Example: Binary classification¶

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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.

The data used is a variation on the Australian weather dataset from Kaggle. You can download it from here. The goal of this dataset is to predict whether or not it will rain tomorrow training a binary classifier on target RainTomorrow.

Load the data¶

In [1]:

# Import packages
import pandas as pd
from atom import ATOMClassifier

# Import packages import pandas as pd from atom import ATOMClassifier

In [2]:

# Load data
X = pd.read_csv("./datasets/weatherAUS.csv")

# Let's have a look
X.head()

# Load data X = pd.read_csv("./datasets/weatherAUS.csv") # Let's have a look X.head()

Out[2]:

	Location	MinTemp	MaxTemp	Rainfall	Evaporation	Sunshine	WindGustDir	WindGustSpeed	WindDir9am	WindDir3pm	...	Humidity9am	Humidity3pm	Pressure9am	Pressure3pm	Cloud9am	Cloud3pm	Temp9am	Temp3pm	RainToday	RainTomorrow
0	MelbourneAirport	18.0	26.9	21.4	7.0	8.9	SSE	41.0	W	SSE	...	95.0	54.0	1019.5	1017.0	8.0	5.0	18.5	26.0	Yes	0
1	Adelaide	17.2	23.4	0.0	NaN	NaN	S	41.0	S	WSW	...	59.0	36.0	1015.7	1015.7	NaN	NaN	17.7	21.9	No	0
2	Cairns	18.6	24.6	7.4	3.0	6.1	SSE	54.0	SSE	SE	...	78.0	57.0	1018.7	1016.6	3.0	3.0	20.8	24.1	Yes	0
3	Portland	13.6	16.8	4.2	1.2	0.0	ESE	39.0	ESE	ESE	...	76.0	74.0	1021.4	1020.5	7.0	8.0	15.6	16.0	Yes	1
4	Walpole	16.4	19.9	0.0	NaN	NaN	SE	44.0	SE	SE	...	78.0	70.0	1019.4	1018.9	NaN	NaN	17.4	18.1	No	0

5 rows × 22 columns

Run the pipeline¶

In [3]:

# Call atom using only 5% of the complete dataset (for explanatory purposes)
atom = ATOMClassifier(X, "RainTomorrow", n_rows=0.05, n_jobs=8, verbose=2)

# Call atom using only 5% of the complete dataset (for explanatory purposes) atom = ATOMClassifier(X, "RainTomorrow", n_rows=0.05, n_jobs=8, verbose=2)

<< ================== ATOM ================== >>
Algorithm task: binary classification.
Parallel processing with 8 cores.
Parallelization backend: loky

Dataset stats ==================== >>
Shape: (7109, 22)
Train set size: 5688
Test set size: 1421
-------------------------------------
Memory: 3.08 MB
Scaled: False
Missing values: 15681 (10.0%)
Categorical features: 5 (23.8%)
Duplicate samples: 2 (0.0%)

In [4]:

# Impute missing values
atom.impute(strat_num="median", strat_cat="drop", max_nan_rows=0.8)

# Impute missing values atom.impute(strat_num="median", strat_cat="drop", max_nan_rows=0.8)

Fitting Imputer...
Imputing missing values...
 --> Dropping 11 samples for containing more than 16 missing values.
 --> Imputing 23 missing values with median (11.9) in feature MinTemp.
 --> Imputing 23 missing values with median (22.4) in feature MaxTemp.
 --> Imputing 69 missing values with median (0.0) in feature Rainfall.
 --> Imputing 2986 missing values with median (4.8) in feature Evaporation.
 --> Imputing 3358 missing values with median (8.4) in feature Sunshine.
 --> Dropping 474 samples due to missing values in feature WindGustDir.
 --> Imputing 471 missing values with median (39.0) in feature WindGustSpeed.
 --> Dropping 490 samples due to missing values in feature WindDir9am.
 --> Dropping 179 samples due to missing values in feature WindDir3pm.
 --> Imputing 50 missing values with median (13.0) in feature WindSpeed9am.
 --> Imputing 121 missing values with median (19.0) in feature WindSpeed3pm.
 --> Imputing 73 missing values with median (69.0) in feature Humidity9am.
 --> Imputing 176 missing values with median (52.0) in feature Humidity3pm.
 --> Imputing 695 missing values with median (1017.6) in feature Pressure9am.
 --> Imputing 697 missing values with median (1015.1) in feature Pressure3pm.
 --> Imputing 2605 missing values with median (5.0) in feature Cloud9am.
 --> Imputing 2756 missing values with median (5.0) in feature Cloud3pm.
 --> Imputing 36 missing values with median (16.6) in feature Temp9am.
 --> Imputing 131 missing values with median (20.9) in feature Temp3pm.
 --> Dropping 69 samples due to missing values in feature RainToday.

In [5]:

# Encode the categorical features
atom.encode(strategy="Target", max_onehot=10, infrequent_to_value=0.04)

# Encode the categorical features atom.encode(strategy="Target", max_onehot=10, infrequent_to_value=0.04)

Fitting Encoder...
Encoding categorical columns...
 --> Target-encoding feature Location. Contains 47 classes.
 --> Target-encoding feature WindGustDir. Contains 16 classes.
 --> Target-encoding feature WindDir9am. Contains 16 classes.
 --> Target-encoding feature WindDir3pm. Contains 16 classes.
 --> Ordinal-encoding feature RainToday. Contains 2 classes.

In [6]:

# Train an Extra-Trees and a Random Forest model
atom.run(models=["ET", "RF"], metric="f1", n_bootstrap=5)

# Train an Extra-Trees and a Random Forest model atom.run(models=["ET", "RF"], metric="f1", n_bootstrap=5)

Training ========================= >>
Models: ET, RF
Metric: f1


Results for ExtraTrees:
Fit ---------------------------------------------
Train evaluation --> f1: 1.0
Test evaluation --> f1: 0.5688
Time elapsed: 9.395s
Bootstrap ---------------------------------------
Evaluation --> f1: 0.5463 ± 0.0135
Time elapsed: 4.742s
-------------------------------------------------
Total time: 14.138s


Results for RandomForest:
Fit ---------------------------------------------
Train evaluation --> f1: 1.0
Test evaluation --> f1: 0.5969
Time elapsed: 1.368s
Bootstrap ---------------------------------------
Evaluation --> f1: 0.576 ± 0.0117
Time elapsed: 5.341s
-------------------------------------------------
Total time: 6.709s


Final results ==================== >>
Total time: 20.861s
-------------------------------------
ExtraTrees   --> f1: 0.5463 ± 0.0135 ~
RandomForest --> f1: 0.576 ± 0.0117 ~ !

Analyze the results¶

In [7]:

# Let's have a look at the final results
atom.results

# Let's have a look at the final results atom.results

Out[7]:

	score_train	score_test	time_fit	score_bootstrap	time_bootstrap	time
ET	1.0	0.5688	9.395485	0.546307	4.742292	14.137777
RF	1.0	0.5969	1.367760	0.575995	5.341101	6.708861

In [8]:

# Visualize the bootstrap results
atom.plot_results(title="RF vs ET performance")

# Visualize the bootstrap results atom.plot_results(title="RF vs ET performance")

In [9]:

# Print the results of some common metrics
atom.evaluate()

# Print the results of some common metrics atom.evaluate()

Out[9]:

	accuracy	average_precision	balanced_accuracy	f1	jaccard	matthews_corrcoef	precision	recall	roc_auc
ET	0.8427	0.6813	0.6911	0.5403	0.3701	0.4828	0.7550	0.4207	0.8613
RF	0.8516	0.6871	0.7180	0.5869	0.4153	0.5212	0.7558	0.4797	0.8652

In [10]:

# The winner attribute calls the best model (atom.winner == atom.rf)
print(f"The winner is the {atom.winner.name} model!!")

# The winner attribute calls the best model (atom.winner == atom.rf) print(f"The winner is the {atom.winner.name} model!!")

The winner is the RF model!!

In [11]:

# Visualize the distribution of predicted probabilities
atom.winner.plot_probabilities()

# Visualize the distribution of predicted probabilities atom.winner.plot_probabilities()

In [12]:

# Compare how different metrics perform for different thresholds
atom.winner.plot_threshold(metric=["f1", "accuracy", "ap"], steps=50)

# Compare how different metrics perform for different thresholds atom.winner.plot_threshold(metric=["f1", "accuracy", "ap"], steps=50)