► 程式碼範例 / 結構化資料 / 從頭開始的結構化資料分類

從頭開始的結構化資料分類

作者： fchollet
建立日期 2020/06/09
上次修改日期 2020/06/09
描述： 結構化資料的二元分類，包括數值和類別特徵。

ⓘ 此範例使用 Keras 3

在 Colab 中檢視 • GitHub 原始碼

簡介

此範例示範如何從原始 CSV 檔案開始進行結構化資料分類。我們的資料包含數值和類別特徵。我們將使用 Keras 預處理層來正規化數值特徵，並向量化類別特徵。

請注意，此範例應在 TensorFlow 2.5 或更高版本上執行。

資料集

我們的資料集由克里夫蘭診所基金會提供，用於研究心臟病。它是一個包含 303 列的 CSV 檔案。每一列都包含有關患者的資訊（一個樣本），而每一欄都描述患者的屬性（一個特徵）。我們使用這些特徵來預測患者是否患有心臟病（二元分類）。

以下是每個特徵的描述

欄位	描述	特徵類型
年齡	以年為單位的年齡	數值
性別	(1 = 男性；0 = 女性)	類別
CP	胸痛類型 (0, 1, 2, 3, 4)	類別
Trestbpd	靜息血壓（入院時以毫米汞柱為單位）	數值
膽固醇	血清膽固醇，以毫克/分升為單位	數值
FBS	空腹血糖，以 120 毫克/分升為單位 (1 = 真；0 = 假)	類別
RestECG	靜息心電圖結果 (0, 1, 2)	類別
Thalach	達到的最大心率	數值
Exang	運動引起的心絞痛 (1 = 有；0 = 無)	類別
Oldpeak	相對於靜息狀態，運動引起的 ST 段壓低	數值
Slope	運動高峰時 ST 段的斜率	數值
CA	通過螢光鏡觀察到的主要血管數量 (0-3)	數值和類別
Thal	3 = 正常；6 = 固定缺陷；7 = 可逆缺陷	類別
目標	心臟病診斷 (1 = 真；0 = 假)	目標

設定

import os

# TensorFlow is the only backend that supports string inputs.
os.environ["KERAS_BACKEND"] = "tensorflow"

import tensorflow as tf
import pandas as pd
import keras
from keras import layers

準備資料

讓我們下載資料並將其載入到 Pandas 資料框架中

file_url = "http://storage.googleapis.com/download.tensorflow.org/data/heart.csv"
dataframe = pd.read_csv(file_url)

資料集包含 303 個樣本，每個樣本有 14 個欄位（13 個特徵加上目標標籤）

dataframe.shape

(303, 14)

以下是一些樣本的預覽

dataframe.head()

	年齡	性別	cp	trestbps	膽固醇	fbs	restecg	thalach	exang	oldpeak	slope	ca	thal	target
0	63	1	1	145	233	1	2	150	0	2.3	3	0	固定	0
1	67	1	4	160	286	0	2	108	1	1.5	2	3	正常	1
2	67	1	4	120	229	0	2	129	1	2.6	2	2	可逆	0
3	37	1	3	130	250	0	0	187	0	3.5	3	0	正常	0
4	41	0	2	130	204	0	2	172	0	1.4	1	0	正常	0

最後一欄「target」表示患者是否患有心臟病 (1) 或沒有患病 (0)。

讓我們將資料分成訓練集和驗證集

val_dataframe = dataframe.sample(frac=0.2, random_state=1337)
train_dataframe = dataframe.drop(val_dataframe.index)

print(
    f"Using {len(train_dataframe)} samples for training "
    f"and {len(val_dataframe)} for validation"
)

Using 242 samples for training and 61 for validation

讓我們為每個資料框架產生 tf.data.Dataset 物件

def dataframe_to_dataset(dataframe):
    dataframe = dataframe.copy()
    labels = dataframe.pop("target")
    ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
    ds = ds.shuffle(buffer_size=len(dataframe))
    return ds


train_ds = dataframe_to_dataset(train_dataframe)
val_ds = dataframe_to_dataset(val_dataframe)

每個 Dataset 都會產生一個元組 (input, target)，其中 input 是一個特徵字典，而 target 是值 0 或 1

for x, y in train_ds.take(1):
    print("Input:", x)
    print("Target:", y)

Input: {'age': <tf.Tensor: shape=(), dtype=int64, numpy=64>, 'sex': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'cp': <tf.Tensor: shape=(), dtype=int64, numpy=4>, 'trestbps': <tf.Tensor: shape=(), dtype=int64, numpy=128>, 'chol': <tf.Tensor: shape=(), dtype=int64, numpy=263>, 'fbs': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'restecg': <tf.Tensor: shape=(), dtype=int64, numpy=0>, 'thalach': <tf.Tensor: shape=(), dtype=int64, numpy=105>, 'exang': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'oldpeak': <tf.Tensor: shape=(), dtype=float64, numpy=0.2>, 'slope': <tf.Tensor: shape=(), dtype=int64, numpy=2>, 'ca': <tf.Tensor: shape=(), dtype=int64, numpy=1>, 'thal': <tf.Tensor: shape=(), dtype=string, numpy=b'reversible'>}
Target: tf.Tensor(0, shape=(), dtype=int64)

讓我們將資料集分批

train_ds = train_ds.batch(32)
val_ds = val_ds.batch(32)

使用 Keras 層進行特徵預處理

以下特徵是編碼為整數的類別特徵

性別
cp
fbs
restecg
exang
ca

我們將使用單熱編碼來編碼這些特徵。我們在這裡有兩種選擇

使用 CategoryEncoding()，這需要知道輸入值的範圍，並且會對超出範圍的輸入產生錯誤。
使用 IntegerLookup()，這將為輸入建立一個查閱表，並為未知的輸入值保留一個輸出索引。

對於此範例，我們希望有一個簡單的解決方案，可以處理推論時超出範圍的輸入，因此我們將使用 IntegerLookup()。

我們還有一個編碼為字串的類別特徵：thal。我們將建立所有可能特徵的索引，並使用 StringLookup() 層來編碼輸出。

最後，以下特徵是連續數值特徵

年齡
trestbps
膽固醇
thalach
oldpeak
slope

對於每個特徵，我們將使用 Normalization() 層來確保每個特徵的平均值為 0，而其標準差為 1。

以下，我們定義 2 個實用函數來執行這些操作

encode_numerical_feature 將特徵正規化應用於數值特徵。
encode_categorical_feature 將單熱編碼應用於字串或整數類別特徵。

def encode_numerical_feature(feature, name, dataset):
    # Create a Normalization layer for our feature
    normalizer = layers.Normalization()

    # Prepare a Dataset that only yields our feature
    feature_ds = dataset.map(lambda x, y: x[name])
    feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))

    # Learn the statistics of the data
    normalizer.adapt(feature_ds)

    # Normalize the input feature
    encoded_feature = normalizer(feature)
    return encoded_feature


def encode_categorical_feature(feature, name, dataset, is_string):
    lookup_class = layers.StringLookup if is_string else layers.IntegerLookup
    # Create a lookup layer which will turn strings into integer indices
    lookup = lookup_class(output_mode="binary")

    # Prepare a Dataset that only yields our feature
    feature_ds = dataset.map(lambda x, y: x[name])
    feature_ds = feature_ds.map(lambda x: tf.expand_dims(x, -1))

    # Learn the set of possible string values and assign them a fixed integer index
    lookup.adapt(feature_ds)

    # Turn the string input into integer indices
    encoded_feature = lookup(feature)
    return encoded_feature

建立模型

完成此操作後，我們可以建立端到端模型

# Categorical features encoded as integers
sex = keras.Input(shape=(1,), name="sex", dtype="int64")
cp = keras.Input(shape=(1,), name="cp", dtype="int64")
fbs = keras.Input(shape=(1,), name="fbs", dtype="int64")
restecg = keras.Input(shape=(1,), name="restecg", dtype="int64")
exang = keras.Input(shape=(1,), name="exang", dtype="int64")
ca = keras.Input(shape=(1,), name="ca", dtype="int64")

# Categorical feature encoded as string
thal = keras.Input(shape=(1,), name="thal", dtype="string")

# Numerical features
age = keras.Input(shape=(1,), name="age")
trestbps = keras.Input(shape=(1,), name="trestbps")
chol = keras.Input(shape=(1,), name="chol")
thalach = keras.Input(shape=(1,), name="thalach")
oldpeak = keras.Input(shape=(1,), name="oldpeak")
slope = keras.Input(shape=(1,), name="slope")

all_inputs = [
    sex,
    cp,
    fbs,
    restecg,
    exang,
    ca,
    thal,
    age,
    trestbps,
    chol,
    thalach,
    oldpeak,
    slope,
]

# Integer categorical features
sex_encoded = encode_categorical_feature(sex, "sex", train_ds, False)
cp_encoded = encode_categorical_feature(cp, "cp", train_ds, False)
fbs_encoded = encode_categorical_feature(fbs, "fbs", train_ds, False)
restecg_encoded = encode_categorical_feature(restecg, "restecg", train_ds, False)
exang_encoded = encode_categorical_feature(exang, "exang", train_ds, False)
ca_encoded = encode_categorical_feature(ca, "ca", train_ds, False)

# String categorical features
thal_encoded = encode_categorical_feature(thal, "thal", train_ds, True)

# Numerical features
age_encoded = encode_numerical_feature(age, "age", train_ds)
trestbps_encoded = encode_numerical_feature(trestbps, "trestbps", train_ds)
chol_encoded = encode_numerical_feature(chol, "chol", train_ds)
thalach_encoded = encode_numerical_feature(thalach, "thalach", train_ds)
oldpeak_encoded = encode_numerical_feature(oldpeak, "oldpeak", train_ds)
slope_encoded = encode_numerical_feature(slope, "slope", train_ds)

all_features = layers.concatenate(
    [
        sex_encoded,
        cp_encoded,
        fbs_encoded,
        restecg_encoded,
        exang_encoded,
        slope_encoded,
        ca_encoded,
        thal_encoded,
        age_encoded,
        trestbps_encoded,
        chol_encoded,
        thalach_encoded,
        oldpeak_encoded,
    ]
)
x = layers.Dense(32, activation="relu")(all_features)
x = layers.Dropout(0.5)(x)
output = layers.Dense(1, activation="sigmoid")(x)
model = keras.Model(all_inputs, output)
model.compile("adam", "binary_crossentropy", metrics=["accuracy"])

讓我們視覺化我們的連接圖

# `rankdir='LR'` is to make the graph horizontal.
keras.utils.plot_model(model, show_shapes=True, rankdir="LR")

png

訓練模型

model.fit(train_ds, epochs=50, validation_data=val_ds)

Epoch 1/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 5s 46ms/step - accuracy: 0.3932 - loss: 0.8749 - val_accuracy: 0.3303 - val_loss: 0.7814
Epoch 2/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 1s 7ms/step - accuracy: 0.4262 - loss: 0.8375 - val_accuracy: 0.4914 - val_loss: 0.6980
Epoch 3/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.4835 - loss: 0.7350 - val_accuracy: 0.6541 - val_loss: 0.6320
Epoch 4/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5932 - loss: 0.6665 - val_accuracy: 0.7543 - val_loss: 0.5743
Epoch 5/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.5861 - loss: 0.6600 - val_accuracy: 0.7683 - val_loss: 0.5360
Epoch 6/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.6489 - loss: 0.6020 - val_accuracy: 0.7748 - val_loss: 0.4998
Epoch 7/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.6880 - loss: 0.5668 - val_accuracy: 0.7699 - val_loss: 0.4800
Epoch 8/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7572 - loss: 0.5009 - val_accuracy: 0.7559 - val_loss: 0.4573
Epoch 9/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7492 - loss: 0.5192 - val_accuracy: 0.8060 - val_loss: 0.4414
Epoch 10/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.7212 - loss: 0.4973 - val_accuracy: 0.8077 - val_loss: 0.4259
Epoch 11/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7616 - loss: 0.4704 - val_accuracy: 0.7904 - val_loss: 0.4143
Epoch 12/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8374 - loss: 0.4342 - val_accuracy: 0.7872 - val_loss: 0.4061
Epoch 13/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7863 - loss: 0.4630 - val_accuracy: 0.7888 - val_loss: 0.3980
Epoch 14/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7742 - loss: 0.4492 - val_accuracy: 0.7996 - val_loss: 0.3998
Epoch 15/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8083 - loss: 0.4280 - val_accuracy: 0.8060 - val_loss: 0.3855
Epoch 16/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8058 - loss: 0.4191 - val_accuracy: 0.8217 - val_loss: 0.3819
Epoch 17/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8071 - loss: 0.4111 - val_accuracy: 0.8389 - val_loss: 0.3763
Epoch 18/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8533 - loss: 0.3676 - val_accuracy: 0.8373 - val_loss: 0.3792
Epoch 19/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8170 - loss: 0.3850 - val_accuracy: 0.8357 - val_loss: 0.3744
Epoch 20/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8207 - loss: 0.3767 - val_accuracy: 0.8168 - val_loss: 0.3759
Epoch 21/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8151 - loss: 0.3596 - val_accuracy: 0.8217 - val_loss: 0.3685
Epoch 22/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.7988 - loss: 0.4087 - val_accuracy: 0.8184 - val_loss: 0.3701
Epoch 23/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8180 - loss: 0.3632 - val_accuracy: 0.8217 - val_loss: 0.3614
Epoch 24/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8295 - loss: 0.3504 - val_accuracy: 0.8200 - val_loss: 0.3683
Epoch 25/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8386 - loss: 0.3864 - val_accuracy: 0.8200 - val_loss: 0.3655
Epoch 26/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8482 - loss: 0.3345 - val_accuracy: 0.8044 - val_loss: 0.3639
Epoch 27/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8340 - loss: 0.3470 - val_accuracy: 0.8077 - val_loss: 0.3616
Epoch 28/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8418 - loss: 0.3684 - val_accuracy: 0.8060 - val_loss: 0.3629
Epoch 29/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8309 - loss: 0.3147 - val_accuracy: 0.8060 - val_loss: 0.3637
Epoch 30/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8722 - loss: 0.3151 - val_accuracy: 0.8044 - val_loss: 0.3672
Epoch 31/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8746 - loss: 0.3043 - val_accuracy: 0.8060 - val_loss: 0.3637
Epoch 32/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8794 - loss: 0.3245 - val_accuracy: 0.8200 - val_loss: 0.3685
Epoch 33/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8644 - loss: 0.3541 - val_accuracy: 0.8357 - val_loss: 0.3714
Epoch 34/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8867 - loss: 0.3007 - val_accuracy: 0.8373 - val_loss: 0.3680
Epoch 35/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8737 - loss: 0.3168 - val_accuracy: 0.8357 - val_loss: 0.3695
Epoch 36/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8191 - loss: 0.3298 - val_accuracy: 0.8357 - val_loss: 0.3736
Epoch 37/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8613 - loss: 0.3543 - val_accuracy: 0.8357 - val_loss: 0.3745
Epoch 38/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8835 - loss: 0.2835 - val_accuracy: 0.8357 - val_loss: 0.3707
Epoch 39/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8784 - loss: 0.2893 - val_accuracy: 0.8357 - val_loss: 0.3716
Epoch 40/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8919 - loss: 0.2587 - val_accuracy: 0.8168 - val_loss: 0.3770
Epoch 41/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8882 - loss: 0.2660 - val_accuracy: 0.8217 - val_loss: 0.3674
Epoch 42/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8790 - loss: 0.2931 - val_accuracy: 0.8200 - val_loss: 0.3723
Epoch 43/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8851 - loss: 0.2892 - val_accuracy: 0.8200 - val_loss: 0.3733
Epoch 44/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8504 - loss: 0.3189 - val_accuracy: 0.8200 - val_loss: 0.3755
Epoch 45/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8610 - loss: 0.3116 - val_accuracy: 0.8184 - val_loss: 0.3788
Epoch 46/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8956 - loss: 0.2544 - val_accuracy: 0.8184 - val_loss: 0.3738
Epoch 47/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.9080 - loss: 0.2895 - val_accuracy: 0.8217 - val_loss: 0.3750
Epoch 48/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8706 - loss: 0.2993 - val_accuracy: 0.8217 - val_loss: 0.3757
Epoch 49/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 7ms/step - accuracy: 0.8724 - loss: 0.2979 - val_accuracy: 0.8184 - val_loss: 0.3781
Epoch 50/50
 8/8 ━━━━━━━━━━━━━━━━━━━━ 0s 8ms/step - accuracy: 0.8609 - loss: 0.2937 - val_accuracy: 0.8217 - val_loss: 0.3791

<keras.src.callbacks.history.History at 0x7efc32e01780>

我們很快就達到 80% 的驗證準確度。

在新資料上進行推論

若要取得新樣本的預測，您只需呼叫 model.predict()。您只需要做兩件事

將純量包裝到列表中，以具有批次維度（模型僅處理批次資料，而不處理單個樣本）
對每個特徵呼叫 convert_to_tensor

sample = {
    "age": 60,
    "sex": 1,
    "cp": 1,
    "trestbps": 145,
    "chol": 233,
    "fbs": 1,
    "restecg": 2,
    "thalach": 150,
    "exang": 0,
    "oldpeak": 2.3,
    "slope": 3,
    "ca": 0,
    "thal": "fixed",
}

input_dict = {name: tf.convert_to_tensor([value]) for name, value in sample.items()}
predictions = model.predict(input_dict)

print(
    f"This particular patient had a {100 * predictions[0][0]:.1f} "
    "percent probability of having a heart disease, "
    "as evaluated by our model."
)

 1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 252ms/step
This particular patient had a 27.6 percent probability of having a heart disease, as evaluated by our model.

從頭開始的結構化資料分類

◆ 簡介

資料集

◆ 設定

◆ 準備資料

◆ 使用 Keras 層進行特徵預處理

◆ 建立模型

◆ 訓練模型

◆ 在新資料上進行推論