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train_and_predict.py
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train_and_predict.py
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import numpy as np
import pandas as pd
from sklearn import metrics
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier, VotingClassifier, GradientBoostingClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.preprocessing import OneHotEncoder
import utils
n_test= 49999
fichero = 'datos.csv'
tests = 'entrega_para_predecir.csv'
resultados_finales = 'resultados_finales/test.csv'
sample = 'resultados_finales/sampleSubmission.csv'
path_dir = 'pokemon-challenge-mlh/'
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------DATASET-----------------------------------------------------------
def get_data():
#-------pokemon.csv
df_pokemon = pd.read_csv(path_dir + 'pokemon.csv')
df_pokemon = df_pokemon.fillna({'Name': 'None', 'Type 1': 'None', 'Type 2': 'None'})
#df_pokemon = df_pokemon.dropna()
#cambiando nombre de variable
df_pokemon = df_pokemon.rename(index=str, columns={"#": "id_pokemon"})
# encoding
df_pokemon['Legendary'] = np.where(df_pokemon['Legendary'] == True, 1, 0)
# encoding name, type1 y type2
valores_type1 = df_pokemon['Type 1'].values
valores_type2 = df_pokemon['Type 2'].values
valores_name = df_pokemon['Name'].values
#print(df_pokemon.isna().sum())
le1 = preprocessing.LabelEncoder()
le2 = preprocessing.LabelEncoder()
lename = preprocessing.LabelEncoder()
encoding1 = le1.fit_transform(valores_type1)
encoding2 = le2.fit_transform(valores_type2)
encodingName = lename.fit_transform(valores_name)
# asignando
df_pokemon['Type 1'] = encoding1
df_pokemon['Type 2'] = encoding2
df_pokemon['Name'] = encodingName
# rapido -> 1, Lento -> 0
sum_speeds = np.sum(df_pokemon['Speed'].values)
total_speeds = len(df_pokemon['Speed'])
media_speeds = sum_speeds / total_speeds
df_pokemon['Rapidez'] = np.where(df_pokemon['Speed'] > media_speeds, 1, 0)
#-------battles.csv
df_battles = pd.read_csv(path_dir + 'battles.csv')
# quitamos el numero de batalla
df_battles = df_battles[['First_pokemon','Second_pokemon', 'Winner']]
print(df_battles.columns)
#winrates
#df_pokemon = utils.get_winrate(df_pokemon, df_battles)
print(df_pokemon.head())
#-------test.csv
df_test = pd.read_csv(path_dir + 'test.csv')
return df_pokemon, df_battles, df_test, le1, le2, lename
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
def juntar_csvs():
df_pokemon, df_battles, df_test, le1, le2, lename = get_data()
#vectorizacion
pokemon_values = df_pokemon.values #(800, cols)
battles_values = df_battles.values #(50000, 3)
ids_pokemon = pokemon_values[:,0]
# obtenemos valores unicos y los indices inversos para luego reconstruir el array original
ids_pok1, inv1 = np.unique(battles_values[:, 0], return_inverse=True)
ids_pok2, inv2 = np.unique(battles_values[:, 1], return_inverse=True)
resultados_batallas = battles_values[:, 2]
# buscamos donde estan las caracteristicas de cada pokemon en las batallas
indices1 = np.intersect1d(ids_pok1, ids_pokemon, return_indices=True)
indices2 = np.intersect1d(ids_pok2, ids_pokemon, return_indices=True)
# asignamos las caracteristicas
vals_pok1 = pokemon_values[indices1[2], 1:]
vals_pok2 = pokemon_values[indices2[2], 1:]
# pokemons sin batallas
sin_battles = pokemon_values[
np.where(
np.logical_not(
np.isin(ids_pokemon, ids_pok1)))]
# 16 en total
print('Pokemons que no han peleado:', len(sin_battles))
# y reconstruimos el array original
lon_values = len(battles_values)
# (50000, 11) cada uno
pok1 = vals_pok1[inv1]
pok2 = vals_pok2[inv2]
#columnas = pok2.shape[1] * 2
columnas = pok2.shape[1] + 3 #nombre2,tipo1_id2,tipo2_id2, el mas rapido
print(pok2.shape)
# aplicamos diff
pok_final = np.ones((lon_values, columnas))
pok_final[:, :3] = pok1[:, :3]#nombre1,tipo1_id1,tipo2_id1
pok_final[:, 3:6] = pok2[:, :3]#nombre2,tipo2_id2,tipo2_id2
pok_final[:, 6:] = pok1[:, 3:] - pok2[:, 3:]
# el mas rapido
#pok_final[:, -1] = np.where(pok1[:, -4] > pok2[:, -4], battles_values[:, 0], battles_values[:, 1])
# aqui juntamos el resto para crear el dataset con el que entrenar
#juntar_carac = np.concatenate((pok1, pok2), axis=1)
juntar_carac = pok_final
caracteristicas_y_resultados = np.ones((lon_values, columnas + 1)) # (50000, 15)
caracteristicas_y_resultados[:,:-1] = juntar_carac
caracteristicas_y_resultados[:,-1] = resultados_batallas
# ids contrincante 1, ids contrincante 2 y el que golpea primero (añadido)
valores = np.array((battles_values[:, 0], battles_values[:, 1], battles_values[:, 0])) #(3, 50000)
valores = valores.T #(50000, 3)
lista = np.concatenate((valores, caracteristicas_y_resultados), axis=1)
lista = lista.astype(int)
# guardo el fichero
df_lista = pd.DataFrame(lista, columns=['First_pokemon', 'Second_pokemon', 'id_primer_ataq',
'nombre1', 'tipo1_id1', 'tipo2_id1',
'nombre2', 'tipo1_id2', 'tipo2_id2',
'diff_HP','diff_Attack','diff_Defense','diff_Sp. Atk','diff_Sp. Def','diff_Speed',
'diff_Generation', 'diff_Legendary',
'diff_Rapidez',
'Winner'])
# efectividad de las habilidades
# primero pasamos a las antiguas labels
df_lista['tipo1_id1'] = le1.inverse_transform(df_lista['tipo1_id1'])
df_lista['tipo2_id1'] = le2.inverse_transform(df_lista['tipo2_id1'])
df_lista['tipo1_id2'] = le1.inverse_transform(df_lista['tipo1_id2'])
df_lista['tipo2_id2'] = le2.inverse_transform(df_lista['tipo2_id2'])
df_lista['nombre1'] = lename.inverse_transform(df_lista['nombre1'])
df_lista['nombre2'] = lename.inverse_transform(df_lista['nombre2'])
# y luego aplicamos los valores
df_lista = utils.calculate_effectiveness(df_lista)
# reordenamos para colocar la columnas Winner al final
winners = df_lista['Winner'].values
df_lista = df_lista.drop(['Winner'], axis=1)
df_lista['Winner'] = winners
#y volvemos a aplicar los encodings
df_lista['tipo1_id1'] = le1.fit_transform(df_lista['tipo1_id1'])
df_lista['tipo2_id1'] = le2.fit_transform(df_lista['tipo2_id1'])
df_lista['tipo1_id2'] = le1.fit_transform(df_lista['tipo1_id2'])
df_lista['tipo2_id2'] = le2.fit_transform(df_lista['tipo2_id2'])
df_lista['nombre1'] = lename.fit_transform(df_lista['nombre1'])
df_lista['nombre2'] = lename.fit_transform(df_lista['nombre2'])
# elimino carac que aportan menos --> no aporta
#df_lista = df_lista.drop(['diff_Generation', 'diff_Legendary'], axis=1)
df_lista.to_csv(fichero, index=False)
#np.savetxt(fichero, lista)
return lista
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
def preparar_test():
df_pokemon, df_battles, df_test, le1, le2, lename = get_data()
# vectorizacion
pokemon_values = df_pokemon.values # (800, 12)
tests_values = df_test.values # (10000, 3)
ids_pokemon = pokemon_values[:, 0]
# obtenemos valores unicos y los indices inversos para luego reconstruir el array original
ids_pok1, inv1 = np.unique(tests_values[:, 1], return_inverse=True)
ids_pok2, inv2 = np.unique(tests_values[:, 2], return_inverse=True)
# buscamos donde estan las caracteristicas de cada pokemon en las batallas
indices1 = np.intersect1d(ids_pok1, ids_pokemon, return_indices=True)
indices2 = np.intersect1d(ids_pok2, ids_pokemon, return_indices=True)
# asignamos las caracteristicas
vals_pok1 = pokemon_values[indices1[2], 1:]
vals_pok2 = pokemon_values[indices2[2], 1:]
# pokemons sin batallas
sin_battles = pokemon_values[
np.where(
np.logical_not(
np.isin(ids_pokemon, ids_pok1)))]
# 16 en total
print('Pokemons que no han peleado en test:', len(sin_battles))
# y reconstruimos el array original
lon_values = len(tests_values)
# (10000, 11) cada uno
pok1 = vals_pok1[inv1]
pok2 = vals_pok2[inv2]
columnas = pok2.shape[1] + 3 # nombre2,tipo1_id2,tipo2_id2, Mas_Winrate
# aplicamos diff
pok_final = np.ones((lon_values, columnas))
pok_final[:, :3] = pok1[:, :3]
pok_final[:, 3:6] = pok2[:, :3]
pok_final[:, 6:] = pok1[:, 3:] - pok2[:, 3:]
# winrate
#pok_final[:, -2] = np.where(pok1[:, -1] > pok2[:, -1], tests_values[:, 0], tests_values[:, 1])
# el mas rapido
#pok_final[:, -1] = np.where(pok1[:, -2] > pok2[:, -2], tests_values[:, 0], tests_values[:, 1])
# aqui juntamos el resto para crear el dataset con el que entrenar
# juntar_carac = np.concatenate((pok1, pok2), axis=1)
juntar_carac = pok_final
# ids contrincante 1, ids contrincante 2 y el que golpea primero (añadido)
valores = np.array((tests_values[:, 1], tests_values[:, 2], tests_values[:, 1])) # (3, 10000)
valores = valores.T # (10000, 3)
lista = np.concatenate((valores, juntar_carac), axis=1)
lista = lista.astype(int)
print(lista.shape)
# guardo el fichero
df_lista = pd.DataFrame(lista, columns=['First_pokemon', 'Second_pokemon', 'id_primer_ataq',
'nombre1', 'tipo1_id1', 'tipo2_id1',
'nombre2', 'tipo1_id2', 'tipo2_id2',
'HP','Attack','Defense','Sp. Atk','Sp. Def','Speed',
'Generation', 'Legendary',
'Rapidez'
])
# efectividad de las habilidades
# primero pasamos a las antiguas labels
df_lista['tipo1_id1'] = le1.inverse_transform(df_lista['tipo1_id1'])
df_lista['tipo2_id1'] = le2.inverse_transform(df_lista['tipo2_id1'])
df_lista['tipo1_id2'] = le1.inverse_transform(df_lista['tipo1_id2'])
df_lista['tipo2_id2'] = le2.inverse_transform(df_lista['tipo2_id2'])
df_lista['nombre1'] = lename.inverse_transform(df_lista['nombre1'])
df_lista['nombre2'] = lename.inverse_transform(df_lista['nombre2'])
# y luego aplicamos los valores
df_lista = utils.calculate_effectiveness(df_lista)
# y volvemos a aplicar los encodings
df_lista['tipo1_id1'] = le1.fit_transform(df_lista['tipo1_id1'])
df_lista['tipo2_id1'] = le2.fit_transform(df_lista['tipo2_id1'])
df_lista['tipo1_id2'] = le1.fit_transform(df_lista['tipo1_id2'])
df_lista['tipo2_id2'] = le2.fit_transform(df_lista['tipo2_id2'])
df_lista['nombre1'] = lename.fit_transform(df_lista['nombre1'])
df_lista['nombre2'] = lename.fit_transform(df_lista['nombre2'])
# elimino carac que aportan menos --> no aporta
#df_lista = df_lista.drop(['Generation', 'Legendary'], axis=1)
df_lista.to_csv(tests, index=False)
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------MODELO EMPLEADOS--------------------------------------------------
def random_forest(train_x, train_y, test_x, test_y):
clf = GradientBoostingClassifier(n_estimators=100, learning_rate=0.08, subsample=0.75,max_depth=7)
clf.fit(train_x, train_y)
y_pred=clf.predict(test_x)
print(clf.feature_importances_)
print("Accuracy random forest:",metrics.accuracy_score(test_y, y_pred))
return clf
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
def Voting(train_x, train_y, test_x, test_y):
clf1 = RandomForestClassifier(n_estimators=150)
clf2 = RandomForestClassifier(n_estimators=200)
clf3 = RandomForestClassifier(n_estimators=175)
'''clf3 = MLPClassifier(solver='lbfgs', alpha=1e-5
, hidden_layer_sizes=(10, 10), random_state=1, activation='logistic')'''
eclf = VotingClassifier(estimators=[('rf1', clf1), ('rf2', clf2),('rf3', clf3)], voting='hard')
clf1 = clf1.fit(train_x, train_y)
clf2 = clf2.fit(train_x, train_y)
clf3 = clf3.fit(train_x, train_y)
eclf = eclf.fit(train_x, train_y)
y_pred1 = clf1.predict(test_x)
y_pred2 = clf2.predict(test_x)
y_pred3 = clf3.predict(test_x)
e_pred = eclf.predict(test_x)
print("Accuracy RandomForestClassifier 150:", metrics.accuracy_score(test_y, y_pred1))
print("Accuracy RandomForestClassifier 200:", metrics.accuracy_score(test_y, y_pred2))
print("Accuracy RandomForestClassifier 175:", metrics.accuracy_score(test_y, y_pred3))
print("Accuracy VotingClassifier:", metrics.accuracy_score(test_y, e_pred))
return eclf
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------------------
# ----------------------------------------------------RESULTADOS--------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------------------
def agrupados():
lista = pd.read_csv(fichero).values
print(lista.shape)
X = lista[:, :-1]
y = lista[:, -1]
train_x,train_y = X[:n_test], y[:n_test]
test_x, test_y = X[n_test:], y[n_test:]
rf = random_forest(train_x, train_y, test_x, test_y)
#mlp = MLP(train_x, train_y, test_x, test_y)
#svm = SVM(train_x, train_y, test_x, test_y)
return rf
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# ------------------------------------------guardar datos finales-------------------------------------------------------
def resultado_final():
lista = pd.read_csv(tests).values
print(lista.shape)
clf = agrupados()
y_pred = clf.predict(lista)
y_pred = y_pred.astype(int)
df_test = pd.read_csv(path_dir + 'test.csv')
df_test['Winner'] = y_pred
df_test.to_csv(resultados_finales, index=False)
df_sample = df_test[['battle_number', 'Winner']]
df_sample.to_csv(sample, index=False)
# ----------------------------------------------------------------------------------------------------------------------
# ----------------------------------------------------------------------------------------------------------------------
# solo_battles()
juntar_csvs()
#preparar_test()
agrupados()
#resultado_final()