73 lines
2.3 KiB
Python
73 lines
2.3 KiB
Python
from tensorflow.keras.models import Sequential, load_model
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from tensorflow.keras.layers import Dense
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from sklearn.metrics import accuracy_score, classification_report
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import pandas as pd
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from sklearn.model_selection import train_test_split
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import numpy as np
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import sys
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from sklearn.preprocessing import StandardScaler, LabelEncoder
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from tensorflow.keras.optimizers import Adam
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from sacred.observers import FileStorageObserver, MongoObserver
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from sacred import Experiment
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from datetime import datetime
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import os
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import sys
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import mlflow
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with mlflow.start_run():
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batch_param = int(sys.argv[1]) if len(sys.argv) > 1 else 16
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epoch_param = int(sys.argv[2]) if len(sys.argv) > 2 else 5
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mlflow.log_param("batch_size: ", batch_param)
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mlflow.log_param("epochs: ", epoch_param)
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wine=pd.read_csv('train.csv')
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y = wine['quality']
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x = wine.drop('quality', axis=1)
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citricacid = x['fixed acidity'] * x['citric acid']
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citric_acidity = pd.DataFrame(citricacid, columns=['citric_accidity'])
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density_acidity = x['fixed acidity'] * x['density']
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density_acidity = pd.DataFrame(density_acidity, columns=['density_acidity'])
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x = wine.join(citric_acidity).join(density_acidity)
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bins = (2, 5, 8)
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gnames = ['bad', 'nice']
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y = pd.cut(y, bins = bins, labels = gnames)
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enc = LabelEncoder()
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yenc = enc.fit_transform(y)
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scale = StandardScaler()
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scaled_x = scale.fit_transform(x)
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NeuralModel = Sequential([
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Dense(128, activation='relu', input_shape=(14,)),
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Dense(32, activation='relu'),
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Dense(64, activation='relu'),
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Dense(64, activation='relu'),
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Dense(64, activation='relu'),
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Dense(1, activation='sigmoid')
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])
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rms = Adam(lr=0.0003)
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NeuralModel.compile(optimizer=rms, loss='binary_crossentropy', metrics=['accuracy'])
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NeuralModel.fit(scaled_x, yenc, batch_size= batch_param, epochs = epoch_param) #verbose = 1
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NeuralModel.save('wine_model.h5')
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#TO TYLKO NA POTRZEBY ZADANIA
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y_pred = NeuralModel.predict(scaled_x)
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y_pred = np.around(y_pred, decimals=0)
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results = accuracy_score(yenc,y_pred)
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mlflow.log_metric("Accuracy: ", results) |