UM project

.gitignore

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+env

requirements.txt

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+absl-py==0.13.0
+astunparse==1.6.3
+cachetools==4.2.2
+certifi==2021.5.30
+chardet==4.0.0
+flatbuffers==1.12
+gast==0.4.0
+google-auth==1.32.0
+google-auth-oauthlib==0.4.4
+google-pasta==0.2.0
+grpcio==1.34.1
+h5py==3.1.0
+idna==2.10
+joblib==1.0.1
+Keras==2.4.3
+keras-nightly==2.5.0.dev2021032900
+Keras-Preprocessing==1.1.2
+Markdown==3.3.4
+numpy==1.19.5
+oauthlib==3.1.1
+opt-einsum==3.3.0
+pandas==1.2.5
+protobuf==3.17.3
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+python-dateutil==2.8.1
+pytz==2021.1
+PyYAML==5.4.1
+requests==2.25.1
+requests-oauthlib==1.3.0
+rsa==4.7.2
+scikit-learn==0.24.2
+scipy==1.7.0
+six==1.15.0
+tensorboard==2.5.0
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.0
+tensorflow==2.5.0
+tensorflow-estimator==2.5.0
+termcolor==1.1.0
+threadpoolctl==2.1.0
+typing-extensions==3.7.4.3
+urllib3==1.26.6
+Werkzeug==2.0.1
+wrapt==1.12.1

script.py

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+import pandas as pd
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.wrappers.scikit_learn import KerasClassifier
+from sklearn import tree
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.linear_model import SGDClassifier
+from sklearn.pipeline import make_pipeline
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score
+
+# Przewidywanie choroby serca na podstawie danych i wyników pacjenta.
+
+data = pd.read_csv("./heart_problem.csv")
+
+# Usunięcie wierszy z pustymi wartościami
+
+data.dropna(inplace=True)
+
+# Usuń wartości odstające.
+indexes = data[(data['cholesterol'] == 0) | (data['resting bp s'] == 0)].index
+data.drop(indexes , inplace=True)
+
+# Skalowanie danych
+scaler = MinMaxScaler()
+X = data[['age','sex','resting bp s','cholesterol','fasting blood sugar', 'max heart rate']].to_numpy()
+Y = data['target'].to_numpy()
+X = scaler.fit_transform(X)
+
+# Podział na zbiór trenujący/testowy
+X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.2)
+
+print(X_train.shape)
+print(X_test.shape)
+
+neural_accuracy = None
+neural_fscore = None
+neural_precision = None
+neural_recall = None
+
+sgd_accuracy = None
+sgd_fscore = None
+sgd_precision = None
+sgd_recall = None
+
+tree_accuracy = None
+tree_fscore = None
+tree_precision = None
+tree_recall = None
+
+# --------------------------------------------------------------------------
+# Klasyfikacja ma/nie ma chorobę serca na podstawie modelu sieci neuronowej.
+def classificator_model():
+	model = Sequential()
+	model.add(Dense(8, input_dim=6, activation='relu'))
+	model.add(Dense(2, activation='softmax'))
+	model.compile(loss='categorical_crossentropy', optimizer='adam')
+	return model
+
+# Trenowanie modelu
+estimator = KerasClassifier(build_fn=classificator_model, epochs=5, batch_size=10, verbose=0)
+estimator.fit(X_train, y_train)
+predicts = estimator.predict(X_test)
+neural_accuracy = accuracy_score(y_test, predicts)
+neural_fscore = f1_score(y_test, predicts)
+neural_precision = precision_score(y_test, predicts)
+neural_recall = recall_score(y_test, predicts)
+
+
+# --------------------------------------------------------------------------
+# Klasyfikacja ma/nie ma chorobę serca na podstawie klasyfikatora SGD.
+
+model = SGDClassifier(max_iter=1000)
+model.fit(X_train, y_train)
+predicts = model.predict(X_test)
+sgd_accuracy = accuracy_score(y_test, predicts)
+sgd_fscore = f1_score(y_test, predicts)
+sgd_precision = precision_score(y_test, predicts)
+sgd_recall = recall_score(y_test, predicts)
+
+# --------------------------------------------------------------------------
+# Klasyfikacja ma/nie ma chorobę serca na podstawie drzew decyzyjnych.
+
+model = tree.DecisionTreeClassifier()
+model.fit(X_train, y_train)
+predicts = model.predict(X_test)
+tree_accuracy = accuracy_score(y_test, predicts)
+tree_fscore = f1_score(y_test, predicts)
+tree_precision = precision_score(y_test, predicts)
+tree_recall = recall_score(y_test, predicts)
+
+# ---------------------------------------------------------------------------
+
+print(f'''
+Sieć neuronowa:
+_____________________________
+Accuracy: {neural_accuracy}
+Precision: {neural_precision}
+Recall: {neural_recall}
+F-score: {neural_fscore}
+
+Klasyfikator SGD:
+_____________________________
+Accuracy: {sgd_accuracy}
+Precision: {sgd_precision}
+Recall: {sgd_recall}
+F-score: {sgd_fscore}
+
+Drzewa decyzyjne:
+_____________________________
+Accuracy: {tree_accuracy}
+Precision: {tree_precision}
+Recall: {tree_recall}
+F-score: {tree_fscore}
+''')
+