Data-Analysis-Jupyter/sentdex_data_analysis/pandas_TPOT.py

import pandas as pd
import numpy as np
from tpot import TPOTClassifier
from sklearn.model_selection import train_test_split

benchmark = pd.read_pickle(
    "us_pct.pickle"
)  # us overall housing price index percentage change
HPI = pd.read_pickle(
    "HPI_complete.pickle"
)  # all of the state data, thirty year mortgage, unemployment rate, GDP, SP500
HPI = HPI.join(benchmark["United States"])
# all in percentage change since the start of the data (1975-01-01)

HPI.dropna(inplace=True)

housing_pct = HPI.pct_change()
housing_pct.replace([np.inf, -np.inf], np.nan, inplace=True)

housing_pct["US_HPI_future"] = housing_pct["United States"].shift(-1)
housing_pct.dropna(inplace=True)


def create_labels(cur_hpi, fut_hpi):
    if fut_hpi > cur_hpi:
        return 1
    else:
        return 0


housing_pct["label"] = list(
    map(create_labels, housing_pct["United States"], housing_pct["US_HPI_future"])
)

# housing_pct['ma_apply_example'] = housing_pct['M30'].rolling(window=10).apply(moving_average)
# print(housing_pct.tail())
X = np.array(housing_pct.drop(["label", "US_HPI_future"], 1))
y = np.array(housing_pct["label"])

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)

tpot = TPOTClassifier(generations=10, population_size=20, verbosity=2)
tpot.fit(X_train, y_train)
print(tpot.score(X_test, y_test))
tpot.export("HPI_tpot_pipeline.py")