46 KiB
46 KiB
Potrzebne importy
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.metrics import classification_report, accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from tensorflow import keras
from tensorflow.keras import layers
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.preprocessing import StandardScaler, LabelEncoderOdczytywanie danych
df = pd.read_csv('bodyPerformance.csv')df.head()| age | gender | height_cm | weight_kg | body fat_% | diastolic | systolic | gripForce | sit and bend forward_cm | sit-ups counts | broad jump_cm | class | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 27.0 | M | 172.3 | 75.24 | 21.3 | 80.0 | 130.0 | 54.9 | 18.4 | 60.0 | 217.0 | C |
| 1 | 25.0 | M | 165.0 | 55.80 | 15.7 | 77.0 | 126.0 | 36.4 | 16.3 | 53.0 | 229.0 | A |
| 2 | 31.0 | M | 179.6 | 78.00 | 20.1 | 92.0 | 152.0 | 44.8 | 12.0 | 49.0 | 181.0 | C |
| 3 | 32.0 | M | 174.5 | 71.10 | 18.4 | 76.0 | 147.0 | 41.4 | 15.2 | 53.0 | 219.0 | B |
| 4 | 28.0 | M | 173.8 | 67.70 | 17.1 | 70.0 | 127.0 | 43.5 | 27.1 | 45.0 | 217.0 | B |
df.info()<class 'pandas.core.frame.DataFrame'> RangeIndex: 13393 entries, 0 to 13392 Data columns (total 12 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 age 13393 non-null float64 1 gender 13393 non-null object 2 height_cm 13393 non-null float64 3 weight_kg 13393 non-null float64 4 body fat_% 13393 non-null float64 5 diastolic 13393 non-null float64 6 systolic 13393 non-null float64 7 gripForce 13393 non-null float64 8 sit and bend forward_cm 13393 non-null float64 9 sit-ups counts 13393 non-null float64 10 broad jump_cm 13393 non-null float64 11 class 13393 non-null object dtypes: float64(10), object(2) memory usage: 1.2+ MB
Przygotowanie danych
df.isna().sum()age 0 gender 0 height_cm 0 weight_kg 0 body fat_% 0 diastolic 0 systolic 0 gripForce 0 sit and bend forward_cm 0 sit-ups counts 0 broad jump_cm 0 class 0 dtype: int64
df['gender'].describe()count 13393 unique 2 top M freq 8467 Name: gender, dtype: object
df = pd.get_dummies(df, columns=['gender'])df.head()| age | height_cm | weight_kg | body fat_% | diastolic | systolic | gripForce | sit and bend forward_cm | sit-ups counts | broad jump_cm | class | gender_F | gender_M | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 27.0 | 172.3 | 75.24 | 21.3 | 80.0 | 130.0 | 54.9 | 18.4 | 60.0 | 217.0 | C | False | True |
| 1 | 25.0 | 165.0 | 55.80 | 15.7 | 77.0 | 126.0 | 36.4 | 16.3 | 53.0 | 229.0 | A | False | True |
| 2 | 31.0 | 179.6 | 78.00 | 20.1 | 92.0 | 152.0 | 44.8 | 12.0 | 49.0 | 181.0 | C | False | True |
| 3 | 32.0 | 174.5 | 71.10 | 18.4 | 76.0 | 147.0 | 41.4 | 15.2 | 53.0 | 219.0 | B | False | True |
| 4 | 28.0 | 173.8 | 67.70 | 17.1 | 70.0 | 127.0 | 43.5 | 27.1 | 45.0 | 217.0 | B | False | True |
df['gender_F'] = df['gender_F'].map({True:1, False:0})
df['gender_M'] = df['gender_M'].map({True:1, False:0}) df.head()| age | height_cm | weight_kg | body fat_% | diastolic | systolic | gripForce | sit and bend forward_cm | sit-ups counts | broad jump_cm | class | gender_F | gender_M | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 27.0 | 172.3 | 75.24 | 21.3 | 80.0 | 130.0 | 54.9 | 18.4 | 60.0 | 217.0 | C | 0 | 1 |
| 1 | 25.0 | 165.0 | 55.80 | 15.7 | 77.0 | 126.0 | 36.4 | 16.3 | 53.0 | 229.0 | A | 0 | 1 |
| 2 | 31.0 | 179.6 | 78.00 | 20.1 | 92.0 | 152.0 | 44.8 | 12.0 | 49.0 | 181.0 | C | 0 | 1 |
| 3 | 32.0 | 174.5 | 71.10 | 18.4 | 76.0 | 147.0 | 41.4 | 15.2 | 53.0 | 219.0 | B | 0 | 1 |
| 4 | 28.0 | 173.8 | 67.70 | 17.1 | 70.0 | 127.0 | 43.5 | 27.1 | 45.0 | 217.0 | B | 0 | 1 |
Podział na zbiory uczące i testowe oraz skalowanie
y = df['class']
X = df.drop('class', axis =1)X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)X_train.shape[0]10714
X_test.shape[0]2679
Regresja logistyczna
logistic_model = LogisticRegression()logistic_model.fit(X_train, y_train)LogisticRegression()In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
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LogisticRegression()
logistic_predicts = logistic_model.predict(X_test)cr = classification_report(y_test, logistic_predicts)
accuracy = accuracy_score(y_test, logistic_predicts)print(cr)
print('accuracy: ',accuracy) precision recall f1-score support
A 0.70 0.71 0.71 685
B 0.45 0.44 0.45 662
C 0.52 0.53 0.53 650
D 0.79 0.78 0.78 682
accuracy 0.62 2679
macro avg 0.62 0.62 0.62 2679
weighted avg 0.62 0.62 0.62 2679
accuracy: 0.6188876446435237
Naiwny klasyfikator Bayesa
bayes_model = GaussianNB().fit(X_train, y_train)bayes_predicts = bayes_model.predict(X_test)report = classification_report(y_test, bayes_predicts)
accuracy = accuracy_score(y_test, bayes_predicts)print(report)
print('accuracy: ',accuracy) precision recall f1-score support
A 0.59 0.74 0.66 685
B 0.41 0.30 0.34 662
C 0.46 0.46 0.46 650
D 0.68 0.69 0.68 682
accuracy 0.55 2679
macro avg 0.53 0.55 0.54 2679
weighted avg 0.54 0.55 0.54 2679
accuracy: 0.5487122060470325
Klasyfikator najbliższych sąsiadów
KNN = KNeighborsClassifier(n_neighbors=8).fit(X_train, y_train)knn_predicts = KNN.predict(X_test)report = classification_report(y_test, knn_predicts)
accuracy = accuracy_score(y_test, knn_predicts)print(report)
print('accuracy: ',accuracy) precision recall f1-score support
A 0.62 0.82 0.71 685
B 0.43 0.47 0.45 662
C 0.56 0.51 0.54 650
D 0.91 0.64 0.75 682
accuracy 0.61 2679
macro avg 0.63 0.61 0.61 2679
weighted avg 0.64 0.61 0.61 2679
accuracy: 0.6114221724524076
Drzewo decyzyjne
label_encoder = LabelEncoder()
y_train = label_encoder.fit_transform(y_train)
y_test = label_encoder.transform(y_test)tree_model = DecisionTreeClassifier(max_depth=9).fit(X_train,y_train)tree_predicts = tree_model.predict(X_test)report = classification_report(y_test, tree_predicts)
accuracy = accuracy_score(y_test, tree_predicts)print(report)
print('accuracy: ',accuracy) precision recall f1-score support
0 0.69 0.82 0.75 685
1 0.54 0.57 0.56 662
2 0.66 0.59 0.62 650
3 0.88 0.76 0.81 682
accuracy 0.69 2679
macro avg 0.69 0.69 0.69 2679
weighted avg 0.70 0.69 0.69 2679
accuracy: 0.6875699888017918
Sieć neuronowa
model = keras.Sequential(
[
keras.Input(shape=(12,)),
layers.Dense(128, activation="relu"),
layers.Dense(128, activation="relu"),
layers.Dense(128, activation="relu"),
layers.Dense(4, activation="softmax"),
]
)
model.summary()Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
dense (Dense) (None, 128) 1664
dense_1 (Dense) (None, 128) 16512
dense_2 (Dense) (None, 128) 16512
dense_3 (Dense) (None, 4) 516
=================================================================
Total params: 35,204
Trainable params: 35,204
Non-trainable params: 0
_________________________________________________________________
model.compile(loss="sparse_categorical_crossentropy", optimizer="adam", metrics=["accuracy"])
model.fit(X_train, y_train, batch_size=128, epochs=50, validation_split=0.1)Epoch 1/50 76/76 [==============================] - 1s 7ms/step - loss: 0.9884 - accuracy: 0.5528 - val_loss: 0.8428 - val_accuracy: 0.6287 Epoch 2/50 76/76 [==============================] - 0s 3ms/step - loss: 0.8034 - accuracy: 0.6459 - val_loss: 0.7855 - val_accuracy: 0.6670 Epoch 3/50 76/76 [==============================] - 0s 3ms/step - loss: 0.7376 - accuracy: 0.6876 - val_loss: 0.7341 - val_accuracy: 0.6931 Epoch 4/50 76/76 [==============================] - 0s 3ms/step - loss: 0.6971 - accuracy: 0.7119 - val_loss: 0.7143 - val_accuracy: 0.7146 Epoch 5/50 76/76 [==============================] - 0s 3ms/step - loss: 0.6715 - accuracy: 0.7206 - val_loss: 0.6984 - val_accuracy: 0.7201 Epoch 6/50 76/76 [==============================] - 0s 2ms/step - loss: 0.6511 - accuracy: 0.7301 - val_loss: 0.6901 - val_accuracy: 0.7164 Epoch 7/50 76/76 [==============================] - 0s 3ms/step - loss: 0.6375 - accuracy: 0.7359 - val_loss: 0.6782 - val_accuracy: 0.7285 Epoch 8/50 76/76 [==============================] - 0s 3ms/step - loss: 0.6239 - accuracy: 0.7410 - val_loss: 0.7001 - val_accuracy: 0.7062 Epoch 9/50 76/76 [==============================] - 0s 3ms/step - loss: 0.6144 - accuracy: 0.7483 - val_loss: 0.6578 - val_accuracy: 0.7304 Epoch 10/50 76/76 [==============================] - 0s 3ms/step - loss: 0.6059 - accuracy: 0.7492 - val_loss: 0.6606 - val_accuracy: 0.7183 Epoch 11/50 76/76 [==============================] - 0s 3ms/step - loss: 0.6000 - accuracy: 0.7541 - val_loss: 0.6597 - val_accuracy: 0.7341 Epoch 12/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5942 - accuracy: 0.7582 - val_loss: 0.6617 - val_accuracy: 0.7183 Epoch 13/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5856 - accuracy: 0.7586 - val_loss: 0.6732 - val_accuracy: 0.7285 Epoch 14/50 76/76 [==============================] - 0s 5ms/step - loss: 0.5807 - accuracy: 0.7627 - val_loss: 0.6709 - val_accuracy: 0.7369 Epoch 15/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5731 - accuracy: 0.7659 - val_loss: 0.6618 - val_accuracy: 0.7416 Epoch 16/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5665 - accuracy: 0.7694 - val_loss: 0.6483 - val_accuracy: 0.7463 Epoch 17/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5620 - accuracy: 0.7694 - val_loss: 0.6635 - val_accuracy: 0.7220 Epoch 18/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5555 - accuracy: 0.7709 - val_loss: 0.6493 - val_accuracy: 0.7425 Epoch 19/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5523 - accuracy: 0.7715 - val_loss: 0.6649 - val_accuracy: 0.7313 Epoch 20/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5489 - accuracy: 0.7771 - val_loss: 0.6862 - val_accuracy: 0.7164 Epoch 21/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5409 - accuracy: 0.7801 - val_loss: 0.6567 - val_accuracy: 0.7435 Epoch 22/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5373 - accuracy: 0.7784 - val_loss: 0.6638 - val_accuracy: 0.7285 Epoch 23/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5316 - accuracy: 0.7838 - val_loss: 0.6582 - val_accuracy: 0.7407 Epoch 24/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5308 - accuracy: 0.7857 - val_loss: 0.6762 - val_accuracy: 0.7285 Epoch 25/50 76/76 [==============================] - 0s 2ms/step - loss: 0.5279 - accuracy: 0.7844 - val_loss: 0.6775 - val_accuracy: 0.7229 Epoch 26/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5165 - accuracy: 0.7884 - val_loss: 0.6599 - val_accuracy: 0.7257 Epoch 27/50 76/76 [==============================] - 0s 2ms/step - loss: 0.5186 - accuracy: 0.7899 - val_loss: 0.6610 - val_accuracy: 0.7369 Epoch 28/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5117 - accuracy: 0.7903 - val_loss: 0.6590 - val_accuracy: 0.7388 Epoch 29/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5162 - accuracy: 0.7883 - val_loss: 0.6800 - val_accuracy: 0.7211 Epoch 30/50 76/76 [==============================] - 0s 3ms/step - loss: 0.5048 - accuracy: 0.7945 - val_loss: 0.6595 - val_accuracy: 0.7379 Epoch 31/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4991 - accuracy: 0.7955 - val_loss: 0.6911 - val_accuracy: 0.7146 Epoch 32/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4927 - accuracy: 0.8012 - val_loss: 0.6630 - val_accuracy: 0.7332 Epoch 33/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4943 - accuracy: 0.8029 - val_loss: 0.6822 - val_accuracy: 0.7285 Epoch 34/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4825 - accuracy: 0.8061 - val_loss: 0.6837 - val_accuracy: 0.7285 Epoch 35/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4813 - accuracy: 0.8067 - val_loss: 0.6821 - val_accuracy: 0.7267 Epoch 36/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4830 - accuracy: 0.8034 - val_loss: 0.6730 - val_accuracy: 0.7379 Epoch 37/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4725 - accuracy: 0.8099 - val_loss: 0.7114 - val_accuracy: 0.7295 Epoch 38/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4729 - accuracy: 0.8085 - val_loss: 0.7065 - val_accuracy: 0.7183 Epoch 39/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4679 - accuracy: 0.8136 - val_loss: 0.6859 - val_accuracy: 0.7248 Epoch 40/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4683 - accuracy: 0.8111 - val_loss: 0.7185 - val_accuracy: 0.7108 Epoch 41/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4571 - accuracy: 0.8174 - val_loss: 0.7018 - val_accuracy: 0.7276 Epoch 42/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4459 - accuracy: 0.8218 - val_loss: 0.7098 - val_accuracy: 0.7127 Epoch 43/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4492 - accuracy: 0.8227 - val_loss: 0.7279 - val_accuracy: 0.7248 Epoch 44/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4493 - accuracy: 0.8253 - val_loss: 0.7087 - val_accuracy: 0.7211 Epoch 45/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4405 - accuracy: 0.8242 - val_loss: 0.7006 - val_accuracy: 0.7155 Epoch 46/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4327 - accuracy: 0.8279 - val_loss: 0.7278 - val_accuracy: 0.7155 Epoch 47/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4324 - accuracy: 0.8272 - val_loss: 0.7120 - val_accuracy: 0.7267 Epoch 48/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4275 - accuracy: 0.8313 - val_loss: 0.7302 - val_accuracy: 0.7090 Epoch 49/50 76/76 [==============================] - 0s 4ms/step - loss: 0.4230 - accuracy: 0.8335 - val_loss: 0.7484 - val_accuracy: 0.7183 Epoch 50/50 76/76 [==============================] - 0s 3ms/step - loss: 0.4182 - accuracy: 0.8354 - val_loss: 0.7354 - val_accuracy: 0.7164
<keras.callbacks.History at 0x260d1488370>
neural_predicts = model.predict(X_test)84/84 [==============================] - 0s 1ms/step
neural_predicts_labels = np.argmax(neural_predicts, axis=1)report = classification_report(y_test, neural_predicts_labels)
accuracy = accuracy_score(y_test, neural_predicts_labels)print(report)
print('accuracy: ',accuracy) precision recall f1-score support
0 0.75 0.84 0.79 685
1 0.60 0.65 0.62 662
2 0.72 0.62 0.67 650
3 0.89 0.84 0.86 682
accuracy 0.74 2679
macro avg 0.74 0.74 0.74 2679
weighted avg 0.74 0.74 0.74 2679
accuracy: 0.7375886524822695