分类特征¶

特征的处理方式取决于其语义，例如数值、分类、布尔或文本。如果未指定语义，则会自动推断。例如，浮点和整数特征会被检测为数值特征，而字符串则会被检测为分类特征。

分类特征表示有限可能值集合中的一种类型或类别，且这些值之间没有顺序关系。例如，集合 {RED, BLUE, GREEN} 中的颜色 RED 就是一个分类特征。分类特征可以是字符串、字节字面量、整数或布尔值。缺失值用 ""（空字符串）表示。

让我们训练一个使用分类字符串特征的例子。

In [ ]

import ydf
import pandas as pd

import ydf import pandas as pd

In [ ]

dataset = pd.DataFrame({
    "label": [True, False, True, False],
    "feature_1": ["red", "red", "blue", "green"],
    "feature_2": ["hot", "hot", "cold", ""],
})

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

dataset = pd.DataFrame({ "label": [True, False, True, False], "feature_1": ["red", "red", "blue", "green"], "feature_2": ["hot", "hot", "cold", ""], }) model = ydf.RandomForestLearner(label="label").train(dataset)

Train model on 4 examples
Model trained in 0:00:00.008941

我们可以看到这些特征在 Dataspec 标签页中被检测为分类特征。

In [ ]

model.describe()

model.describe()

Out[ ]

模型数据规范训练变量重要性结构

名称 : RANDOM_FOREST
任务 : CLASSIFICATION
标签 : label
特征 (2) : feature_1 feature_2
权重 : 无
使用调参器训练 : 否
模型大小 : 57 kB

Number of records: 4
Number of columns: 3

Number of columns by type:
	CATEGORICAL: 3 (100%)

Columns:

CATEGORICAL: 3 (100%)
	0: "label" CATEGORICAL has-dict vocab-size:3 no-ood-item most-frequent:"false" 2 (50%)
	1: "feature_1" CATEGORICAL has-dict vocab-size:1 num-oods:4 (100%)
	2: "feature_2" CATEGORICAL num-nas:1 (25%) has-dict vocab-size:1 num-oods:3 (100%)

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.

以下评估是基于验证集或袋外数据集计算的。

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

Accuracy: 0  CI95[W][0 0.527129]
LogLoss: : 1.57374
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

变量重要性衡量输入特征对模型的重要性。

INV_MEAN_MIN_DEPTHNUM_AS_ROOTNUM_NODESSUM_SCORE

这些变量重要性在训练期间计算。在测试数据集上分析模型时，可以获得更多（可能更具信息量）的变量重要性。

树的数量 : 300

仅打印第一棵树。

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

有时，您可能想要强制某个特征的语义为分类特征。

在下一个例子中，"feature_1" 和 "feature_2" 是整数，因此它们将被自动检测为数值特征。但是，我们希望 "feature_1" 被检测为分类特征。

在模型描述中，注意 "feature_1" 是分类特征，而 "feature_2" 是数值特征。

In [ ]

dataset = pd.DataFrame({
    "label": [True, False, True, False],
    "feature_1": [1, 2, 2, 1],
    "feature_2": [5, 6, 7, 6],
})

model = ydf.RandomForestLearner(label="label",
                                features=[ydf.Feature("feature_1", ydf.Semantic.CATEGORICAL)],
                                include_all_columns=True,
                                ).train(dataset)
# Note: include_all_columns=True allows the model to use all the
# columns as features, not just the ones in "features".

model.describe()

dataset = pd.DataFrame({ "label": [True, False, True, False], "feature_1": [1, 2, 2, 1], "feature_2": [5, 6, 7, 6], }) model = ydf.RandomForestLearner(label="label", features=[ydf.Feature("feature_1", ydf.Semantic.CATEGORICAL)], include_all_columns=True, ).train(dataset) # 注意：include_all_columns=True 允许模型使用所有列作为特征，而不仅仅是 "features" 中指定的列。 model.describe()

Train model on 4 examples
Model trained in 0:00:00.004352

Out[ ]

模型数据规范训练变量重要性结构

名称 : RANDOM_FOREST
任务 : CLASSIFICATION
标签 : label
特征 (2) : feature_1 feature_2
权重 : 无
使用调参器训练 : 否
模型大小 : 57 kB

Number of records: 4
Number of columns: 3

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

Columns:

CATEGORICAL: 2 (66.6667%)
	0: "feature_1" CATEGORICAL has-dict vocab-size:1 num-oods:4 (100%)
	1: "label" CATEGORICAL has-dict vocab-size:3 no-ood-item most-frequent:"false" 2 (50%)

NUMERICAL: 1 (33.3333%)
	2: "feature_2" NUMERICAL mean:6 min:5 max:7 sd:0.707107

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.

以下评估是基于验证集或袋外数据集计算的。

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

Accuracy: 0  CI95[W][0 0.527129]
LogLoss: : 1.57805
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

变量重要性衡量输入特征对模型的重要性。

INV_MEAN_MIN_DEPTHNUM_AS_ROOTNUM_NODESSUM_SCORE

这些变量重要性在训练期间计算。在测试数据集上分析模型时，可以获得更多（可能更具信息量）的变量重要性。

树的数量 : 300

仅打印第一棵树。

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