keras
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patrycjalazna
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6
.idea/vcs.xml
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6
.idea/vcs.xml
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<?xml version="1.0" encoding="UTF-8"?>
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<project version="4">
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<component name="VcsDirectoryMappings">
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<mapping directory="$PROJECT_DIR$" vcs="Git" />
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</component>
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</project>
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24
Dockerfile
24
Dockerfile
@ -4,12 +4,28 @@ RUN apt update
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RUN apt install -y python3 python3-pip
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RUN pip3 install kaggle
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RUN apt install -y unzip
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RUN pip3 install pandas
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RUN pip3 install tensorflow
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RUN pip3 install scikit-learn
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RUN pip3 install pandas
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# RUN apt install -y unzip
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RUN mkdir /.kaggle
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RUN chmod -R 777 /.kaggle
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WORKDIR /app
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COPY ./avocado-preprocessing.sh ./
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RUN chmod +x avocado-preprocessing.sh
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CMD ./avocado-preprocessing.sh
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COPY ./requirements.txt ./
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COPY ./avocado-preprocessing.py ./
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RUN chmod +x ./requirements.txt
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RUN chmod +x ./avocado-preprocessing.py
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RUN pip3 install -r ./requirements.txt
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CMD python3 avocado-preprocessing.py
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# COPY ./avocado-preprocessing.sh ./
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# RUN chmod +x avocado-preprocessing.sh
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# CMD ./avocado-preprocessing.sh
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@ -2,42 +2,34 @@ import kaggle
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import pandas as pd
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import numpy as np
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from sklearn import preprocessing
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from sklearn.linear_model import LinearRegression
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import tensorflow as tf
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from tensorflow.keras.layers import Input, Dense, Activation,Dropout
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from tensorflow.keras.models import Model
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from tensorflow.keras.callbacks import EarlyStopping
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from keras.models import Sequential
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device = 'cpu'
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# kaggle
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kaggle.api.authenticate()
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kaggle.api.dataset_download_files('timmate/avocado-prices-2020', path='.', unzip=True)
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# wczytanie danych
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avocado_with_year = pd.read_csv('avocado-updated-2020.csv')
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# usuniecie redundantnej kolumny 'year' i zamiana wartosci 'type' na 0 lub 1
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new = ['date', 'average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags', 'type', 'geography']
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avocado = avocado_with_year[new]
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avocado.to_csv("avocado.csv", index=False)
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avocado = pd.read_csv('avocado.csv')
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avocado['type'] = avocado.type.map(dict(organic=1, conventional=0))
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avocado_train, avocado_validate, avocado_test = np.split(avocado.sample(frac=1), [int(.6*len(avocado)), int(.8*len(avocado))])
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print("Avocado: ".ljust(20), np.size(avocado))
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print("Avocado (train) : ".ljust(20), np.size(avocado_train))
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print("Avocado (validate): ".ljust(20), np.size(avocado_validate))
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print("Avocado (test) ".ljust(20), np.size(avocado_test))
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avocado.describe(include = 'all')
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avocado_train.describe(include= 'all')
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avocado_validate.describe(include = 'all')
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avocado_test.describe(include = 'all')
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avocado.geography.value_counts()
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avocado_test.geography.value_counts()
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avocado_train.geography.value_counts()
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pd.value_counts(avocado['type']).plot.bar()
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pd.value_counts(avocado_train['type']).plot.bar()
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pd.value_counts(avocado_test['type']).plot.bar()
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avocado['average_price'].hist()
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avocado_train['average_price'].hist()
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avocado_validate['average_price'].hist()
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avocado_test['average_price'].hist()
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# usuniecie wierszy z pustymi wartosciami
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avocado.isnull().sum()
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avocado.dropna()
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# preprocessing
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num_values = avocado.select_dtypes(include='float64').values
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scaler = preprocessing.MinMaxScaler()
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x_scaled = scaler.fit_transform(num_values)
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@ -47,5 +39,66 @@ for col in avocado.columns:
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if col in num_columns:
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avocado[col] = avocado_normalized[col]
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avocado.isnull().sum()
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avocado.dropna()
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avocado_normalized['type'] = avocado['type']
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avocado_normalized['geography'] = avocado['geography']
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# podział na train/dev/test
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avocado_train, avocado_validate, avocado_test = np.split(avocado_normalized.sample(frac=1), [int(.6*len(avocado_normalized)), int(.8*len(avocado_normalized))])
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print("Avocado: ".ljust(20), np.size(avocado_normalized))
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print("Avocado (train) : ".ljust(20), np.size(avocado_train))
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print("Avocado (validate): ".ljust(20), np.size(avocado_validate))
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print("Avocado (test) ".ljust(20), np.size(avocado_test))
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# sprawdzenie danych
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avocado_normalized.describe(include = 'all')
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avocado_train.describe(include= 'all')
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avocado_validate.describe(include = 'all')
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avocado_test.describe(include = 'all')
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avocado_normalized.geography.value_counts()
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avocado_test.geography.value_counts()
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avocado_train.geography.value_counts()
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pd.value_counts(avocado_normalized['type']).plot.bar()
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pd.value_counts(avocado_train['type']).plot.bar()
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pd.value_counts(avocado_test['type']).plot.bar()
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avocado_normalized['average_price'].hist()
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avocado_train['average_price'].hist()
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avocado_validate['average_price'].hist()
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avocado_test['average_price'].hist()
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# print(avocado_train[:10])
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# print(avocado_test[:10])
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print(avocado_normalized)
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# podzial na X i y
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X_train = avocado_train[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
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y_train = avocado_train[['type']]
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X_test = avocado_test[['average_price', 'total_volume', '4046', '4225', '4770', 'total_bags', 'small_bags', 'large_bags', 'xlarge_bags']]
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y_test = avocado_test[['type']]
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print(X_train.shape[1])
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# keras model
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model = Sequential()
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model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
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model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
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early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
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# kompilacja
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model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
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# model fit
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model.fit(X_train, y_train, epochs=30, batch_size=10, validation_data=(X_test, y_test))
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# predict
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predictions = model.predict(X_test)
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pd.DataFrame(predictions).to_csv('prediction_results.csv')
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# ewaluacja
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# _, accuracy = model.evaluate(y_test, predictions)
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# print('Accuracy: %.2f' % (accuracy*100))
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6610
prediction_results.csv
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6610
prediction_results.csv
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File diff suppressed because it is too large
Load Diff
58
requirements.txt
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58
requirements.txt
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appnope==0.1.0
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attrs==19.1.0
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backcall==0.1.0
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bleach==3.1.0
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click==7.1.2
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csv-diff==1.0
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cycler==0.10.0
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decorator==4.4.0
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defusedxml==0.6.0
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dictdiffer==0.8.1
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entrypoints==0.3
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ipykernel==5.1.1
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ipython==7.5.0
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ipython-genutils==0.2.0
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ipywidgets==7.4.2
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jedi==0.13.3
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jsonschema==3.0.1
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jupyter==1.0.0
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jupyter-client==5.2.4
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jupyter-console==6.0.0
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jupyter-core==4.4.0
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kiwisolver==1.1.0
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matplotlib==3.1.0
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mistune==0.8.4
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mpmath==1.1.0
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nbconvert==5.5.0
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nbformat==4.4.0
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nose==1.3.7
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notebook==5.7.8
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numpy==1.16.4
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pandas==0.24.2
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pandocfilters==1.4.2
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parso==0.4.0
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pexpect==4.7.0
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pickleshare==0.7.5
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prometheus-client==0.7.0
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prompt-toolkit==2.0.9
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ptyprocess==0.6.0
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pygame==1.9.4
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Pygments==2.4.2
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pyparsing==2.4.0
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pyrsistent==0.15.2
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python-dateutil==2.8.0
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pytz==2019.1
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pyzmq==18.0.1
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qtconsole==4.5.1
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scipy==1.3.0
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Send2Trash==1.5.0
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six==1.12.0
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sympy==1.4
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terminado==0.8.2
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testpath==0.4.2
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tornado==6.0.2
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traitlets==4.3.2
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wcwidth==0.1.7
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webencodings==0.5.1
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widgetsnbextension==3.4.2
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xlrd==1.2.0
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