model.py

import pandas as pd
import matplotlib.pyplot as plt
import acquire
import prepare
import math
from sklearn import metrics

df = acquire.acquire_data()
df = prepare.prepare_data(df)


def split_store_data(df, train_prop=.7): 
    train_size = int(len(df) * train_prop)
    train, test = df[0:train_size], df[train_size:len(df)]
    return train, test

train, test = split_store_data(df)
target_vars=['steps']
yhat = pd.DataFrame(test[target_vars])

def evaluate(target_var, train = train, test = test, output=True):
    mse = metrics.mean_squared_error(test[target_var], yhat[target_var])
    rmse = math.sqrt(mse)

    if output:
        print('MSE:  {}'.format(mse))
        print('RMSE: {}'.format(rmse))
    else:
        return mse, rmse



def plot_and_eval(target_vars, train = train, test = test, metric_fmt = '{:.2f}', linewidth = 4):
    if type(target_vars) is not list:
        target_vars = [target_vars]

    plt.figure(figsize=(16, 8))
    plt.plot(train[target_vars],label='Train', linewidth=1)
    plt.plot(test[target_vars], label='Test', linewidth=1)

    for var in target_vars:
        mse, rmse = evaluate(target_var = var, train = train, test = test, output=False)
        plt.plot(yhat[var], linewidth=linewidth)
        print(f'{var} -- MSE: {metric_fmt} RMSE: {metric_fmt}'.format(mse, rmse))

    plt.show()    


eval_df = pd.DataFrame(columns=['model_type', 'target_var', 'metric', 'value'])

def append_eval_df(model_type, target_vars, train = train, test = test):
    temp_eval_df = pd.concat([pd.DataFrame([[model_type, i, 'mse', evaluate(target_var = i, 
                                                                            train = train, 
                                                                            test = test, 
                                                                            output=False)[0]],
                                            [model_type, i, 'rmse', evaluate(target_var = i, 
                                                                             train = train, 
                                                                             test = test, 
                                                                             output=False)[1]]],
                                           columns=['model_type', 'target_var', 'metric', 'value']) 
                              for i in target_vars], ignore_index=True)
    return eval_df.append(temp_eval_df, ignore_index=True)