Benchmark
Binary classification
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Datasets
Phishing
Phishing websites.
This dataset contains features from web pages that are classified as phishing or not.
Name Phishing
Task Binary classification
Samples 1,250
Features 9
Sparse False
Path /Users/kulbach/Documents/environments/deep-river39/lib/python3.9/site-packages/river/datasets/phishing.csv.gz
Bananas
Bananas dataset.
An artificial dataset where instances belongs to several clusters with a banana shape.
There are two attributes that correspond to the x and y axis, respectively.
Name Bananas
Task Binary classification
Samples 5,300
Features 2
Sparse False
Path /Users/kulbach/Documents/environments/deep-river39/lib/python3.9/site-packages/river/datasets/banana.zip
Models
Logistic regression
Pipeline (
StandardScaler (
with_std=True
),
LogisticRegression (
optimizer=SGD (
lr=Constant (
learning_rate=0.005
)
)
loss=Log (
weight_pos=1.
weight_neg=1.
)
l2=0.
l1=0.
intercept_init=0.
intercept_lr=Constant (
learning_rate=0.01
)
clip_gradient=1e+12
initializer=Zeros ()
)
)
Torch Logistic Regression
Pipeline (
StandardScaler (
with_std=True
),
Classifier (
module=None
loss_fn="binary_cross_entropy"
optimizer_fn=<class 'torch.optim.sgd.SGD'>
lr=0.005
output_is_logit=True
is_class_incremental=True
device="cpu"
seed=42
)
)
Torch MLP
Pipeline (
StandardScaler (
with_std=True
),
Classifier (
module=None
loss_fn="binary_cross_entropy"
optimizer_fn=<class 'torch.optim.sgd.SGD'>
lr=0.005
output_is_logit=True
is_class_incremental=True
device="cpu"
seed=42
)
)
Torch LSTM
Pipeline (
StandardScaler (
with_std=True
),
RollingClassifier (
module=None
loss_fn="binary_cross_entropy"
optimizer_fn=<class 'torch.optim.sgd.SGD'>
lr=0.005
output_is_logit=True
is_class_incremental=True
device="cpu"
seed=42
window_size=20
append_predict=False
)
)
[baseline] Last Class
NoChangeClassifier ()
Multiclass classification
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Datasets
ImageSegments
Image segments classification.
This dataset contains features that describe image segments into 7 classes: brickface, sky,
foliage, cement, window, path, and grass.
Name ImageSegments
Task Multi-class classification
Samples 2,310
Features 18
Sparse False
Path /Users/kulbach/Documents/environments/deep-river39/lib/python3.9/site-packages/river/datasets/segment.csv.zip
Insects
Insects dataset.
This dataset has different variants, which are:
- abrupt_balanced
- abrupt_imbalanced
- gradual_balanced
- gradual_imbalanced
- incremental-abrupt_balanced
- incremental-abrupt_imbalanced
- incremental-reoccurring_balanced
- incremental-reoccurring_imbalanced
- incremental_balanced
- incremental_imbalanced
- out-of-control
The number of samples and the difficulty change from one variant to another. The number of
classes is always the same (6), except for the last variant (24).
Name Insects
Task Multi-class classification
Samples 52,848
Features 33
Classes 6
Sparse False
Path /Users/kulbach/river_data/Insects/INSECTS-abrupt_balanced_norm.arff
URL http://sites.labic.icmc.usp.br/vsouza/repository/creme/INSECTS-abrupt_balanced_norm.arff
Size 15.66 MB
Downloaded True
Variant abrupt_balanced
Parameters
----------
variant
Indicates which variant of the dataset to load.
Keystroke
CMU keystroke dataset.
Users are tasked to type in a password. The task is to determine which user is typing in the
password.
The only difference with the original dataset is that the "sessionIndex" and "rep" attributes
have been dropped.
Name Keystroke
Task Multi-class classification
Samples 20,400
Features 31
Sparse False
Path /Users/kulbach/river_data/Keystroke/DSL-StrongPasswordData.csv
URL http://www.cs.cmu.edu/~keystroke/DSL-StrongPasswordData.csv
Size 4.45 MB
Downloaded True
Models
Torch Logistic Regression
Pipeline (
StandardScaler (
with_std=True
),
Classifier (
module=None
loss_fn="binary_cross_entropy"
optimizer_fn=<class 'torch.optim.sgd.SGD'>
lr=0.005
output_is_logit=True
is_class_incremental=True
device="cpu"
seed=42
)
)
Torch MLP
Pipeline (
StandardScaler (
with_std=True
),
Classifier (
module=None
loss_fn="binary_cross_entropy"
optimizer_fn=<class 'torch.optim.sgd.SGD'>
lr=0.005
output_is_logit=True
is_class_incremental=True
device="cpu"
seed=42
)
)
Torch LSTM
Pipeline (
StandardScaler (
with_std=True
),
RollingClassifier (
module=None
loss_fn="binary_cross_entropy"
optimizer_fn=<class 'torch.optim.sgd.SGD'>
lr=0.005
output_is_logit=True
is_class_incremental=True
device="cpu"
seed=42
window_size=20
append_predict=False
)
)
[baseline] Last Class
NoChangeClassifier ()
Regression
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}
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Datasets
TrumpApproval
Donald Trump approval ratings.
This dataset was obtained by reshaping the data used by FiveThirtyEight for analyzing Donald
Trump's approval ratings. It contains 5 features, which are approval ratings collected by
5 polling agencies. The target is the approval rating from FiveThirtyEight's model. The goal of
this task is to see if we can reproduce FiveThirtyEight's model.
Name TrumpApproval
Task Regression
Samples 1,001
Features 6
Sparse False
Path /Users/kulbach/Documents/environments/deep-river39/lib/python3.9/site-packages/river/datasets/trump_approval.csv.gz
Friedman7k
Sample from the stationary version of the Friedman dataset.
This sample contains 10k instances sampled from the Friedman generator.
Name Friedman7k
Task Regression
Samples 7,000
Features 10
Sparse False
FriedmanLEA10k
Sample from the FriedmanLEA generator.
This sample contains 10k instances sampled from the Friedman generator and presents
local-expanding abrupt concept drifts that locally affect the data and happen after
2k, 5k, and 8k instances.
Name FriedmanLEA10k
Task Regression
Samples 10,000
Features 10
Sparse False
FriedmanGSG10k
Sample from the FriedmanGSG generator.
This sample contains 10k instances sampled from the Friedman generator and presents
global and slow gradual concept drifts that affect the data and happen after
3.5k and 7k instances. The transition window between different concepts has a length of
1k instances.
Name FriedmanGSG10k
Task Regression
Samples 10,000
Features 10
Sparse False
Models
Torch Linear Regression
Pipeline (
StandardScaler (
with_std=True
),
Regressor (
module=None
loss_fn="mse_loss"
optimizer_fn=<class 'torch.optim.sgd.SGD'>
lr=0.005
device="cpu"
seed=42
)
)
Torch MLP
Pipeline (
StandardScaler (
with_std=True
),
Regressor (
module=None
loss_fn="mse_loss"
optimizer_fn=<class 'torch.optim.sgd.SGD'>
lr=0.005
device="cpu"
seed=42
)
)
River MLP
Pipeline (
StandardScaler (
with_std=True
),
MLPRegressor (
hidden_dims=(5,)
activations=(<class 'river.neural_net.activations.ReLU'>, <class 'river.neural_net.activations.ReLU'>, <class 'river.neural_net.activations.Identity'>)
loss=Squared ()
optimizer=SGD (
lr=Constant (
learning_rate=0.001
)
)
seed=42
)
)
Torch LSTM
Pipeline (
StandardScaler (
with_std=True
),
RollingRegressor (
module=None
loss_fn="mse_loss"
optimizer_fn=<class 'torch.optim.sgd.SGD'>
lr=0.005
window_size=20
append_predict=False
device="cpu"
seed=42
)
)
[baseline] Mean predictor
StatisticRegressor (
statistic=Mean ()
)
Environment
Python implementation: CPython
Python version : 3.9.18
IPython version : 8.17.2
river : 0.19.0
numpy : 1.25.2
scikit-learn: 1.3.2
pandas : 2.1.2
scipy : 1.11.3
Compiler : GCC 11.4.0
OS : Linux
Release : 6.2.0-1015-azure
Machine : x86_64
Processor : x86_64
CPU cores : 4
Architecture: 64bit