def TreePartialFit(): # clf_p = RandomForestRegressor() clf_p = ExtraTreesRegressor() for batch in range(1000, dataset.shape[0]): print("in partial fit") data_batch = dataset[batch:batch + 1000] f_batch = data_batch[data_batch.columns.values.tolist()[:-1]] l_batch = data_batch['Price'] clf_p.partial_fit(f_batch, l_batch) print(clf_p.feature_importances_)
m = (~gameFinishedMask) & (actingPlayerIdx == randomizedPlayerIdx) regressorOld = copy.deepcopy(regressor) #regressor = ExtraTreesRegressor(n_estimators=100, min_samples_leaf=1, min_samples_split=2, # verbose=2, n_jobs=-1, random_state=0) #regressor = ExtraTreesClassifier(n_estimators=100, min_samples_leaf=3, min_samples_split=2, # verbose=2, n_jobs=-1, class_weight='balanced') #regressor.fit(features[m], targetActions[m]) #regressor = MLPClassifier(hidden_layer_sizes=(100,100,),max_iter=100,verbose=1) #regressor.fit(features2[m], targetActions[m]) rndIdx = np.random.choice(len(features), size=len(features), replace=0) features2 = scaler.transform(features) for i in range(100): regressor.partial_fit(features2[rndIdx][m], targetActions[rndIdx][m]) print('Iteration: ' + str(i) + ' loss: ' + str(regressor.loss_)) # %% np.histogram(targetActions[m], 6) from sklearn.metrics import confusion_matrix pred = regressor.predict(features[m]) cm = confusion_matrix(targetActions[m], pred) cm22 = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis] print(np.around(cm22, 2))