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run4.py
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run4.py
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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 keras_tuner as kt
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import tensorflow.keras
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from sklearn.preprocessing import PolynomialFeatures
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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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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from keras_tuner import HyperModel
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class ANNHyperModel(HyperModel):
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def build(self, hp):
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model = tensorflow.keras.Sequential()
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# Tune the number of units in the first Dense layer
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# Choose an optimal value between 32-512
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hp_units1 = hp.Int('units1', min_value=32, max_value=512, step=32)
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hp_units2 = hp.Int('units2', min_value=32, max_value=512, step=32)
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hp_units3 = hp.Int('units3', min_value=32, max_value=512, step=32)
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hp_units4 = hp.Int('units4', min_value=32, max_value=512, step=32)
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hp_units5 = hp.Int('units5', min_value=32, max_value=512, step=32)
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model.add(Dense(units=hp_units1, activation='relu'))
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model.add(tensorflow.keras.layers.Dense(units=hp_units2, activation='relu'))
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model.add(tensorflow.keras.layers.Dense(units=hp_units3, activation='relu'))
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model.add(tensorflow.keras.layers.Dense(units=hp_units4, activation='relu'))
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model.add(tensorflow.keras.layers.Dense(units=hp_units5, activation='relu'))
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model.add(Dense(1, kernel_initializer='normal', activation='linear'))
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# Tune the learning rate for the optimizer
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# Choose an optimal value from 0.01, 0.001, or 0.0001
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hp_learning_rate = hp.Choice('learning_rate',
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values=[1e-2, 1e-3, 1e-4])
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model.compile(
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optimizer=tensorflow.keras.optimizers.Adam(learning_rate=hp_learning_rate),
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loss="mean_squared_error",
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metrics=["mean_squared_error"]
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)
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return model
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hypermodel = ANNHyperModel()
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tuner = kt.Hyperband(
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hypermodel,
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objective='mean_squared_error',
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max_epochs=100,
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factor=3,
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directory='keras_tuner_dir',
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project_name='keras_tuner_demo2'
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)
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#po#ly = PolynomialFeatures(2, interaction_only=True)
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#x = poly.fit_transform(x)
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tuner.search(x, y, epochs=100)
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for h_param in [f"units{i}" for i in range(1,4)] + ['learning_rate']:
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print(h_param, tuner.get_best_hyperparameters()[0].get(h_param))
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best_model = tuner.get_best_models()[0]
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best_model.build(x.shape)
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best_model.summary()
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best_model.fit(
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x,
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y,
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epochs=100,
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batch_size=64
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)
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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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#poly = PolynomialFeatures(2, interaction_only=True)
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#x_test= poly.fit_transform(x_test)
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pred = best_model.predict(x_test)
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out = pd.DataFrame(pred)
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out.to_csv("test-A/out.tsv", sep="\t", header=False, index=False)
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test-A/out.tsv
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test-A/out.tsv
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