In [1]:
#Import required packages
import pandas as pd
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import datetime
import seaborn as sns
import matplotlib.pyplot as plt
from statsmodels.tsa.stattools import adfuller
from statsmodels.tsa.seasonal import seasonal_decompose
In [2]:
def format_date(df_date):
"""
Splits Meeting Times and Dates into datetime objects where applicable using regex.
"""
df_date['Days'] = df_date['Meeting_Times'].str.extract('([^\s]+)', expand=True)
df_date['Start_Date'] = df_date['Meeting_Dates'].str.extract('([^\s]+)', expand=True)
df_date['Year'] = df_date['Term'].astype(str).str.slice(0,4)
df_date['Quarter'] = df_date['Term'].astype(str).str.slice(4,6)
df_date['Term_Date'] = pd.to_datetime(df_date['Year'] + df_date['Quarter'], format='%Y%m')
df_date['Start_Month'] = pd.to_datetime(df_date['Year'] + df_date['Start_Date'], format='%Y%b')
df_date['End_Date'] = df_date['Meeting_Dates'].str.extract('(?<=-)(.*)(?= )', expand=True)
#df_date['End_Month'] = pd.to_datetime(df_date['End_Date'], format='%b')
df_date['Start_Time'] = df_date['Meeting_Times'].str.extract('(?<= )(.*)(?=-)', expand=True)
df_date['Start_Time'] = pd.to_datetime(df_date['Start_Time'], format='%H%M')
df_date['End_Time'] = df_date['Meeting_Times'].str.extract('((?<=-).*$)', expand=True)
df_date['End_Time'] = pd.to_datetime(df_date['End_Time'], format='%H%M')
df_date['Duration_Hr'] = ((df_date['End_Time'] - df_date['Start_Time']).dt.seconds)/3600
#df_date = df_date.set_index(pd.DatetimeIndex(df_date['Term_Date']))
return df_date
def format_xlist(df_xl):
"""
revises % capacity calculations by using Max Enrollment instead of room capacity.
"""
df_xl['Cap_Diff'] = np.where(df_xl['Xlst'] != '',
df_xl['Max_Enrl'].astype(int) - df_xl['Actual_Enrl'].astype(int),
df_xl['Room_Capacity'].astype(int) - df_xl['Actual_Enrl'].astype(int))
df_xl = df_xl.loc[df_xl['Room_Capacity'].astype(int) < 999]
return df_xl
def grouped_plot_graph(df_in):
fig, ax = plt.subplots()
grouped = df_in.groupby(['Year', 'Quarter'])
for key, group in grouped:
group.plot(ax=ax, kind='scatter', x='Start_Month', y='Cap_Diff', label=key)
plt.show()
def plot_graph(x_vals, y_vals):
"""
Plots the dataframe information.
"""
x = range(len(x_vals))
plt.figure(figsize=(20,10))
N = 50
colors = np.random.rand(N)
sns.stripplot(x_vals, y_vals)
plt.xticks(rotation=90)
#plt.scatter(x, y_vals, alpha=0.5, )
plt.show()
def OLS_operations(y, X):
#mod = smf.OLS('Cap_Diff ~ C(Dept)', data=df_data)
mod = sm.OLS(np.asarray(y), X)
res = mod.fit()
print(res.summary())
fig = plt.figure(figsize=(20,10))
fig = sm.graphics.plot_partregress_grid(mod, fig=fig)
plt.show()
def test_stationarity(timeseries):
#Determing rolling statistics
rolmean = pd.Series.rolling(timeseries, center=False,window=4).mean()
rolstd = pd.Series.rolling(timeseries, center=False,window=4).std()
#Plot rolling statistics:
orig = plt.plot(timeseries, color='blue',label='Original')
mean = plt.plot(rolmean, color='red', label='Rolling Mean')
std = plt.plot(rolstd, color='black', label = 'Rolling Std')
plt.legend(loc='best')
plt.title('Rolling Mean & Standard Deviation')
#plt.figure(figsize=(20,10))
plt.show(block=False)
#Perform Dickey-Fuller test:
print('Results of Dickey-Fuller Test:')
dftest = adfuller(timeseries, autolag='AIC')
dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
for key,value in dftest[4].items():
dfoutput['Critical Value (%s)'%key] = value
print(dfoutput)
In [7]:
"""
Main program control flow.
"""
#pd.set_option('display.max_rows', None)
pd.set_option('display.max_columns', None)
df = pd.read_csv('data/PSU_master_classroom.csv')
df = df.fillna('')
df = format_date(df)
# Avoid classes that only occur on a single day
df = df.loc[df['Start_Date'] != df['End_Date']]
df = df.loc[df['Online Instruct Method'] != 'Fully Online']
# Calculate number of days per week and treat Sunday condition
df['Days_Per_Week'] = df['Days'].str.len()
df['Room_Capacity'] = df['Room_Capacity'].apply(lambda x: x if (x != 'No Data Available') else 0)
df_cl = format_xlist(df)
df_cl = df_cl.groupby('Term_Date').sum()
In [8]:
test_stationarity(df_cl['Cap_Diff'])
In [13]:
#Estimating and eliminating trend
ts_log = np.log(df_cl['Cap_Diff'])
moving_avg = pd.Series.rolling(ts_log, center=False,window=4).mean()
plt.plot(ts_log)
plt.plot(moving_avg, color='red')
plt.show()
In [14]:
ts_log_moving_avg_diff = ts_log - moving_avg
ts_log_moving_avg_diff.head(4)
ts_log_moving_avg_diff.dropna(inplace=True)
print("\n ===Test stationarity using log moving average===\n")
test_stationarity(ts_log_moving_avg_diff)
In [16]:
print("\n ===Use Exponentially Weighted Moving Average===\n")
expwighted_avg = pd.Series.ewm(ts_log, ignore_na=False,min_periods=0,adjust=True,halflife=4).mean()
ts_log_ewma_diff = ts_log - expwighted_avg
test_stationarity(ts_log_ewma_diff)
In [17]:
#Differencing
print("\n ===Use Differencing Technique===\n")
ts_log_diff = ts_log - ts_log.shift()
plt.plot(ts_log_diff)
ts_log_diff.dropna(inplace=True)
test_stationarity(ts_log_diff)
In [18]:
decomposition = seasonal_decompose(ts_log, freq=4)
trend = decomposition.trend
seasonal = decomposition.seasonal
residual = decomposition.resid
plt.subplot(411)
plt.plot(ts_log, label='Original')
plt.legend(loc='best')
plt.subplot(412)
plt.plot(trend, label='Trend')
plt.legend(loc='best')
plt.subplot(413)
plt.plot(seasonal,label='Seasonality')
plt.legend(loc='best')
plt.subplot(414)
plt.plot(residual, label='Residuals')
plt.legend(loc='best')
plt.tight_layout()
plt.show()
In [20]:
print("\n ===Test Stationarity of Residuals===\n")
ts_log_decompose = residual
ts_log_decompose.dropna(inplace=True)
test_stationarity(ts_log_decompose)
In [32]:
from statsmodels.tsa.arima_model import ARIMA
from statsmodels.tsa.stattools import acf, pacf
lag_acf = acf(ts_log_diff, nlags=10)
lag_pacf = pacf(ts_log_diff, nlags=10, method='ols')
#Plot ACF:
plt.subplot(121)
plt.plot(lag_acf)
plt.axhline(y=0,linestyle='--',color='gray')
plt.axhline(y=-1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.axhline(y=1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.title('Autocorrelation Function')
#Plot PACF:
plt.subplot(122)
plt.plot(lag_pacf)
plt.axhline(y=0,linestyle='--',color='gray')
plt.axhline(y=-1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.axhline(y=1.96/np.sqrt(len(ts_log_diff)),linestyle='--',color='gray')
plt.title('Partial Autocorrelation Function')
plt.tight_layout()
plt.show()
In [46]:
model = ARIMA(ts_log, order=(1, 1, 0))
results_AR = model.fit(disp=-1)
plt.plot(ts_log_diff)
plt.plot(results_AR.fittedvalues, color='red')
plt.title('RSS: %.4f'% sum((results_AR.fittedvalues-ts_log_diff)**2))
plt.show()
In [54]:
model = ARIMA(ts_log, order=(1, 1, 0))
results_MA = model.fit(disp=-1)
plt.plot(ts_log_diff)
plt.plot(results_MA.fittedvalues, color='red')
plt.title('RSS: %.4f'% sum((results_MA.fittedvalues-ts_log_diff)**2))
plt.show()