数值¶

特征的处理方式取决于其语义，例如数值型、类别型、布尔型或文本型。如果未指定语义，则会自动推断。例如，浮点数和整数特征被检测为数值型，而字符串则被检测为类别型。

数值特征可以表示数量或计数。例如，一个人的年龄，或一个袋子里的物品数量。缺失的数值用 math.nan 表示。

让我们在一个浮点特征上训练一个示例。

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import ydf
import pandas as pd
import ydf
import pandas as pd

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dataset = pd.DataFrame({
    "label": [True, False, True, False],
    "feature_1": [1, 2, 2, 1],
    "feature_2": [0.1, 0.8, 0.9, 0.1],
})

model = ydf.RandomForestLearner(label="label").train(dataset)
dataset = pd.DataFrame({
    "label": [True, False, True, False],
    "feature_1": [1, 2, 2, 1],
    "feature_2": [0.1, 0.8, 0.9, 0.1],
})

model = ydf.RandomForestLearner(label="label").train(dataset)

Train model on 4 examples
Model trained in 0:00:00.009728

我们可以看到在数据规格标签中，该特征被检测为数值型。

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model.describe()
model.describe()

Out[3]:

Name : RANDOM_FOREST
Task : CLASSIFICATION
Label : label
Features (2) : feature_1 feature_2
Weights : None
Trained with tuner : No
Model size : 56 kB

Number of records: 4
Number of columns: 3

Number of columns by type:
	NUMERICAL: 2 (66.6667%)
	CATEGORICAL: 1 (33.3333%)

Columns:

NUMERICAL: 2 (66.6667%)
	1: "feature_1" NUMERICAL mean:1.5 min:1 max:2 sd:0.5
	2: "feature_2" NUMERICAL mean:0.475 min:0.1 max:0.9 sd:0.376663

CATEGORICAL: 1 (33.3333%)
	0: "label" CATEGORICAL has-dict vocab-size:3 no-ood-item most-frequent:"false" 2 (50%)

Terminology:
	nas: Number of non-available (i.e. missing) values.
	ood: Out of dictionary.
	manually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.
	tokenized: The attribute value is obtained through tokenization.
	has-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.
	vocab-size: Number of unique values.

The following evaluation is computed on the validation or out-of-bag dataset.

Number of predictions (without weights): 4
Number of predictions (with weights): 4
Task: CLASSIFICATION
Label: label

Accuracy: 0  CI95[W][0 0.527129]
LogLoss: : 1.59508
ErrorRate: : 1

Default Accuracy: : 0.5
Default LogLoss: : 0.693147
Default ErrorRate: : 0.5

Confusion Table:
truth\prediction
       false  true
false      0     2
 true      2     0
Total: 4

Variable importances measure the importance of an input feature for a model.

Those variable importances are computed during training. More, and possibly more informative, variable importances are available when analyzing a model on a test dataset.

Num trees : 300

Only printing the first tree.

Tree #0:
    val:"false" prob:[0.5, 0.5]

有时，您可能希望强制一个特征的语义为数值型。

在接下来的示例中，“feature_1”和“feature_2”看起来是布尔型。然而，我们希望“feature_1”是数值型。

在模型描述中，请注意“feature_1”是数值型，而“feature_2”是布尔型。

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dataset = pd.DataFrame({
    "label": [True, False, True, False],
    "feature_1": [True, True, False, False],
    "feature_2": [True, False, True, False],
})

model = ydf.RandomForestLearner(label="label",
                                features=[ydf.Feature("feature_1", ydf.Semantic.NUMERICAL)],
                                include_all_columns=True,
                                ).train(dataset)
# 注意：include_all_columns=True 允许模型使用所有列
# columns as features, not just the ones in "features".

model.describe()
dataset = pd.DataFrame({
    "label": [True, False, True, False],
    "feature_1": [True, True, False, False],
    "feature_2": [True, False, True, False],
})

model = ydf.RandomForestLearner(label="label",
                                features=[ydf.Feature("feature_1", ydf.Semantic.NUMERICAL)],
                                include_all_columns=True,
                                ).train(dataset)
# 注意：include_all_columns=True 允许模型使用所有列
# columns as features, not just the ones in "features".

model.describe()

Train model on 4 examples
Model trained in 0:00:00.004133

Out[4]:

Name : RANDOM_FOREST
Task : CLASSIFICATION
Label : label
Features (2) : feature_1 feature_2
Weights : None
Trained with tuner : No
Model size : 56 kB

Number of records: 4
Number of columns: 3

Number of columns by type:
	BOOLEAN: 1 (33.3333%)
	CATEGORICAL: 1 (33.3333%)
	NUMERICAL: 1 (33.3333%)

Columns:

BOOLEAN: 1 (33.3333%)
	2: "feature_2" BOOLEAN true_count:2 false_count:2

CATEGORICAL: 1 (33.3333%)
	1: "label" CATEGORICAL has-dict vocab-size:3 no-ood-item most-frequent:"false" 2 (50%)

NUMERICAL: 1 (33.3333%)
	0: "feature_1" NUMERICAL mean:0.5 min:0 max:1 sd:0.5

Terminology:
	nas: Number of non-available (i.e. missing) values.
	ood: Out of dictionary.
	manually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.
	tokenized: The attribute value is obtained through tokenization.
	has-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.
	vocab-size: Number of unique values.

The following evaluation is computed on the validation or out-of-bag dataset.

Number of predictions (without weights): 4
Number of predictions (with weights): 4
Task: CLASSIFICATION
Label: label

Accuracy: 0  CI95[W][0 0.527129]
LogLoss: : 1.66759
ErrorRate: : 1

Default Accuracy: : 0.5
Default LogLoss: : 0.693147
Default ErrorRate: : 0.5

Confusion Table:
truth\prediction
       false  true
false      0     2
 true      2     0
Total: 4

Variable importances measure the importance of an input feature for a model.

Those variable importances are computed during training. More, and possibly more informative, variable importances are available when analyzing a model on a test dataset.

Num trees : 300

Only printing the first tree.

Tree #0:
    val:"false" prob:[0.5, 0.5]

让我们创建一些缺失值。

在 Dataspec 表中，请注意“feature_2”的 num-nas:2 (50%)。这意味着“feature_2”包含两个缺失值（即，50%的值是缺失的）。

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import math

dataset = pd.DataFrame({
    "label": [True, False, True, False],
    "feature_1": [1, 2, 2, 1],
    "feature_2": [0.1, 0.8, math.nan, math.nan],
})

model = ydf.RandomForestLearner(label="label").train(dataset)
model.describe()
import math

dataset = pd.DataFrame({
    "label": [True, False, True, False],
    "feature_1": [1, 2, 2, 1],
    "feature_2": [0.1, 0.8, math.nan, math.nan],
})

model = ydf.RandomForestLearner(label="label").train(dataset)
model.describe()

Train model on 4 examples
Model trained in 0:00:00.005587

Out[6]:

Name : RANDOM_FOREST
Task : CLASSIFICATION
Label : label
Features (2) : feature_1 feature_2
Weights : None
Trained with tuner : No
Model size : 56 kB

Number of records: 4
Number of columns: 3

Number of columns by type:
	NUMERICAL: 2 (66.6667%)
	CATEGORICAL: 1 (33.3333%)

Columns:

NUMERICAL: 2 (66.6667%)
	1: "feature_1" NUMERICAL mean:1.5 min:1 max:2 sd:0.5
	2: "feature_2" NUMERICAL num-nas:2 (50%) mean:0.45 min:0.1 max:0.8 sd:0.35

CATEGORICAL: 1 (33.3333%)
	0: "label" CATEGORICAL has-dict vocab-size:3 no-ood-item most-frequent:"false" 2 (50%)

Terminology:
	nas: Number of non-available (i.e. missing) values.
	ood: Out of dictionary.
	manually-defined: Attribute whose type is manually defined by the user, i.e., the type was not automatically inferred.
	tokenized: The attribute value is obtained through tokenization.
	has-dict: The attribute is attached to a string dictionary e.g. a categorical attribute stored as a string.
	vocab-size: Number of unique values.

The following evaluation is computed on the validation or out-of-bag dataset.

Number of predictions (without weights): 4
Number of predictions (with weights): 4
Task: CLASSIFICATION
Label: label

Accuracy: 0  CI95[W][0 0.527129]
LogLoss: : 1.6165
ErrorRate: : 1

Default Accuracy: : 0.5
Default LogLoss: : 0.693147
Default ErrorRate: : 0.5

Confusion Table:
truth\prediction
       false  true
false      0     2
 true      2     0
Total: 4

Variable importances measure the importance of an input feature for a model.

Those variable importances are computed during training. More, and possibly more informative, variable importances are available when analyzing a model on a test dataset.

Num trees : 300

Only printing the first tree.

Tree #0:
    val:"false" prob:[0.5, 0.5]