def parksParamTuning():
train_scores_dict = {}
test_scores_dict = {}
radius_list = [0.5, 1]
area_list = [100, 200]
for radius in radius_list:
for area in area_list:
file_name = "_parksRadius" + str(radius) + "_area" + str(area)
all_data = MainTable(extra=file_name)
df = all_data.getDB()
# Split to Data and Actual results
X = selectCols(df, features)
y = df['SQR_FEET_PRICE']
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42)
tot_train_score = 0
tot_test_score = 0
n = 5
for i in range(0, n):
regressor = RandomForestRegressor(n_estimators=N_ESTIMATORS,
min_impurity_decrease=200)
regressor.fit(X_train, y_train)
tot_train_score += regressor.score(X_train, y_train)
tot_test_score += regressor.score(X_test, y_test)
train_scores_dict["radius " + str(radius) + "\narea " +
str(area)] = tot_train_score / n
test_scores_dict["radius " + str(radius) + "\narea " +
str(area)] = tot_test_score / n
graph_paramTuning(train_scores_dict, test_scores_dict,
'Tuning parks radius and area with Desicion Trees',
'Parks radius and area')
def paramTuning(file_name, param_values_list, param_name):
train_scores_dict = {}
test_scores_dict = {}
for p in param_values_list:
# Get the base table
all_data = MainTable(extra=file_name + str(p))
df = all_data.getDB()
# Split to Data and Actual results
X = selectCols(df, features)
y = df['SQR_FEET_PRICE']
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=42)
tot_train_score = 0
tot_test_score = 0
n = 5
for i in range(1, n):
regressor = RandomForestRegressor(n_estimators=N_ESTIMATORS,
min_impurity_decrease=200)
regressor.fit(X_train, y_train)
tot_train_score += regressor.score(X_train, y_train)
tot_test_score += regressor.score(X_test, y_test)
train_scores_dict[p] = tot_train_score / n
test_scores_dict[p] = tot_test_score / n
graph_paramTuning(train_scores_dict, test_scores_dict,
'Tuning ' + param_name + 'with Desicion Trees',
param_name)
def compareFeatures():
# Get the base table
all_data = MainTable()
df = all_data.getDB()
base_feats = [ 'BOROUGH',
'BUILDING_AGE' ]
external_feats = [ 'CRIMES',
'HI_ED',
'HIGH_SCHOOLS',
'BUS_STOPS',
'SUBWAY_STOPS',
'NUM_OF_PARKS',
'AREA_OF_PARKS',
'NOISE',
'HEALTH',
'GALLERIES',
'MUSEUMS' ]
mean_train_score_b, mean_test_score_b = getBaseFeatsScores(df, base_feats)
train_scores_dict = {}
test_scores_dict = {}
for feat in external_feats:
curr_feats = base_feats
curr_feats.append(feat)
# Split to Data and Actual results
X = selectCols(df, curr_feats)
y = df['SQR_FEET_PRICE']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
tot_train_score = 0
tot_test_score = 0
n = 5
for i in range(1, n+1):
regressor = DecisionTreeRegressor(min_impurity_decrease=200)
regressor.fit(X_train, y_train)
tot_train_score += regressor.score(X_train, y_train)
tot_test_score += regressor.score(X_test, y_test)
train_scores_dict[feat] = tot_train_score / n
test_scores_dict[feat] = tot_test_score / n
graph_barsForFeatures(train_scores_dict, test_scores_dict, 'Comparing features using Desicion Trees',
'Feature Name', mean_train_score_b, mean_test_score_b)
def __init__(self, save):
self.save = save
self.data = MainTable().getDB()
training_set, test_set = train_test_split(self.data, test_size=TEST_SIZE)
# separate data to apartments features (without prices) and apartments prices
self.training_features = removeCols(training_set, ['SQR_FEET_PRICE'])
self.test_features = removeCols(test_set, ['SQR_FEET_PRICE'])
self.training_prices = selectCols(training_set, ['SQR_FEET_PRICE'])
self.test_prices = selectCols(test_set, ['SQR_FEET_PRICE'])
self.all_data_without_prices = removeCols(self.data, ['SQR_FEET_PRICE'])
self.all_data_only_prices = selectCols(self.data, ['SQR_FEET_PRICE'])
def paramTuning(file_name, param_values_list, param_name):
train_scores_dict = {}
test_scores_dict = {}
for p in param_values_list:
# Get the base table
all_data = MainTable(extra = file_name + str(p))
df = all_data.getDB()
# Split to Data and Actual results
X = selectCols(df, features)
y = df['SQR_FEET_PRICE']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
regressor = neighbors.KNeighborsRegressor(n_neighbors=16)
regressor.fit(X_train, y_train)
train_score = regressor.score(X_train, y_train)
test_score = regressor.score(X_test, y_test)
train_scores_dict[p] = train_score
test_scores_dict[p] = test_score
graph_paramTuning(train_scores_dict, test_scores_dict, 'KNN', param_name)
y = df['SQR_FEET_PRICE']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
regressor = neighbors.KNeighborsRegressor(n_neighbors=16)
regressor.fit(X_train, y_train)
train_score = regressor.score(X_train, y_train)
test_score = regressor.score(X_test, y_test)
train_scores_dict[p] = train_score
test_scores_dict[p] = test_score
graph_paramTuning(train_scores_dict, test_scores_dict, 'KNN', param_name)
if __name__ == '__main__':
# # Get the base table
all_data = MainTable()
df = all_data.getDB()
# # Split to Data and Actual results
X = selectCols(df, features)
y = df['SQR_FEET_PRICE']
# Basic prediction with optimal K
y_pred, test_score, train_score = predictionAndScore(X, y, 16)
print("KNN: Training score: " + str(train_score) + "Test score: " + str(test_score))
# Cross validation
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
knn_regressor = neighbors.KNeighborsRegressor(n_neighbors=16)
cv_results = cross_validate(knn_regressor, X_train, y_train, cv=3, return_train_score=True)
print("KNN w/ Cross Validation: Training score: " + str(cv_results['train_score']) + ", Test score: " + str(cv_results['test_score']))