LinearRegression¶
Linear regression.
This estimator supports learning with mini-batches. On top of the single instance methods, it provides the following methods: learn_many
, predict_many
, predict_proba_many
. Each method takes as input a pandas.DataFrame
where each column represents a feature.
It is generally a good idea to scale the data beforehand in order for the optimizer to converge. You can do this online with a preprocessing.StandardScaler
.
Parameters¶
-
optimizer (optim.Optimizer) – defaults to
None
The sequential optimizer used for updating the weights. Note that the intercept updates are handled separately.
-
loss (optim.losses.RegressionLoss) – defaults to
None
The loss function to optimize for.
-
l2 – defaults to
0.0
Amount of L2 regularization used to push weights towards 0.
-
intercept_init – defaults to
0.0
Initial intercept value.
-
intercept_lr (Union[optim.schedulers.Scheduler, float]) – defaults to
0.01
Learning rate scheduler used for updating the intercept. A
optim.schedulers.Constant
is used if afloat
is provided. The intercept is not updated when this is set to 0. -
clip_gradient – defaults to
1000000000000.0
Clips the absolute value of each gradient value.
-
initializer (optim.initializers.Initializer) – defaults to
None
Weights initialization scheme.
Attributes¶
-
weights (dict)
The current weights.
Examples¶
>>> from river import datasets
>>> from river import evaluate
>>> from river import linear_model
>>> from river import metrics
>>> from river import preprocessing
>>> dataset = datasets.TrumpApproval()
>>> model = (
... preprocessing.StandardScaler() |
... linear_model.LinearRegression(intercept_lr=.1)
... )
>>> metric = metrics.MAE()
>>> evaluate.progressive_val_score(dataset, model, metric)
MAE: 0.555971
>>> model['LinearRegression'].intercept
35.617670
You can call the debug_one
method to break down a prediction. This works even if the
linear regression is part of a pipeline.
>>> x, y = next(iter(dataset))
>>> report = model.debug_one(x)
>>> print(report)
0. Input
--------
gallup: 43.84321 (float)
ipsos: 46.19925 (float)
morning_consult: 48.31875 (float)
ordinal_date: 736389 (int)
rasmussen: 44.10469 (float)
you_gov: 43.63691 (float)
<BLANKLINE>
1. StandardScaler
-----------------
gallup: 1.18810 (float)
ipsos: 2.10348 (float)
morning_consult: 2.73545 (float)
ordinal_date: -1.73032 (float)
rasmussen: 1.26872 (float)
you_gov: 1.48391 (float)
<BLANKLINE>
2. LinearRegression
-------------------
Name Value Weight Contribution
Intercept 1.00000 35.61767 35.61767
ipsos 2.10348 0.62689 1.31866
morning_consult 2.73545 0.24180 0.66144
gallup 1.18810 0.43568 0.51764
rasmussen 1.26872 0.28118 0.35674
you_gov 1.48391 0.03123 0.04634
ordinal_date -1.73032 3.45162 -5.97242
<BLANKLINE>
Prediction: 32.54607
Methods¶
clone
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via copy.deepcopy
if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
debug_one
Debugs the output of the linear regression.
Parameters
- x (dict)
- decimals (int) – defaults to
5
Returns
str: A table which explains the output.
learn_many
Update the model with a mini-batch of features X
and boolean targets y
.
Parameters
- X (pandas.core.frame.DataFrame)
- y (pandas.core.series.Series)
- w (Union[float, pandas.core.series.Series]) – defaults to
1
Returns
MiniBatchRegressor: self
learn_one
Fits to a set of features x
and a real-valued target y
.
Parameters
- x (dict)
- y (numbers.Number)
- w – defaults to
1.0
Returns
Regressor: self
predict_many
Predict the outcome for each given sample.
Parameters --------- X A dataframe of features.
Parameters
- X
Returns
The predicted outcomes.
predict_one
Predicts the target value of a set of features x
.
Parameters
- x
Returns
The prediction.