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ml-model.py
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ml-model.py
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# %%
# Libraries:
from joblib import dump
import json
import pandas as pd
import numpy as np
#import seaborn as sns
import sklearn.metrics
from sklearn.model_selection import RandomizedSearchCV, train_test_split
from skopt import BayesSearchCV
from skopt.space import Real, Categorical, Integer
#from sklearn.ensemble import RandomForestRegressor
from xgboost.sklearn import XGBRegressor
from xgboost import plot_importance
import matplotlib.pyplot as plt
#from catboost import CatBoostRegressor
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from scipy.stats import randint, uniform
from joblib import load
from data.functions.ml_functions import get_feature_names, get_feature_out, get_ct_feature_names
#from data.functions.selenium_funcs import RedfinScraper
# Tune hyper paramters?
hyper_param_tune = False
# %%%
df = pd.read_csv(r'.\data\Model_Data.csv', low_memory=False, thousands=',')
# %%
categorical_features = ['PROPERTY TYPE',
# , 'CITY'
# , 'kmn_cluster'
# , 'ZIP OR POSTAL CODE'
'AIR_CONDITIONING_CODE',
'FUEL_CODE',
'HEATING_TYPE_CODE',
'EXTERIOR_WALL_CODE',
'ROOF_TYPE_CODE',
'PARKING_TYPE',
'STYLE_CODE',
'BASEMENT_TYPE_CODE',
'BUILDING_CODE'
]
# %%
#df = df[(pd.to_datetime(df['DATE SOLD_UNIX'], unit='s', origin='unix').dt.year >= 2018)]
df = df[~df['DATE SOLD_UNIX'].isna()]
df = df[np.abs(df['PRICE']-df['PRICE'].mean()) <= (5*df['PRICE'].std())]
#df = df[df['PRICE'] < 10000000]
#df = df[df['PRICE'] > 10000]
# %%
print(df.nunique())
print(df.info())
#del test,start
# %%
X = df.drop(columns=['PRICE'])
y = np.log(df['PRICE'])
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.20, random_state=42)
# %%
categorical_transformer = Pipeline(steps=[
('imputer_cat', SimpleImputer(strategy='most_frequent' # , fill_value='Unknown'
)),
('ohe', OneHotEncoder(handle_unknown='ignore'))])
numeric_features = X.drop(columns=categorical_features).columns.values
numeric_transformer = Pipeline(steps=[
('imputer_num', SimpleImputer(strategy='mean')),
('scaler', StandardScaler())])
preprocessor = ColumnTransformer(
transformers=[
('num', numeric_transformer, numeric_features),
('cat', categorical_transformer, categorical_features)])
# %%
print(X_train.dtypes.to_dict())
X_train = preprocessor.fit_transform(X_train)
try:
X_train = pd.DataFrame(X_train,
columns=get_ct_feature_names(preprocessor))
except:
X_train = pd.DataFrame.sparse.from_spmatrix(X_train,
columns=get_ct_feature_names(preprocessor))
# %%
#preprocessor = load('preprocessor.joblib')
X_test = preprocessor.transform(X_test)
try:
X_test = pd.DataFrame(X_test, columns=get_ct_feature_names(preprocessor))
except:
X_test = pd.DataFrame.sparse.from_spmatrix(
X_test, columns=get_ct_feature_names(preprocessor))
# %%
# X_train.to_csv('test2.csv')
# %%
with open('regression_best_params.json') as json_file:
reg_best_params = json.load(json_file)
# %%
model = XGBRegressor(**reg_best_params, tree_method='gpu_hist', gpu_id=0, random_state=42
)
# Tuning
if hyper_param_tune == True:
param_dist = {'n_estimators': Integer(750, 1150),
'learning_rate': Real(0.02, 0.04),
'subsample': Real(0.4, 0.6),
'max_depth': Integer(10, 14),
'colsample_bytree': Real(0.01, 0.99),
'min_child_weight': Integer(7, 11)
}
model = BayesSearchCV(model, param_dist, verbose=5, random_state=42, n_jobs=-1
)
# Fit model
# %%
eval_set = [(X_train, y_train), (X_test, y_test)]
#
model.fit(X_train, y_train
#, verbose=5
#, eval_metric="rmse", eval_set=eval_set, early_stopping_rounds=300
)
# %%
if hyper_param_tune == True:
jsn = json.dumps(model.best_params_)
f = open("regression_best_params.json", "w")
f.write(jsn)
f.close()
# %%
y_pred = model.predict(X_test)
mse = sklearn.metrics.mean_squared_error(np.exp(y_test), np.exp(y_pred))
rmse = mse**(1/2.0)
print(rmse)
# %%
if hyper_param_tune == False:
ax = plot_importance(model)
fig = ax.figure
fig.set_size_inches(10, 25)
fig.tight_layout()
fig.savefig('xgboost feature importance.png')
# %%
#from sklearn.feature_selection import SelectFromModel
#feat_compare = pd.DataFrame()
#
# 60 features is a good cutoff
#thresholds = np.array([10,11,12,13,14,15])
# for thresh in thresholds:
# # select features using threshold
# selection = SelectFromModel(model, max_features=thresh, prefit=True)
# select_X_train = selection.transform(X_train)
# select_X_test = selection.transform(X_test)
# select_eval_set = [(select_X_train, y_train), (select_X_test, y_test)]
# # train model
# selection_model = XGBRegressor(**reg_best_params
# ,tree_method='gpu_hist', gpu_id = 0
# ,random_state = 42
# )
# selection_model.fit(select_X_train, y_train
# , eval_metric="rmse", eval_set=select_eval_set
# , early_stopping_rounds=300)
# # eval model
# select_X_test = selection.transform(X_test)
# predictions = selection_model.predict(select_X_test)
# rmse = sklearn.metrics.mean_squared_error(np.exp(y_test), np.exp(predictions))**(1/2.000)
# print("Thresh=%.3f, n=%d, RMSE: %d" % (thresh, select_X_train.shape[1], rmse))
# feat_compare = feat_compare.append({'thresh': thresh, 'rmse': rmse}, ignore_index = True)
#
# plt.figure(figsize=(10,10))
#plt.scatter(feat_compare['thresh'].values, feat_compare['rmse'].values, c='crimson')
#plt.xlabel('True Values', fontsize=15)
#plt.ylabel('Predictions', fontsize=15)
# plt.show()
#
#
# %%
#
#selection = SelectFromModel(model, max_features=15, prefit=True)
#select_X_train = selection.transform(X_train)
#select_X_test = selection.transform(X_test)
#
#
#select_eval_set = [(select_X_train, y_train), (select_X_test, y_test)]
#
# train model
# selection_model = XGBRegressor(**reg_best_params
# ,tree_method='gpu_hist', gpu_id = 0
# ,random_state = 42
# )
# selection_model.fit(select_X_train, y_train
# , eval_metric="rmse", eval_set=select_eval_set
# , early_stopping_rounds=300)
# eval model
#
#predictions = selection_model.predict(select_X_test)
#rmse = sklearn.metrics.mean_squared_error(np.exp(y_test), np.exp(predictions))**(1/2.000)
#print("Thresh=%.3f, n=%d, RMSE: %d" % (thresh, select_X_train.shape[1], rmse))
# %%
X = preprocessor.transform(X)
try:
X = pd.DataFrame(X, columns=get_ct_feature_names(preprocessor))
except:
X = pd.DataFrame.sparse.from_spmatrix(
X, columns=get_ct_feature_names(preprocessor))
# Final Model
model.fit(X, y, verbose=True
)
# %%
dump(model, 'model.joblib')
dump(preprocessor, 'preprocessor.joblib')
# %%