def average(input_df, kunag, matnr):
"""
applies average model and calculates mse score on test data
:param input_df:
:param kunag:
:param matnr:
:return:
"""
df = input_df.copy()
df = remove_negative_rows(df)
df_series = individual_series(df, kunag, matnr)
df_series = data_transformation.get_weekly_aggregate(df_series)
df_series["date"] = df_series["dt_week"].map(str)
df_series["date"] = df_series["date"].apply(lambda x: x.replace("-", ""))
df_series["prediction"] = df_series["quantity"]
df_series_train, df_series_test = splitter(df_series)
k = 0
for index, row in df_series_test.iterrows():
row["prediction"] = df_series_train["quantity"].mean()
df_series_train = pd.concat([df_series_train,
pd.DataFrame(row).T
]).reset_index(drop=True)
if k == 0:
test_index = df_series_train.shape[0] - 1
k = 1
output_df = df_series_train
test_df = df_series_train.iloc[test_index:]
# print("mean squared error is :",mean_squared_error(output_df["quantity"], output_df["prediction"]))
return output_df, mean_squared_error(test_df["quantity"],
test_df["prediction"])
def arima(input_df, kunag, matnr, p=2, d=1, q=4, trend="n"):
df = input_df.copy()
df = remove_negative_rows(df)
df_series = individual_series(df, kunag, matnr)
df_series = data_transformation.get_weekly_aggregate(df_series)
df_series["date"] = df_series["dt_week"].map(str)
df_series["date"] = df_series["date"].apply(lambda x: x.replace("-", ""))
df_series["prediction"] = df_series["quantity"]
df_series_train, df_series_test = splitter(df_series)
k = 0
for index, row in df_series_test.iterrows():
df_series_train["quantity"] = df_series_train["quantity"].map(float)
fit1 = sm.tsa.statespace.SARIMAX(df_series_train["quantity"],
order=(p, d, q),
trend=trend).fit()
predicted = fit1.forecast(1)
row["prediction"] = predicted.values[0]
df_series_train = pd.concat([df_series_train,
pd.DataFrame(row).T
]).reset_index(drop=True)
if k == 0:
test_index = df_series_train.shape[0] - 1
k = 1
output_df = df_series_train
test_df = df_series_train.iloc[test_index:]
# print("mean squared error is :",mean_squared_error(output_df["quantity"], output_df["prediction"]))
return output_df, mean_squared_error(test_df["quantity"],
test_df["prediction"])
def holts_linear_trend(input_df,
kunag,
matnr,
smoothing_level=0.3,
smoothing_slope=0.1):
df = input_df.copy()
df = remove_negative_rows(df)
df_series = individual_series(df, kunag, matnr)
df_series = data_transformation.get_weekly_aggregate(df_series)
df_series["date"] = df_series["dt_week"].map(str)
df_series["date"] = df_series["date"].apply(lambda x: x.replace("-", ""))
df_series["prediction"] = df_series["quantity"]
df_series_train, df_series_test = splitter(df_series)
k = 0
for index, row in df_series_test.iterrows():
df_series_train["quantity"] = df_series_train["quantity"].map(float)
fit1 = Holt(np.asarray(df_series_train["quantity"])).fit(
smoothing_level=smoothing_level, smoothing_slope=smoothing_slope)
predicted = fit1.forecast(1)
row["prediction"] = predicted[0]
df_series_train = pd.concat([df_series_train,
pd.DataFrame(row).T
]).reset_index(drop=True)
if k == 0:
test_index = df_series_train.shape[0] - 1
k = 1
output_df = df_series_train
test_df = df_series_train.iloc[test_index:]
# print("mean squared error is :",mean_squared_error(output_df["quantity"], output_df["prediction"]))
return output_df, mean_squared_error(test_df["quantity"],
test_df["prediction"])
def simple_exponential_smoothing(input_df, kunag, matnr, alpha=0.6):
df = input_df.copy()
df = remove_negative_rows(df)
df_series = individual_series(df, kunag, matnr)
df_series = data_transformation.get_weekly_aggregate(df_series)
df_series["date"] = df_series["dt_week"].map(str)
df_series["date"] = df_series["date"].apply(lambda x: x.replace("-", ""))
df_series["prediction"] = df_series["quantity"]
df_series_train, df_series_test = splitter(df_series)
k = 0
for index, row in df_series_test.iterrows():
fit2 = SimpleExpSmoothing(np.asarray(df_series_train["quantity"])).fit(
smoothing_level=alpha, optimized=False)
row["prediction"] = fit2.forecast(1)
df_series_train = pd.concat([df_series_train,
pd.DataFrame(row).T
]).reset_index(drop=True)
if k == 0:
test_index = df_series_train.shape[0] - 1
k = 1
output_df = df_series_train
test_df = df_series_train.iloc[test_index:]
# print("mean squared error is :",mean_squared_error(output_df["quantity"], output_df["prediction"]))
return output_df, mean_squared_error(test_df["quantity"],
test_df["prediction"])
def prophet(input_df,
kunag,
matnr,
growth="linear",
changepoint_prior_scale=0.5,
yearly_seasonality=False,
daily_seasonality=False,
weekly_seasonality=False,
seasonality_prior_scale=100):
df = input_df.copy()
df = remove_negative_rows(df)
df_series = individual_series(
df,
kunag,
matnr,
)
df_series = data_transformation.get_weekly_aggregate(df_series)
df_series["date"] = df_series["dt_week"].map(str)
df_series["date"] = df_series["date"].apply(lambda x: x.replace("-", ""))
df_series["prediction"] = df_series["quantity"]
df_series_train, df_series_test = splitter(df_series)
k = 0
for index, row in df_series_test.iterrows():
df_series_train["quantity"] = df_series_train["quantity"].map(float)
df_copy = df_series_train.copy()
ts = prophet_preprocess(df_copy)
m = Prophet(growth=growth,
changepoint_prior_scale=changepoint_prior_scale,
yearly_seasonality=yearly_seasonality,
daily_seasonality=daily_seasonality,
weekly_seasonality=weekly_seasonality,
seasonality_prior_scale=seasonality_prior_scale)
m.fit(ts)
future = m.make_future_dataframe(
periods=1, freq="W", include_history=False).apply(
lambda x:
(x + pd.Timedelta(4, unit="D"))) # + pd.Timedelta(4, unit="D")
forecast = m.predict(future)
row["prediction"] = forecast.iloc[0]["yhat"]
df_series_train = pd.concat([df_series_train,
pd.DataFrame(row).T
]).reset_index(drop=True)
if k == 0:
test_index = df_series_train.shape[0] - 1
k = 1
output_df = df_series_train
test_df = df_series_train.iloc[test_index:]
return output_df, mean_squared_error(test_df["quantity"],
test_df["prediction"])
def holts_winter_method(input_df,
kunag,
matnr,
seasonal_period,
alpha=None,
beta=None,
gamma=None):
"""
:param input_df:
:param kunag:
:param matnr:
:param seasonal_period:
:param alpha:
:param beta:
:param gamma:
:return:
"""
df = input_df.copy()
df = remove_negative_rows(df)
df_series = individual_series(df, kunag, matnr)
df_series = data_transformation.get_weekly_aggregate(df_series)
df_series["date"] = df_series["dt_week"].map(str)
df_series["date"] = df_series["date"].apply(lambda x: x.replace("-", ""))
df_series["prediction"] = df_series["quantity"]
df_series_train, df_series_test = splitter(df_series)
k = 0
for index, row in df_series_test.iterrows():
df_series_train["quantity"] = df_series_train["quantity"].map(float)
fit1 = ExponentialSmoothing(
np.asarray(df_series_train["quantity"]),
seasonal_periods=seasonal_period,
trend='add',
seasonal='add',
).fit(smoothing_level=alpha,
smoothing_slope=beta,
smoothing_seasonal=gamma)
predicted = fit1.forecast(1)
row["prediction"] = predicted[0]
df_series_train = pd.concat([df_series_train,
pd.DataFrame(row).T
]).reset_index(drop=True)
if k == 0:
test_index = df_series_train.shape[0] - 1
k = 1
output_df = df_series_train
test_df = df_series_train.iloc[test_index:]
# print("mean squared error is :",mean_squared_error(output_df["quantity"], output_df["prediction"]))
return output_df, mean_squared_error(test_df["quantity"],
test_df["prediction"])