
" 0.4828 | \n",
" 0.7550 | \n",
" 0.4207 | \n",
" 0.8613 | \n",
"
\n",
" \n",
" RF | \n",
" 0.8516 | \n",
" 0.6871 | \n",
" 0.7180 | \n",
" 0.5869 | \n",
" 0.4153 | \n",
" 0.5212 | \n",
" 0.7558 | \n",
" 0.4797 | \n",
" 0.8652 | \n",
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\n",
" \n",
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\n",
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"
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" accuracy average_precision balanced_accuracy f1 jaccard \\\n",
"ET 0.8427 0.6813 0.6911 0.5403 0.3701 \n",
"RF 0.8516 0.6871 0.7180 0.5869 0.4153 \n",
"\n",
" matthews_corrcoef precision recall roc_auc \n",
"ET 0.4828 0.7550 0.4207 0.8613 \n",
"RF 0.5212 0.7558 0.4797 0.8652 "
]
},
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"source": [
"# Print the results of some common metrics\n",
"atom.evaluate()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The winner is the RF model!!\n"
]
}
],
"source": [
"# The winner attribute calls the best model (atom.winner == atom.rf)\n",
"print(f\"The winner is the {atom.winner.name} model!!\")"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
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