OLS Analysis Using Full PSU dataset

In [1]:

    

#Import required packages
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt


    

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['End_Date'] = df_date['Meeting_Dates'].str.extract('(?<=-)(.*)(?= )', expand=True)
    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
    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


    

In [3]:

    

pd.set_option('display.max_rows', None)  


df = pd.read_csv('data/PSU_master_classroom_91-17.csv', dtype={'Schedule': object, 'Schedule Desc': object})
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']]

#terms = [199104, 199204, 199304, 199404, 199504, 199604, 199704, 199804, 199904, 200004, 200104, 200204, 200304, 200404, 200504, 200604, 200704, 200804, 200904, 201004, 201104, 201204, 201304, 201404, 201504, 201604]
terms = [200604, 200704, 200804, 200904, 201004, 201104, 201204, 201304, 201404, 201504, 201604]
df = df.loc[df['Term'].isin(terms)]
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['Building'] = df['ROOM'].str.extract('([^\s]+)', expand=True)

df_cl = format_xlist(df)
df_cl['%_Empty'] = df_cl['Cap_Diff'].astype(float) / df_cl['Room_Capacity'].astype(float)

# Normalize the results
df_cl['%_Empty'] = df_cl['Actual_Enrl'].astype(np.float32)/df_cl['Room_Capacity'].astype(np.float32)
df_cl = df_cl.replace([np.inf, -np.inf], np.nan).dropna()


    

    




/home/kevin/anaconda3/envs/data-science/lib/python3.5/site-packages/ipykernel/__main__.py:22: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy
/home/kevin/anaconda3/envs/data-science/lib/python3.5/site-packages/ipykernel/__main__.py:25: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy

In [4]:

    

from sklearn.preprocessing import LabelEncoder

df_cl = df_cl.sample(n = 15000)

# Save as a 1D array. Otherwise will throw errors.
y = np.asarray(df_cl['%_Empty'], dtype="|S6")
df_cl = df_cl[['Dept', 'Class', 'Days', 'Start_Time', 'ROOM', 'Term', 'Room_Capacity', 'Building']]

cat_columns = ['Dept', 'Class', 'Days', 'Start_Time', 'ROOM', 'Building']

for column in cat_columns:
    room_mapping = {label: idx for idx, label in enumerate(np.unique(df_cl['{0}'.format(column)]))}
    df_cl['{0}'.format(column)] = df_cl['{0}'.format(column)].map(room_mapping)

from distutils.version import LooseVersion as Version
from sklearn import __version__ as sklearn_version
    
if Version(sklearn_version) < '0.18':
    from sklearn.cross_validation import train_test_split
else:
    from sklearn.model_selection import train_test_split

X = df_cl.iloc[:, 1:].values

X_train, X_test, y_train, y_test = \
    train_test_split(X, y, test_size=0.3, random_state=0)


    

In [5]:

    

from sklearn.ensemble import RandomForestClassifier

feat_labels = df_cl.columns[1:]

forest = RandomForestClassifier(n_estimators=100,
                                random_state=0,
                                n_jobs=-1) # -1 sets n_jobs=n_CPU cores

forest.fit(X_train, y_train)
importances = forest.feature_importances_

indices = np.argsort(importances)[::-1]

for f in range(X_train.shape[1]):
    print("%2d) %-*s %f" % (f + 1, 30, 
                            feat_labels[indices[f]], 
                            importances[indices[f]]))

plt.title('Feature Importances')
plt.bar(range(X_train.shape[1]), 
        importances[indices],
        color='lightblue', 
        align='center')

plt.xticks(range(X_train.shape[1]), 
           feat_labels[indices], rotation=90)
plt.xlim([-1, X_train.shape[1]])
plt.tight_layout()
plt.show()


    

    




 1) Class                          0.255443
 2) Term                           0.191124
 3) Start_Time                     0.172338
 4) ROOM                           0.132014
 5) Days                           0.120786
 6) Room_Capacity                  0.087958
 7) Building                       0.040338






    

In [6]:

    

# Random Forest Classification

from sklearn import model_selection

kfold = model_selection.KFold(n_splits=10, random_state=0)
model = RandomForestClassifier(n_estimators=20, random_state=0, n_jobs=-1)
results = model_selection.cross_val_score(model, X, y, cv=kfold)
print(results.mean())


    

    




0.106