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random-forest-train-and-test-pca.py
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import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import pickle
import time
plt.close("all")
date_columns = ['dateCreated', 'lastSeen']
# A date looks like => '2016-04-07 03:16:57'
dateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')
raw = pd.read_csv('autos.csv', parse_dates=date_columns, date_parser=dateparse, encoding='cp1252')
heads = list(raw.columns)
cleanData = raw.copy()
cleanData.drop(['nrOfPictures'], axis = 1, inplace = True)
cleanData.drop(['abtest'], axis = 1, inplace = True)
cleanData = cleanData[cleanData.seller != 'gewerblich']
cleanData.drop(['seller'], axis = 1, inplace = True)
cleanData.drop(['offerType'], axis = 1, inplace = True)
cleanData.drop(['dateCrawled'], axis = 1, inplace = True)
cleanData.drop(['dateCreated'], axis = 1, inplace = True)
cleanData.drop(['lastSeen'], axis = 1, inplace = True)
cleanData.drop(['postalCode'], axis = 1, inplace = True)
cleanData.drop(['monthOfRegistration'], axis = 1, inplace = True)
# todo try with these features
cleanData.drop(['name'], axis = 1, inplace = True)
#discarding meaningless data
cleanData = cleanData[cleanData['powerPS'] >= 60]
cleanData = cleanData[cleanData['powerPS'] <= 1000]
cleanData = cleanData[cleanData['yearOfRegistration'] > 1910]
cleanData = cleanData[cleanData['yearOfRegistration'] < 2019]
minP, maxP = 250, 100000
cleanData = cleanData[cleanData['price'] >= minP]
cleanData = cleanData[cleanData['price'] <= maxP]
#removing data points with null feature
print('Total number of rows', len(cleanData))
print('Rows without a vehicle type', cleanData['vehicleType'].isna().sum())
cleanData = cleanData.dropna(subset = ['vehicleType'])
print('Rows without a gearbox type', cleanData['gearbox'].isna().sum())
cleanData = cleanData.dropna(subset = ['gearbox'])
cleanData['gearbox'] = np.where(cleanData['gearbox'] == 'manuell', 1, 0)
print('Rows without a fuel type', cleanData['fuelType'].isna().sum())
cleanData = cleanData.dropna(subset = ['fuelType'])
cleanData = pd.concat([cleanData, pd.get_dummies(cleanData['fuelType'], prefix='fuelType')], axis=1)
cleanData.drop(['fuelType'], axis = 1, inplace = True)
#cleanData['fuelType'].value_counts().plot(kind='bar', title='Fuel type distribution')
#cleanData['brand'].value_counts().plot(kind='bar', figsize=(16, 8), title='Brand distribution of cars')
print('Rows without a notRepairedDamage', cleanData['notRepairedDamage'].isna().sum())
cleanData = cleanData.dropna(subset = ['notRepairedDamage'])
cleanData['isDamaged'] = np.where(cleanData['notRepairedDamage'] == 'ja', 1, 0)
cleanData.drop(['notRepairedDamage'], axis = 1, inplace = True)
#cleanData.plot(y='yearOfRegistration', kind='hist', bins=35, figsize=(10, 7), title='Cars and their registration years')
#raw.plot(y='price', kind='hist', figsize=(10, 7), bins=10, title='Km for cars...')
#pd.DataFrame.hist(raw,'price')
#one hot encoding
cleanData = pd.concat([cleanData, pd.get_dummies(cleanData['brand'], prefix='brand')], axis=1)
#cleanData = cleanData[cleanData['brand'] == 'ford']
print('Rows without a brand', cleanData['brand'].isna().sum())
cleanData.drop(['brand'], axis = 1, inplace = True)
print('Rows without a vehicleType', cleanData['vehicleType'].isna().sum())
cleanData = pd.concat([cleanData, pd.get_dummies(cleanData['vehicleType'], prefix='vehicleType')], axis=1)
cleanData.drop(['vehicleType'], axis = 1, inplace = True)
cleanData = pd.concat([cleanData, pd.get_dummies(cleanData['model'], prefix='model')], axis=1)
cleanData.drop(['model'], axis = 1, inplace = True)
#store price in another array
price = cleanData['price'].to_numpy()[:, np.newaxis]
#featureNames
featNames = list(cleanData.columns)
featuresToNormalize = cleanData[['kilometer','yearOfRegistration', 'powerPS']]
#normalize data min-max normalization
#featuresToNormalize = (featuresToNormalize - featuresToNormalize.min()) / (featuresToNormalize.max() -featuresToNormalize.min())
#standatize
featuresToNormalize = (featuresToNormalize - featuresToNormalize.mean()) / (featuresToNormalize.std())
#featuresToNormalize = (featuresToNormalize - featuresToNormalize.min()) / (featuresToNormalize.max() -featuresToNormalize.min()) - 0.5
cleanData[['kilometer','yearOfRegistration', 'powerPS']] = featuresToNormalize
cleanData = cleanData.to_numpy()
print('Remaining data points after cleaning data:', cleanData.shape[0])
print('Number of features:', cleanData.shape[1] - 1)
featNames = np.asarray(featNames)
cleanData = cleanData[:,1:]
#%%
Number_of_dimensions = 15
# calculate the mean of each column
featureMeans = np.mean(cleanData.T, axis=1)
# center columns by subtracting column means
Centered_data = cleanData #- featureMeans
# calculate covariance matrix of centered matrix
CovMatrice = np.cov(Centered_data.T)
# eigendecomposition of covariance
values, vectors = np.linalg.eig(CovMatrice)
# highest eigenvalue vectors
new_order = (-values).argsort()[:Number_of_dimensions]
new_vectors = vectors[new_order]
pca_outcome = np.dot(Centered_data,new_vectors.T)
#%%
dataSize = np.int(pca_outcome.shape[0])
np.random.seed(2)
idx = np.arange(dataSize)
np.random.shuffle( idx)
#get training, test and validation data
trainPrc = price[idx[:int(dataSize*75/100)]]
trainData = pca_outcome[idx[:int(dataSize*75/100)]]
testPrc = price[idx[int(dataSize*75/100):int(dataSize*90/100)]]
testData = pca_outcome[idx[int(dataSize*75/100):int(dataSize*90/100)]]
valPrc = price[idx[int(dataSize*90/100):]]
valData = pca_outcome[idx[int(dataSize*90/100):]]
testData = np.hstack((np.ones((len(testData),1)), testData))
trainData = np.hstack((np.ones((len(trainData),1)), trainData))
valData = np.hstack((np.ones((len(valData),1)), valData))
#%%
class RandomTree():
def __init__(self, x, y, featureNumber, feature_indices, data_indices, max_depth ,min_node_sample):
self.x = x
self.y = y
self.featureNumber = featureNumber
self.data_indices = data_indices
self.feature_indices = feature_indices
self.max_depth = max_depth
self.min_node_sample = min_node_sample;
self.length = self.data_indices.shape[0]
self.avg_price = np.mean(y[data_indices]) ###################
if np.isnan(self.avg_price):
print("nan avg_price")
self.avg_price=0
if self.length == 1:
print("one")
self.weighted_sd = float('inf')
for j in self.feature_indices:
temp_x = self.x[self.data_indices, j ]
temp_y = self.y[self.data_indices]
sorted_index = np.argsort(temp_x,axis=0) ###
sorted_x, sorted_y = temp_x[sorted_index], temp_y[sorted_index]
rightCount,rightSum,rightSumSquare = self.length, sorted_y.sum(),(sorted_y**2).sum()
leftCount,leftSum,leftSumSquare = 0,0.,0.
for i in range(0,self.length-self.min_node_sample-1):
leftCount += 1;
rightCount -= 1
leftSum += sorted_y[i]
rightSum -= sorted_y[i]
leftSumSquare += sorted_y[i]**2
rightSumSquare -= sorted_y[i]**2
if i<self.min_node_sample or sorted_x[i]==sorted_x[i+1]:
continue
# if leftCount == 0 or rightCount==0 :
# current_weighted_sd = float('inf')
# else:
left_Sd = ((leftSumSquare/leftCount) - (leftSum/leftCount)**2)**0.5
right_Sd = ((rightSumSquare/rightCount) - (rightSum/rightCount)**2)**0.5
current_weighted_sd = left_Sd*leftCount + right_Sd*rightCount
if current_weighted_sd<self.weighted_sd:
self.selected_feature,self.weighted_sd,self.decision_value = j,current_weighted_sd,sorted_x[i] #change this
if self.weighted_sd== float('inf') or self.max_depth <= 0:
return
temp_right_fi = np.arange(self.x.shape[1])
np.random.shuffle(temp_right_fi)
right_feature_indices = temp_right_fi [:self.featureNumber]
temp_left_fi = np.arange(self.x.shape[1])
np.random.shuffle(temp_left_fi)
left_feature_indices = temp_left_fi [:self.featureNumber]
x = self.x[self.data_indices,self.selected_feature]
left_list= []
right_list= []
for k in range(x.shape[0]):
if x[k]<=self.decision_value:
left_list.append(k)
else:
right_list.append(k)
left_tree_indices = np.asarray(left_list)
righ_tree_indices = np.asarray(right_list)
# if left_tree_indices.shape[0] < 2 or righ_tree_indices.shape[0] < 2:
# print("number of samples for tree is less than 2")
self.left_tree = RandomTree(self.x, self.y, self.featureNumber, left_feature_indices, self.data_indices[left_tree_indices], max_depth=self.max_depth-1, min_node_sample=self.min_node_sample)
self.right_tree = RandomTree(self.x, self.y, self.featureNumber, right_feature_indices, self.data_indices[righ_tree_indices], max_depth=self.max_depth-1, min_node_sample=self.min_node_sample)
def find_estimate (self, x):
return np.array([self.find_one_estimate(xi) for xi in x])
def find_one_estimate(self, xi):
if self.weighted_sd== float('inf') or self.max_depth <= 0:
return self.avg_price
if xi[self.selected_feature]<=self.decision_value:
smaller_tree = self.left_tree
else:
smaller_tree = self.right_tree
return smaller_tree.find_one_estimate(xi)
tree_number = 15;
feature_number = 8;
forest_depth = float('inf')
min_leaf_sample = 1;
avg_errors = np.zeros((2,10))
error_values_train = np.zeros((5,10))
error_values_test = np.zeros((5,10))
#validation
data_train = trainData
price_train = trainPrc
test_x = testData
price_of_test = testPrc
initial_indices=np.array(range(data_train.shape[0]))
forest2 = [];
for i in range(tree_number):
print("new tree")
# temp_di = np.arange(data_train.shape[0])
# np.random.shuffle(temp_di)
# x_indices = temp_di [:sample_size]
x_indices = np.random.randint(data_train.shape[0], size=data_train.shape[0])
temp_fi = np.arange(data_train.shape[1])
np.random.shuffle(temp_fi)
feature_indices = temp_fi [:feature_number]
forest2.append(RandomTree(data_train[x_indices], price_train[x_indices], feature_number, feature_indices,
data_indices=initial_indices, max_depth = forest_depth, min_node_sample = min_leaf_sample))
print("prediction")
predict_outcome = np.mean([t.find_estimate(data_train) for t in forest2], axis=0)
predict_outcome_test = np.mean([t.find_estimate(test_x) for t in forest2], axis=0)
prediction_mat = np.asmatrix(predict_outcome);
prediction_mat = prediction_mat.T
nan_count = 0
sum_er = 0
real_price = (100000-250)*price_train+250
real_prediction = (100000-250)*prediction_mat+250
for i in range (1, prediction_mat.shape[0]-1):
if not (np.isnan(prediction_mat[i,0])):
sum_er = sum_er + np.absolute(real_prediction[i,0] - real_price[i,0])/real_price[i,0]
else:
nan_count += 1
error_values_train[0,0] = sum_er / (prediction_mat.shape[0] - nan_count);
prediction_mat2 = np.asmatrix(predict_outcome_test);
prediction_mat2 = prediction_mat2.T
nan_count2 = 0
sum_er2 = 0
real_price2 = (100000-250)*price_of_test+250
real_prediction2 = (100000-250)*prediction_mat2+250
for i in range (1, prediction_mat2.shape[0]-1):
if not (np.isnan(prediction_mat2[i,0])):
sum_er2 = sum_er2 + np.absolute(real_prediction2[i,0] - real_price2[i,0])/real_price2[i,0]
else:
nan_count2 += 1
error_values_test[0,0] = sum_er2 / (prediction_mat2.shape[0] - nan_count2);
avg_errors [0,0] = np.mean(error_values_train[:,0])
avg_errors [1,0] = np.mean(error_values_test[:,0])