import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.ensemble import BaggingClassifier as BC
from sklearn.metrics import f1_score
from sklearn.metrics import log_loss
class RFModel:
def __init__(self, input_shape):
self.input_shape = input_shape
def make_model(self, n_estimators, n_jobs, verbose=1):
model1 = RF(n_estimators=1, criterion='entropy', bootstrap=False, class_weight='balanced_subsample')
self.model = BC(base_estimator=model1, n_estimators=n_estimators, max_features=1., verbose=verbose)
def train_model(self, x, y, sample_weights=None):
self.model.fit(x, y, sample_weight=sample_weights)
def test_model(self, x, y, sample_weights=None):
# model_acc = self.model.score(x, y, sample_weight=sample_weights)
# zeros_count = y['y_values'].value_counts().loc[0]
# null_acc = zeros_count/len(y)
y_true = pd.DataFrame(index=y.index)
y_true.loc[y['y_values'] == 1, 'up'] = 1
y_true.loc[y['y_values'] == -1, 'down'] = 1
y_true.loc[y['y_values'] == 0, 'no_ch'] = 1
y_true = y_true.fillna(0)
y_pred = self.model.predict_proba(x)
model_loss = log_loss(y_true, y_pred, sample_weight=sample_weights)
base_case = pd.DataFrame(index=y.index)
base_case['up'] = np.zeros(len(y))
base_case['down'] = np.zeros(len(y))
base_case['no_ch'] = np.ones(len(y))
base_loss = log_loss(y_true, base_case)
# print(f'Model accuracy: {model_acc}')
# print(f'Null accuracy: {null_acc}')
print(f'Model log loss: {model_loss}')
print(f'Base log loss: {base_loss}')
