sklearn_.model() - Code Metrics - Inspection of "change example" - SimonBlanke/Hyperactive - Measure and Improve Code Quality continuously with Scrutinizer

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Push — master ( 3bf564...d758e3 )

by Simon

created 2019-09-13 17:40 UTC

sklearn_.model() A

↳ Parent: sklearn_

Complexity

Conditions

Size

Total Lines	12
Code Lines	10

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	10
dl	0
loc	12
rs	9.9
c	0
b	0
f	0
cc	1
nop	3

from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression

from hyperactive import Hyperactive

iris_data = load_iris()
X = iris_data.data
y = iris_data.target


def model(para, X_train, y_train):
    model = LogisticRegression(
        C=para["C"],
        dual=para["dual"],
        penalty=para["penalty"],
        solver=para["solver"],
        multi_class=para["multi_class"],
        max_iter=para["max_iter"],
    )
    scores = cross_val_score(model, X_train, y_train, cv=3)

    return scores.mean(), model


# this defines the model and hyperparameter search space
search_config = {
    model: {
        "penalty": ["l1", "l2"],
        "C": [1e-4, 1e-3, 1e-2, 1e-1, 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0],
        "dual": [False],
        "solver": ["liblinear"],
        "multi_class": ["auto", "ovr"],
        "max_iter": range(300, 1000, 10),
    }
}


opt = Hyperactive(search_config, n_iter=100, n_jobs=2)

# search best hyperparameter for given data
opt.fit(X, y)


1			from sklearn.datasets import load_iris
2			from sklearn.model_selection import cross_val_score
3			from sklearn.linear_model import LogisticRegression
4
5			from hyperactive import Hyperactive
6
7			iris_data = load_iris()
8			X = iris_data.data
9			y = iris_data.target
10
11
12			def model(para, X_train, y_train):
13			model = LogisticRegression(
14			C=para["C"],
15			dual=para["dual"],
16			penalty=para["penalty"],
17			solver=para["solver"],
18			multi_class=para["multi_class"],
19			max_iter=para["max_iter"],
20			)
21			scores = cross_val_score(model, X_train, y_train, cv=3)
22
23			return scores.mean(), model
24
25
26			# this defines the model and hyperparameter search space
27			search_config = {
28			model: {
29			"penalty": ["l1", "l2"],
30			"C": [1e-4, 1e-3, 1e-2, 1e-1, 0.5, 1.0, 5.0, 10.0, 15.0, 20.0, 25.0],
31			"dual": [False],
32			"solver": ["liblinear"],
33			"multi_class": ["auto", "ovr"],
34			"max_iter": range(300, 1000, 10),
35			}
36			}
37
38
39			opt = Hyperactive(search_config, n_iter=100, n_jobs=2)
40
41			# search best hyperparameter for given data
42			opt.fit(X, y)
43

SimonBlanke / Hyperactive

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