add zadanie 5 (Biblioteki Deep Learning)
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5/main.py
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5/main.py
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import pandas
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import tensorflow as tf
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path_to_data = '../barcelona_weekends_datasets/'
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if __name__ == '__main__':
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train_data = pandas.read_csv(path_to_data + 'train_set.csv')
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dev_data = pandas.read_csv(path_to_data + 'dev_set.csv')
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test_data = pandas.read_csv(path_to_data + 'test_set.csv')
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x_train, y_train = train_data.iloc[:, :-1], train_data['guest_satisfaction_overall']
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x_dev, y_dev = dev_data.iloc[:, :-1], dev_data['guest_satisfaction_overall']
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x_test, y_test = dev_data.iloc[:, :-1], dev_data['guest_satisfaction_overall']
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model = tf.keras.models.Sequential([
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tf.keras.layers.Dense(64, activation='relu', input_shape=(18,)),
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tf.keras.layers.Dense(64, activation='relu'),
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tf.keras.layers.Dense(1, activation='sigmoid')
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])
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model.compile(loss='mean_squared_error', optimizer='adam', metrics=['mae'])
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model.fit(x_train, y_train, epochs=10, batch_size=32, validation_data=(x_dev, y_dev))
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loss, mae = model.evaluate(x_test, y_test)
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print("Test Loss:", loss)
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print("Test MAE:", mae)
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model.save('model_file.h5')
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5/model_file.h5
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5/model_file.h5
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5/predict.py
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5/predict.py
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import tensorflow as tf
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import pandas as pd
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import numpy as np
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if __name__ == '__main__':
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test_data = pd.read_csv('../barcelona_weekends_datasets/test_set.csv')
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x_test, y_test = test_data.iloc[:, :-1], test_data['guest_satisfaction_overall']
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model = tf.keras.models.load_model('model_file.h5')
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predictions = model.predict(x_test)
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predictions = np.reshape(predictions, (-1,))
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df_predictions = pd.DataFrame({'Predictions': predictions})
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df_predictions.to_csv('predictions.csv', index=False)
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5/predictions.csv
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5/predictions.csv
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Predictions
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barcelona_weekends_datasets/dev_set.csv
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barcelona_weekends_datasets/dev_set.csv
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realSum,room_type,room_shared,room_private,person_capacity,host_is_superhost,multi,biz,cleanliness_rating,bedrooms,dist,metro_dist,attr_index,attr_index_norm,rest_index,rest_index_norm,lng,lat,guest_satisfaction_overall
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|
257
barcelona_weekends_datasets/test_set.csv
Normal file
257
barcelona_weekends_datasets/test_set.csv
Normal file
@ -0,0 +1,257 @@
|
||||
realSum,room_type,room_shared,room_private,person_capacity,host_is_superhost,multi,biz,cleanliness_rating,bedrooms,dist,metro_dist,attr_index,attr_index_norm,rest_index,rest_index_norm,lng,lat,guest_satisfaction_overall
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|
767
barcelona_weekends_datasets/train_set.csv
Normal file
767
barcelona_weekends_datasets/train_set.csv
Normal file
@ -0,0 +1,767 @@
|
||||
realSum,room_type,room_shared,room_private,person_capacity,host_is_superhost,multi,biz,cleanliness_rating,bedrooms,dist,metro_dist,attr_index,attr_index_norm,rest_index,rest_index_norm,lng,lat,guest_satisfaction_overall
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||||
0.019386819678810306,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.16666666666666666,0.2566414658151942,0.7424061889409752,0.10964269850130948,0.10964269850130945,0.14889314212020094,0.14889314212020094,0.697723618515699,0.1517034068135672,0.9750000000000001
|
||||
0.01806267612806845,1.0,0.0,1.0,0.0,1.0,0.0,0.0,1.0,0.16666666666666666,0.3965683656054601,0.9040969263759615,0.05949219567348844,0.05949219567348846,0.07167479453984071,0.07167479453984069,0.8199204535838227,0.41422845691380417,0.9750000000000001
|
||||
0.06844803585373306,1.0,0.0,1.0,0.25,0.0,1.0,0.0,0.875,0.16666666666666666,0.08631578635131822,0.12401285085957714,0.2132451138262159,0.21324511382621594,0.20515597146679837,0.2051559714667984,0.596259625962599,0.31342685370736945,0.925
|
||||
0.007096051336026888,1.0,0.0,1.0,0.0,0.0,0.0,0.0,1.0,0.16666666666666666,0.3582590183752611,0.5407036269477137,0.10490016153050077,0.10490016153050077,0.06729649088101833,0.0672964908810183,0.6424642464246446,0.5209418837675344,1.0
|
||||
0.013852578684684072,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.16666666666666666,0.17976379568953993,0.08241179490744416,0.1411512313299803,0.14115123132998028,0.16754698761399178,0.1675469876139918,0.5141744943725151,0.13707414829650588,1.0
|
||||
0.015346484229110792,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.875,0.16666666666666666,0.11947105620106847,0.21549093394598604,0.14774120709080313,0.14774120709080313,0.20277719608978267,0.20277719608978262,0.4261656934924254,0.25631262525047305,0.875
|
||||
0.0052286694054934964,1.0,0.0,1.0,0.0,0.0,0.0,0.0,0.875,0.0,0.5261183658842195,0.49077823426605466,0.07109801462716508,0.0710980146271651,0.03592008311941208,0.03592008311941209,0.36938309215537046,0.6335671342685032,0.8375000000000001
|
||||
0.016704580178589612,1.0,0.0,1.0,0.25,0.0,1.0,0.0,1.0,0.16666666666666666,0.07830141912885975,0.12381223589269233,0.36796301769087925,0.36796301769087925,0.3010302830125779,0.3010302830125779,0.5377845476855398,0.20961923847693242,0.9375
|
||||
0.0253284894577802,1.0,0.0,1.0,0.0,1.0,0.0,1.0,1.0,0.16666666666666666,0.0674012353967548,0.2252431472587786,0.27227778111099266,0.27227778111099254,0.2913342599409289,0.29133425994092893,0.5200981636625244,0.21783567134264104,0.9500000000000002
|
||||
0.06478117679014021,0.0,0.0,0.0,1.0,0.0,0.0,1.0,0.875,0.5,0.2619957621870341,0.2614071387605596,0.09015634079112722,0.09015634079112719,0.11411482992892917,0.11411482992892916,0.3094694084793126,0.2811623246492445,0.875
|
||||
0.00271619189895766,1.0,0.0,1.0,0.0,0.0,1.0,0.0,0.875,0.16666666666666666,0.5044759079008622,0.19983672760131893,0.05404027310138769,0.05404027310138769,0.040109724852975245,0.04010972485297524,0.14089870525514314,0.14258517034068063,0.9125000000000001
|
|
18
main.py
18
main.py
@ -53,7 +53,11 @@ def normalize_data(data):
|
||||
data[numeric_columns] = numeric_data_norm
|
||||
data[non_numeric_columns] = non_numeric_data_norm
|
||||
|
||||
return data
|
||||
y_col = 'guest_satisfaction_overall'
|
||||
new_order = list(data.columns.drop(y_col)) + [y_col]
|
||||
new_df = data.reindex(columns=new_order)
|
||||
|
||||
return new_df
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
@ -86,5 +90,13 @@ if __name__ == '__main__':
|
||||
print('Test', calculate_value_counts(test_set, 'person_capacity'), end='\n\n')
|
||||
|
||||
# 1.4 & 1.5
|
||||
normalized_data = normalize_data(whole_set)
|
||||
print(normalized_data, '\n\n')
|
||||
whole_set_normalized = normalize_data(whole_set)
|
||||
train_set_normalized = normalize_data(train_set)
|
||||
dev_set_normalized = normalize_data(dev_set)
|
||||
test_set_normalized = normalize_data(test_set)
|
||||
print(whole_set_normalized, '\n\n')
|
||||
|
||||
# Save all sets into files
|
||||
train_set_normalized.to_csv('./barcelona_weekends_datasets/train_set.csv', index=False)
|
||||
dev_set_normalized.to_csv('./barcelona_weekends_datasets/dev_set.csv', index=False)
|
||||
test_set_normalized.to_csv('./barcelona_weekends_datasets/test_set.csv', index=False)
|
||||
|
Loading…
Reference in New Issue
Block a user