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.

Dataset stats ==================== >>
Shape: (7109, 22)
Memory: 3.08 MB
Scaled: False
Missing values: 15933 (10.2%)
Categorical features: 5 (23.8%)
-------------------------------------
Train set size: 5688
Test set size: 1421
-------------------------------------
|   |      dataset |        train |         test |
| - | ------------ | ------------ | ------------ |
| 0 |   5592 (3.7) |   4474 (3.7) |   1118 (3.7) |
| 1 |   1517 (1.0) |   1214 (1.0) |    303 (1.0) |

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 5 samples for containing more than 16 missing values.
 --> Imputing 17 missing values with median (12.0) in feature MinTemp.
 --> Imputing 13 missing values with median (22.6) in feature MaxTemp.
 --> Imputing 73 missing values with median (0.0) in feature Rainfall.
 --> Imputing 3068 missing values with median (4.8) in feature Evaporation.
 --> Imputing 3413 missing values with median (8.6) in feature Sunshine.
 --> Dropping 462 samples due to missing values in feature WindGustDir.
 --> Imputing 461 missing values with median (37.0) in feature WindGustSpeed.
 --> Dropping 469 samples due to missing values in feature WindDir9am.
 --> Dropping 185 samples due to missing values in feature WindDir3pm.
 --> Imputing 53 missing values with median (13.0) in feature WindSpeed9am.
 --> Imputing 128 missing values with median (17.0) in feature WindSpeed3pm.
 --> Imputing 89 missing values with median (70.0) in feature Humidity9am.
 --> Imputing 175 missing values with median (52.0) in feature Humidity3pm.
 --> Imputing 707 missing values with median (1017.8) in feature Pressure9am.
 --> Imputing 704 missing values with median (1015.3) in feature Pressure3pm.
 --> Imputing 2699 missing values with median (5.0) in feature Cloud9am.
 --> Imputing 2882 missing values with median (5.0) in feature Cloud3pm.
 --> Imputing 41 missing values with median (16.6) in feature Temp9am.
 --> Imputing 132 missing values with median (21.2) in feature Temp3pm.
 --> Dropping 73 samples due to missing values in feature RainToday.

In [5]:

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

# Encode the categorical features atom.encode(strategy="Target", max_onehot=10, rare_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: 0.532
Time elapsed: 0.215s
Bootstrap ---------------------------------------
Evaluation --> f1: 0.4959 ± 0.0125
Time elapsed: 1.003s
-------------------------------------------------
Total time: 1.218s


Results for RandomForest:
Fit ---------------------------------------------
Train evaluation --> f1: 0.9995
Test evaluation --> f1: 0.5235
Time elapsed: 0.283s
Bootstrap ---------------------------------------
Evaluation --> f1: 0.5302 ± 0.0048
Time elapsed: 1.285s
-------------------------------------------------
Total time: 1.567s


Final results ==================== >>
Total time: 2.787s
-------------------------------------
ExtraTrees   --> f1: 0.4959 ± 0.0125 ~
RandomForest --> f1: 0.5302 ± 0.0048 ~ !

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	1.0000	0.5320	0.215197	0.495895	1.002652	1.217849
RF	0.9995	0.5235	0.282572	0.530242	1.284799	1.567371

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	0.8476	0.6559	0.6878	0.5320	0.3624	0.4789	0.7500	0.4122	0.8499
RF	0.8452	0.6729	0.6835	0.5235	0.3545	0.4692	0.7413	0.4046	0.8583

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)