def pre_load_fixed_data():
keywords = [
"music", "food", "sport", "show", "movie", "car", "commercial",
"party", "war", "hello"
]
data = DataReader()
return data.read("static/data/tweets.txt", keywords)
def test_read_function(url):
"""
Test method for read method in read_data class with given
url, does not crash if it passed
"""
web_data = DataReader(url)
data, columns = web_data.read()
assert_equals(True, len(data) > 0)
assert_equals(True, len(columns) > 0)
assert_equals(252, len(data))
assert_equals(15, len(columns))
assert_equals(23, data.loc[0, 'Age (years)'])
assert_equals('Weight (lbs)', columns[3])
return data, columns
def main():
sns.set(font_scale=0.7)
# Process Data
url = 'http://lib.stat.cmu.edu/datasets/bodyfat'
web_data = DataReader(url)
data, columns = web_data.read()
# Plotting
all_correlation = correlation_chart(data, columns)
graphs(data, all_correlation)
x = data.loc[:, 'Age (years)':'Wrist circumference (cm)']
y = data["Percent body fat from Siri's (1956) equation"].to_numpy()
# Linear Regression
x_train, x_test, y_train, y_test = \
train_test_split(x, y, test_size=0.4, random_state=1)
model = DecisionTreeRegressor()
model.fit(x_train, y_train)
linear_reg_model = linear_regression_fit(x_train, y_train)
print('MSE for linear train:',
mean_squared_error(y_train, linear_reg_model.predict(x_train)))
print('MSE for linear test:',
mean_squared_error(y_test, linear_reg_model.predict(x_test)))
print('MSE for decisiontree train:',
mean_squared_error(y_train, model.predict(x_train)))
print('MSE for decisiontree test:',
mean_squared_error(y_test, model.predict(x_test)))
# High correlation part
x_high_correlation = data[high_correlation(all_correlation)].copy()
x_high_train, x_high_test, y_high_train, y_high_test = \
train_test_split(x_high_correlation, y, test_size=0.4, random_state=1)
high_model = DecisionTreeRegressor()
high_model.fit(x_high_train, y_high_train)
high_correlation_model = linear_regression_fit(x_high_train, y_high_train)
print(
'MSE for high correlation train:',
mean_squared_error(y_high_train,
high_correlation_model.predict(x_high_train)))
print(
'MSE for high correlation test:',
mean_squared_error(y_high_test,
high_correlation_model.predict(x_high_test)))
print('MSE for high correlation decisiontree train:',
mean_squared_error(y_high_train, high_model.predict(x_high_train)))
print('MSE for high correlation decisiontree test:',
mean_squared_error(y_high_test, high_model.predict(x_high_test)))
