157 lines
4.6 KiB
Python
157 lines
4.6 KiB
Python
import pandas as pd
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import tensorflow.keras
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from sklearn.linear_model import LinearRegression
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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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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from sklearn.compose import TransformedTargetRegressor
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from sklearn.model_selection import GridSearchCV
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from sklearn.preprocessing import (
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QuantileTransformer,
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StandardScaler,
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PolynomialFeatures,
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)
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import tensorflow.keras
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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# Import the required library
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from geopy.geocoders import Nominatim
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# Initialize Nominatim API
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geolocator = Nominatim(user_agent="MyApp")
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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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# df = df.drop(["nazwa_stacji"], axis=1)
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x = pd.get_dummies(df, columns=["id_stacji", "rok", "miesiąc"])
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# geo_lat = {
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# "BIEBRZA-PIEŃCZYKÓWEK" : 53.65
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# }
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# geo_long = {
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# "BIEBRZA-PIEŃCZYKÓWEK": 22.58
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# }
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# for xd in x["nazwa_stacji"].unique():
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# location = geolocator.geocode(xd)
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# if xd == "BIEBRZA-PIEŃCZYKÓWEK":
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# pass
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# else:
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# print(xd)
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# geo_lat[xd] = location.latitude
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# geo_long[xd] = location.longitude
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#
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#
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# x["latitude"] = x["nazwa_stacji"].map(geo_lat)
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# x["longitude"] = x["nazwa_stacji"].map(geo_long)
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x = x.drop(["nazwa_stacji", "typ_zbioru"], axis=1)
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poly = PolynomialFeatures(2, interaction_only=True)
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#
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# print(x)
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# print(geo_lat)
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# print(geo_long)
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x = x.iloc[:-600]
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x = poly.fit_transform(x)
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y = pd.read_csv("train/expected.tsv", sep="\t", names=["rainfall"])
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from sklearn.preprocessing import PolynomialFeatures
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# xxx
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# poly = PolynomialFeatures(2, interaction_only=True)
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# df = poly.fit_transform(x)
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param_grid = {
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"n_estimators": [100, 80, 60, 55, 51, 45],
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"max_depth": [7, 8],
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"reg_lambda": [0.26, 0.25, 0.2],
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}
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grid = GridSearchCV(
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xg.XGBRFRegressor(), param_grid, refit=True, verbose=3, n_jobs=-1
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) #
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regr_trans = TransformedTargetRegressor(
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regressor=grid, transformer=QuantileTransformer(output_distribution="normal")
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)
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# fitting the model for grid search
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grid_result = regr_trans.fit(x, y)
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best_params = grid_result.regressor_.best_params_
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# using best params to create and fit model
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best_model = xg.XGBRFRegressor(
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max_depth=best_params["max_depth"],
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n_estimators=best_params["n_estimators"],
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reg_lambda=best_params["reg_lambda"],
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)
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regr_trans = TransformedTargetRegressor(
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regressor=best_model, transformer=QuantileTransformer(output_distribution="normal")
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)
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regr_trans.fit(x, y)
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# model = Sequential(
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# [
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# Dense(512, activation="relu", input_dim=75),
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# tensorflow.keras.layers.BatchNormalization(),
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# Dense(512 // 2, activation="relu"),
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# tensorflow.keras.layers.BatchNormalization(),
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# Dense(512 // 4, activation="relu"),
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# tensorflow.keras.layers.BatchNormalization(),
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# Dense(512 // 8, activation="relu"),
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# tensorflow.keras.layers.BatchNormalization(),
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# Dense(32, activation="relu"),
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# tensorflow.keras.layers.BatchNormalization(),
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# Dense(1),
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# ]
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# )
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#
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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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# model.fit(x, y, epochs=100)
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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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# geo_lat = {
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# "BIEBRZA-PIEŃCZYKÓWEK" : 53.65
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# }
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# geo_long = {
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# "BIEBRZA-PIEŃCZYKÓWEK": 22.58
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# }
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x_test = pd.concat([x_test, df_train])
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# for xd in x_test["nazwa_stacji"].unique():
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# location = geolocator.geocode(xd)
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# if xd == "BIEBRZA-PIEŃCZYKÓWEK":
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# pass
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# else:
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# print(xd)
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# geo_lat[xd] = location.latitude
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# geo_long[xd] = location.longitude
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#
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#
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# x_test["latitude"] = x_test["nazwa_stacji"].map(geo_lat)
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# x_test["longitude"] = x_test["nazwa_stacji"].map(geo_long)
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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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poly = PolynomialFeatures(2, interaction_only=True)
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x_test = x_test.iloc[:-8760]
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x_test = poly.fit_transform(x_test)
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# poly = PolynomialFeatures(2, interaction_only=True)
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# x_test2 = poly.fit_transform(x_test)
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# pred = model.predict(x_test)
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test_A_predicted = regr_trans.predict(x_test)
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out = pd.DataFrame(test_A_predicted)
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out.to_csv("test-A/out.tsv", sep="\t", header=False, index=False)
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