75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
import pandas as pd
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import tensorflow.keras
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import numpy as np
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import pandas as pd
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import xgboost as xg
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import tensorflow.keras
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from keras.layers import Dropout
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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from tensorflow.python.keras.wrappers.scikit_learn import KerasRegressor
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in_columns = ["id_stacji", "nazwa_stacji", "typ_zbioru", "rok", "miesiąc"]
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df = pd.read_csv("train/in.tsv", names=in_columns, sep="\t")
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df_test = pd.read_csv("dev-0/in.tsv", names=in_columns, sep="\t")
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df = pd.concat([df, df_test])
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x = pd.get_dummies(df, columns=["id_stacji", "rok", "miesiąc"])
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x = x.drop(["nazwa_stacji", "typ_zbioru"], axis=1)
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x = x.iloc[:-600]
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y = pd.read_csv("train/expected.tsv", sep="\t", names=["rainfall"])
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model = Sequential(
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[
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Dense(2048, activation="relu", input_dim=73),
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Dense(1024, activation="relu"),
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Dense(224, activation="relu"),
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# tensorflow.keras.layers.BatchNormalization(),
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Dense(320, activation="relu"),
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# tensorflow.keras.layers.BatchNormalization(),
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Dense(384, activation="relu"),
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# tensorflow.keras.layers.BatchNormalization(),
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Dense(416, activation="relu"),
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# tensorflow.keras.layers.BatchNormalization(),
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Dense(448, activation="relu"),
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Dense(448, activation="relu"),
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Dense(256, activation="relu"),
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# tensorflow.keras.layers.BatchNormalization(),
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Dense(1, activation="linear"),
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]
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)
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# input = tensorflow.keras.layers.Input(shape=x.shape[1:])
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# hidden1 = tensorflow.keras.layers.Dense(1024, activation='relu')(input)
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# hidden2 = tensorflow.keras.layers.Dense(512, activation='relu')(hidden1)
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# hidden3 = tensorflow.keras.layers.Dense(256, activation='relu')(hidden2)
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# hidden4 = tensorflow.keras.layers.Dense(128, activation='relu')(hidden3)
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# concat = tensorflow.keras.layers.Concatenate()([input, hidden4])
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# output = tensorflow.keras.layers.Dense(1, activation="linear")(concat)
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# model = tensorflow.keras.models.Model(inputs=[input], outputs=[output])
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model.compile(
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loss="mean_squared_error", optimizer="adam", metrics=["mean_squared_error"]
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)
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# estimator = KerasRegressor(build_fn=model, epochs=100, batch_size=10, verbose=0)
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# estimator.fit(x, y)
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model.fit(x, y, epochs=100)
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# exit()
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x_test = pd.read_csv("test-A/in.tsv", sep="\t", names=in_columns)
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df_train = pd.read_csv("train/in.tsv", names=in_columns, sep="\t")
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x_test = pd.concat([x_test, df_train])
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x_test = x_test.drop(["nazwa_stacji", "typ_zbioru"], axis=1)
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x_test = pd.get_dummies(x_test, columns=["id_stacji", "rok", "miesiąc"])
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x_test = x_test.iloc[:-8760]
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pred = model.predict(x_test)
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# pred = estimator.predict(x_test)
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out = pd.DataFrame(pred)
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out.to_csv("test-A/out2.tsv", sep="\t", header=False, index=False)
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