Example: Accelerating pipelines¶

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This example shows how to accelerate your models on cpu using sklearnex.

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("docs_source/examples/datasets/weatherAUS.csv")

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

# Load data X = pd.read_csv("docs_source/examples/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]:

atom = ATOMClassifier(X, "RainTomorrow", verbose=2)

atom = ATOMClassifier(X, "RainTomorrow", verbose=2)

<< ================== ATOM ================== >>

Configuration ==================== >>
Algorithm task: Binary classification.

Dataset stats ==================== >>
Shape: (142193, 22)
Train set size: 113755
Test set size: 28438
-------------------------------------
Memory: 25.03 MB
Scaled: False
Missing values: 316559 (10.1%)
Categorical features: 5 (23.8%)
Duplicates: 45 (0.0%)

In [4]:

# Impute missing values and encode categorical columns
atom.impute()
atom.encode()

# Impute missing values and encode categorical columns atom.impute() atom.encode()

Fitting Imputer...
Imputing missing values...
 --> Imputing 637 missing values with mean (12.18) in column MinTemp.
 --> Imputing 322 missing values with mean (23.22) in column MaxTemp.
 --> Imputing 1406 missing values with mean (2.37) in column Rainfall.
 --> Imputing 60843 missing values with mean (5.46) in column Evaporation.
 --> Imputing 67816 missing values with mean (7.62) in column Sunshine.
 --> Imputing 9330 missing values with most_frequent (W) in column WindGustDir.
 --> Imputing 9270 missing values with mean (39.96) in column WindGustSpeed.
 --> Imputing 10013 missing values with most_frequent (N) in column WindDir9am.
 --> Imputing 3778 missing values with most_frequent (SE) in column WindDir3pm.
 --> Imputing 1348 missing values with mean (13.99) in column WindSpeed9am.
 --> Imputing 2630 missing values with mean (18.62) in column WindSpeed3pm.
 --> Imputing 1774 missing values with mean (68.86) in column Humidity9am.
 --> Imputing 3610 missing values with mean (51.48) in column Humidity3pm.
 --> Imputing 14014 missing values with mean (1017.64) in column Pressure9am.
 --> Imputing 13981 missing values with mean (1015.24) in column Pressure3pm.
 --> Imputing 53657 missing values with mean (4.44) in column Cloud9am.
 --> Imputing 57094 missing values with mean (4.5) in column Cloud3pm.
 --> Imputing 904 missing values with mean (16.98) in column Temp9am.
 --> Imputing 2726 missing values with mean (21.68) in column Temp3pm.
 --> Imputing 1406 missing values with most_frequent (No) in column RainToday.
Fitting Encoder...
Encoding categorical columns...
 --> Target-encoding feature Location. Contains 49 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 [5]:

# Train a K-Nearest Neighbors model (using default sklearn)
atom.run(models="KNN", metric="f1")

# Train a K-Nearest Neighbors model (using default sklearn) atom.run(models="KNN", metric="f1")

Training ========================= >>
Models: KNN
Metric: f1


Results for KNearestNeighbors:
Fit ---------------------------------------------
Train evaluation --> f1: 0.6962
Test evaluation --> f1: 0.5818
Time elapsed: 16.214s
-------------------------------------------------
Time: 16.214s


Final results ==================== >>
Total time: 16.249s
-------------------------------------
KNearestNeighbors --> f1: 0.5818

In [6]:

# Now, we train an accelerated KNN using engine="sklearnex"
# Note the diffrence in training speed!!
atom.run(models="KNN_acc", metric="f1", engine="sklearnex")

# Now, we train an accelerated KNN using engine="sklearnex" # Note the diffrence in training speed!! atom.run(models="KNN_acc", metric="f1", engine="sklearnex")

Training ========================= >>
Models: KNN_acc
Metric: f1


Results for KNearestNeighbors:
Fit ---------------------------------------------
Train evaluation --> f1: 0.6962
Test evaluation --> f1: 0.5818
Time elapsed: 5.443s
-------------------------------------------------
Time: 5.443s


Final results ==================== >>
Total time: 5.477s
-------------------------------------
KNearestNeighbors --> f1: 0.5818

Analyze the results¶

In [7]:

atom.results

atom.results

Out[7]:

	f1_train	f1_test	time_fit	time
KNN	0.6962	0.5818	16.213961	16.213961
KNN_acc	0.6962	0.5818	5.442855	5.442855

In [8]:

# Note how the underlying estimators might look the same...
print(atom.knn.estimator)
print(atom.knn_acc.estimator)

# ... but are using different implementations
print(atom.knn.estimator.__module__)
print(atom.knn_acc.estimator.__module__)

# Note how the underlying estimators might look the same... print(atom.knn.estimator) print(atom.knn_acc.estimator) # ... but are using different implementations print(atom.knn.estimator.__module__) print(atom.knn_acc.estimator.__module__)

KNeighborsClassifier(n_jobs=1)
KNeighborsClassifier(n_jobs=1)
sklearn.neighbors._classification
sklearnex.neighbors.knn_classification

In [9]:

with atom.canvas(1, 2, title="Timing engines: sklearn vs sklearnex"):
    atom.plot_results(metric="time_fit", title="Training")
    atom.plot_results(metric="time", title="Total")

with atom.canvas(1, 2, title="Timing engines: sklearn vs sklearnex"): atom.plot_results(metric="time_fit", title="Training") atom.plot_results(metric="time", title="Total")