【调包侠的机器学习】医疗保险线性回归

```python # https://www.kaggle.com/datasets/mirichoi0218/insurance import numpy as np import tensorflow as tf import pandas as pd ``` ```python def mean_norm(df_input): #@save return df_input.apply(lambda x: (x-x.mean())/ x.std(), axis=0) ``` ```python def de_mean_norm(result, df_input:pd.DataFrame): #@save return result * df_input.std() + df_input.mean() ``` ```python insurenceData = pd.read_csv("../data/insurance.csv") insurenceData.head(10) ``` ```python sex_mapping = {'female':0, 'male':1} insurenceData['sex'] =insurenceData['sex'].map(sex_mapping) ``` ```python insurenceData.head(10) ``` ```python # insurenceData['region'].value_counts() region_mapping = {'southeast':0, 'southwest':1, "northwest": 2, "northeast": 3} insurenceData.region =insurenceData.region.map(region_mapping) insurenceData.head(10) ``` ```python # insurenceData['smoker'].value_counts() smoker_mapping = {"yes": 1, "no": 0} insurenceData.smoker =insurenceData.smoker.map(smoker_mapping) insurenceData.head(100) ``` ```python rData = mean_norm(insurenceData) # rData = insurenceData # print(rData) Y = rData['charges'] print('1charges: ', Y) x = rData.drop(columns='charges') X = tf.convert_to_tensor(x) print('charges: ', Y) y = tf.convert_to_tensor(Y) print(X,y) ``` 1charges: 0 0.298472 1 -0.953333 2 -0.728402 3 0.719574 4 -0.776512 ... 1333 -0.220468 1334 -0.913661 1335 -0.961237 1336 -0.930014 1337 1.310563 Name: charges, Length: 1338, dtype: float64 charges: 0 0.298472 1 -0.953333 2 -0.728402 3 0.719574 4 -0.776512 ... 1333 -0.220468 1334 -0.913661 1335 -0.961237 1336 -0.930014 1337 1.310563 Name: charges, Length: 1338, dtype: float64 tf.Tensor( [[-1.4382265 -1.010141 -0.45315057 -0.90827406 1.9698501 -0.40272369] [-1.50940108 0.98922092 0.50943062 -0.07873775 -0.50727343 -1.2875255 ] [-0.7976553 0.98922092 0.38316358 1.58033487 -0.50727343 -1.2875255 ] ... [-1.50940108 -1.010141 1.01449877 -0.90827406 -0.50727343 -1.2875255 ] [-1.29587735 -1.010141 -0.79751522 -0.90827406 -0.50727343 -0.40272369] [ 1.55110577 -1.010141 -0.26129026 -0.90827406 1.9698501 0.48207812]], shape=(1338, 6), dtype=float64) tf.Tensor( [ 0.2984722 -0.95333272 -0.72840232 ... -0.96123683 -0.93001377 1.31056344], shape=(1338,), dtype=float64) ```python net = tf.keras.Sequential() net.add(tf.keras.layers.Dense(units=1, input_dim=6)) initializer = tf.initializers.RandomNormal(stddev=0.1) net.add(tf.keras.layers.Dense(1, kernel_initializer=initializer)) opt = tf.keras.optimizers.Adam(learning_rate=0.001) # metrics=[tf.keras.metrics.SparseCategoricalAccuracy()] net.compile(optimizer=opt, loss=tf.keras.losses.MeanSquaredError(), metrics=[tf.keras.metrics.SparseCategoricalAccuracy()]) ``` ```python history = net.fit(X, y, batch_size=1000, epochs=500, validation_split=0.2, callbacks=[], shuffle= True) net.summary() ``` Epoch 1/500 2/2 [==============================] - 0s 78ms/step - loss: 0.2575 - sparse_categorical_accuracy: 0.0000e+00 - val_loss: 0.2666 - val_sparse_categorical_accuracy: 0.0000e+00 Epoch 2/500 2/2 [==============================] - 0s 56ms/step - loss: 0.2573 - sparse_categorical_accuracy: 0.0000e+00 - val_loss: 0.2663 - val_sparse_categorical_accuracy: 0.0000e+00 Epoch 3/500 2/2 [==============================] - 0s 47ms/step - loss: 0.2570 - sparse_categorical_accuracy: 0.0000e+00 - val_loss: 0.2661 - val_sparse_categorical_accuracy: 0.0000e+00 Epoch 4/500 2/2 [==============================] - 0s 56ms/step - loss: 0.2568 - sparse_categorical_accuracy: 0.0000e+00 - val_loss: 0.2659 - val_sparse_categorical_accuracy: 0.0000e+00 Epoch 5/500 2/2 [==============================] - 0s 30ms/step - loss: 0.2566 - sparse_categorical_accuracy: 0.0000e+00 - val_loss: 0.2656 - val_sparse_categorical_accuracy: 0.0000e+00 Epoch 6/500 2/2 [==============================] - 0s 53ms/step - loss: 0.2565 - sparse_categorical_accuracy: 0.0000e+00 - val_loss: 0.2653 - val_sparse_categorical_accuracy: 0.0000e+00 。。。。。。。。。。。。。。。。。。。。。 2/2 [==============================] - 0s 53ms/step - loss: 0.2484 - sparse_categorical_accuracy: 0.0000e+00 - val_loss: 0.2521 - val_sparse_categorical_accuracy: 0.0000e+00 Epoch 499/500 2/2 [==============================] - 0s 25ms/step - loss: 0.2484 - sparse_categorical_accuracy: 0.0000e+00 - val_loss: 0.2520 - val_sparse_categorical_accuracy: 0.0000e+00 Epoch 500/500 2/2 [==============================] - 0s 54ms/step - loss: 0.2484 - sparse_categorical_accuracy: 0.0000e+00 - val_loss: 0.2520 - val_sparse_categorical_accuracy: 0.0000e+00 Model: "sequential" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense (Dense) (None, 1) 7 dense_1 (Dense) (None, 1) 2 ================================================================= Total params: 9 Trainable params: 9 Non-trainable params: 0 _________________________________________________________________ ```python import matplotlib.pyplot as plt print(history.history.keys()) plt.plot(history.history["loss"], label="Training Loss") plt.plot(history.history["val_loss"], label="Validation Loss") # plt.plot(history.history["sparse_categorical_accuracy"], label="sparse_categorical_accuracy") # plt.plot(history.history["val_sparse_categorical_accuracy"], label="val_sparse_categorical_accuracy") plt.legend() plt.show() ``` dict_keys(['loss', 'sparse_categorical_accuracy', 'val_loss', 'val_sparse_categorical_accuracy'])  ```python pY = net.predict([[19,0,27.900,0,1,1]]) ``` ```python pY ``` array([[11.344333]], dtype=float32) ```python oriY = insurenceData['charges'] ``` ```python oriY.describe() ``` count 1338.000000 mean 13270.422265 std 12110.011237 min 1121.873900 25% 4740.287150 50% 9382.033000 75% 16639.912515 max 63770.428010 Name: charges, dtype: float64 ```python # 反归一化 de_mean_norm(pY, oriY) ``` array([[150650.42]], dtype=float32)